Driving NB Shift from your own code

curl -s "https://api.skillsafe.ai/v1/app-api/me" \
  -H "Authorization: Bearer $NB_TOKEN"

import json, urllib.request

TOKEN = "YOUR_TOKEN"   # from https://nb-shift.skillsafe.ai/tokens.html
BASE = "https://api.skillsafe.ai/v1/app-api"

def call(path, payload=None):
    data = json.dumps(payload).encode() if payload is not None else None
    req = urllib.request.Request(BASE + path, data=data, method="GET")
    req.add_header("Authorization", "Bearer " + TOKEN)
    if data:
        req.add_header("Content-Type", "application/json")
    with urllib.request.urlopen(req) as r:
        env = json.load(r)
    if not env.get("ok"):
        raise RuntimeError(env["error"]["code"] + ": " + env["error"]["message"])
    return env["data"]

print(call("/me"))

const TOKEN = "YOUR_TOKEN";   // from https://nb-shift.skillsafe.ai/tokens.html
const BASE = "https://api.skillsafe.ai/v1/app-api";

async function call(path, payload) {
  const res = await fetch(BASE + path, {
    method: payload ? "POST" : "GET",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      ...(payload ? { "Content-Type": "application/json" } : {})
    },
    body: payload ? JSON.stringify(payload) : undefined
  });
  const env = await res.json();
  if (!env.ok) throw new Error(`${env.error.code}: ${env.error.message}`);
  return env.data;
}

console.log(await call("/me"));

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	var rdr io.Reader
	_ = bytes.MinRead
	req, _ := http.NewRequest("GET", base+"/me", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/me"))
            .header("Authorization", "Bearer " + TOKEN)
            .GET()
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

uri = URI(BASE.to_s + '/me')
req = Net::HTTP::Get.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$ch = curl_init($base . '/me');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'GET',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
    ],
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.GetAsync(Base + "/me");
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));

curl -s -X POST "https://api.skillsafe.ai/v1/app-api/estimate" \
  -H "Authorization: Bearer $NB_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}'

import json, urllib.request

TOKEN = "YOUR_TOKEN"   # from https://nb-shift.skillsafe.ai/tokens.html
BASE = "https://api.skillsafe.ai/v1/app-api"

def call(path, payload=None):
    data = json.dumps(payload).encode() if payload is not None else None
    req = urllib.request.Request(BASE + path, data=data, method="POST")
    req.add_header("Authorization", "Bearer " + TOKEN)
    if data:
        req.add_header("Content-Type", "application/json")
    with urllib.request.urlopen(req) as r:
        env = json.load(r)
    if not env.get("ok"):
        raise RuntimeError(env["error"]["code"] + ": " + env["error"]["message"])
    return env["data"]

payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}

print(call("/estimate", payload))

const TOKEN = "YOUR_TOKEN";   // from https://nb-shift.skillsafe.ai/tokens.html
const BASE = "https://api.skillsafe.ai/v1/app-api";

async function call(path, payload) {
  const res = await fetch(BASE + path, {
    method: payload ? "POST" : "GET",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      ...(payload ? { "Content-Type": "application/json" } : {})
    },
    body: payload ? JSON.stringify(payload) : undefined
  });
  const env = await res.json();
  if (!env.ok) throw new Error(`${env.error.code}: ${env.error.message}`);
  return env.data;
}

const payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
};

console.log(await call("/estimate", payload));

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	payload := `{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}`

	var rdr io.Reader
	rdr = bytes.NewBufferString(payload)
	req, _ := http.NewRequest("POST", base+"/estimate", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        String payload = """
            {
              "task": "audit",
              "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
              "depth": "standard",
              "audience": "author",
              "python": "3.12",
              "context": "",
              "prescan_facts": {
                "flags": [
                  {
                    "id": "nb-hidden-state",
                    "severity": "critical",
                    "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
                  },
                  {
                    "id": "nb-out-of-order",
                    "severity": "high",
                    "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
                  },
                  {
                    "id": "nb-no-seed",
                    "severity": "high",
                    "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
                  },
                  {
                    "id": "nb-absolute-path",
                    "severity": "high",
                    "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
                  }
                ],
                "resources": [
                  {
                    "id": "nb-format",
                    "detail": "document read as ipynb (4.5)"
                  },
                  {
                    "id": "nb-cells",
                    "detail": "4 code cells, 1 markdown cells, 18 lines"
                  }
                ],
                "stats": {
                  "cellCount": 5,
                  "codeCells": 4,
                  "importCount": 2,
                  "topLevelNames": 4
                }
              }
            }""";

        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/estimate"))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

payload = <<~JSON
  {
    "task": "audit",
    "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
    "depth": "standard",
    "audience": "author",
    "python": "3.12",
    "context": "",
    "prescan_facts": {
      "flags": [
        {
          "id": "nb-hidden-state",
          "severity": "critical",
          "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
        },
        {
          "id": "nb-out-of-order",
          "severity": "high",
          "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
        },
        {
          "id": "nb-no-seed",
          "severity": "high",
          "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
        },
        {
          "id": "nb-absolute-path",
          "severity": "high",
          "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
        }
      ],
      "resources": [
        {
          "id": "nb-format",
          "detail": "document read as ipynb (4.5)"
        },
        {
          "id": "nb-cells",
          "detail": "4 code cells, 1 markdown cells, 18 lines"
        }
      ],
      "stats": {
        "cellCount": 5,
        "codeCells": 4,
        "importCount": 2,
        "topLevelNames": 4
      }
    }
  }
JSON

uri = URI(BASE.to_s + '/estimate')
req = Net::HTTP::Post.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
req['Content-Type'] = 'application/json'
req.body = payload
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$payload = <<<'JSON'
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
JSON;

$ch = curl_init($base . '/estimate');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'POST',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS     => $payload,
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

var payload = """
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
""";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.PostAsync(Base + "/estimate",
    new StringContent(payload, Encoding.UTF8, "application/json"));
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));

curl -s -X POST "https://api.skillsafe.ai/v1/app-api/run" \
  -H "Authorization: Bearer $NB_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}'

import json, urllib.request

TOKEN = "YOUR_TOKEN"   # from https://nb-shift.skillsafe.ai/tokens.html
BASE = "https://api.skillsafe.ai/v1/app-api"

def call(path, payload=None):
    data = json.dumps(payload).encode() if payload is not None else None
    req = urllib.request.Request(BASE + path, data=data, method="POST")
    req.add_header("Authorization", "Bearer " + TOKEN)
    if data:
        req.add_header("Content-Type", "application/json")
    with urllib.request.urlopen(req) as r:
        env = json.load(r)
    if not env.get("ok"):
        raise RuntimeError(env["error"]["code"] + ": " + env["error"]["message"])
    return env["data"]

payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}

print(call("/run", payload))

const TOKEN = "YOUR_TOKEN";   // from https://nb-shift.skillsafe.ai/tokens.html
const BASE = "https://api.skillsafe.ai/v1/app-api";

async function call(path, payload) {
  const res = await fetch(BASE + path, {
    method: payload ? "POST" : "GET",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      ...(payload ? { "Content-Type": "application/json" } : {})
    },
    body: payload ? JSON.stringify(payload) : undefined
  });
  const env = await res.json();
  if (!env.ok) throw new Error(`${env.error.code}: ${env.error.message}`);
  return env.data;
}

const payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
};

console.log(await call("/run", payload));

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	payload := `{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}`

	var rdr io.Reader
	rdr = bytes.NewBufferString(payload)
	req, _ := http.NewRequest("POST", base+"/run", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        String payload = """
            {
              "task": "audit",
              "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
              "depth": "standard",
              "audience": "author",
              "python": "3.12",
              "context": "",
              "prescan_facts": {
                "flags": [
                  {
                    "id": "nb-hidden-state",
                    "severity": "critical",
                    "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
                  },
                  {
                    "id": "nb-out-of-order",
                    "severity": "high",
                    "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
                  },
                  {
                    "id": "nb-no-seed",
                    "severity": "high",
                    "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
                  },
                  {
                    "id": "nb-absolute-path",
                    "severity": "high",
                    "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
                  }
                ],
                "resources": [
                  {
                    "id": "nb-format",
                    "detail": "document read as ipynb (4.5)"
                  },
                  {
                    "id": "nb-cells",
                    "detail": "4 code cells, 1 markdown cells, 18 lines"
                  }
                ],
                "stats": {
                  "cellCount": 5,
                  "codeCells": 4,
                  "importCount": 2,
                  "topLevelNames": 4
                }
              }
            }""";

        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/run"))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

payload = <<~JSON
  {
    "task": "audit",
    "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
    "depth": "standard",
    "audience": "author",
    "python": "3.12",
    "context": "",
    "prescan_facts": {
      "flags": [
        {
          "id": "nb-hidden-state",
          "severity": "critical",
          "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
        },
        {
          "id": "nb-out-of-order",
          "severity": "high",
          "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
        },
        {
          "id": "nb-no-seed",
          "severity": "high",
          "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
        },
        {
          "id": "nb-absolute-path",
          "severity": "high",
          "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
        }
      ],
      "resources": [
        {
          "id": "nb-format",
          "detail": "document read as ipynb (4.5)"
        },
        {
          "id": "nb-cells",
          "detail": "4 code cells, 1 markdown cells, 18 lines"
        }
      ],
      "stats": {
        "cellCount": 5,
        "codeCells": 4,
        "importCount": 2,
        "topLevelNames": 4
      }
    }
  }
JSON

uri = URI(BASE.to_s + '/run')
req = Net::HTTP::Post.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
req['Content-Type'] = 'application/json'
req.body = payload
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$payload = <<<'JSON'
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
JSON;

$ch = curl_init($base . '/run');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'POST',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS     => $payload,
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

var payload = """
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
""";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.PostAsync(Base + "/run",
    new StringContent(payload, Encoding.UTF8, "application/json"));
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));

curl -N -s -X POST "https://api.skillsafe.ai/v1/app-api/run-stream" \
  -H "Authorization: Bearer $NB_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -H "Idempotency-Key: nb-shift:audit:$(date +%s)" \
  -d '{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}'

import json, urllib.request

TOKEN = "YOUR_TOKEN"
payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}

req = urllib.request.Request("https://api.skillsafe.ai/v1/app-api/run-stream", data=json.dumps(payload).encode(), method="POST")
req.add_header("Authorization", "Bearer " + TOKEN)
req.add_header("Content-Type", "application/json")
req.add_header("Accept", "text/event-stream")
req.add_header("Idempotency-Key", "nb-shift:audit:run-1")

chunks = []
with urllib.request.urlopen(req) as r:
    for line in r:
        line = line.decode().strip()
        if not line.startswith("data:"):
            continue
        evt = json.loads(line[5:].strip())
        if evt.get("type") == "delta":
            chunks.append(evt["text"])

reply = json.loads("".join(chunks))
print(reply["verdict"], len(reply["findings"]), "findings")

const TOKEN = "YOUR_TOKEN";
const payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
};

const res = await fetch("https://api.skillsafe.ai/v1/app-api/run-stream", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${TOKEN}`,
    "Content-Type": "application/json",
    "Accept": "text/event-stream",
    "Idempotency-Key": "nb-shift:audit:run-1"
  },
  body: JSON.stringify(payload)
});

const reader = res.body.getReader();
const dec = new TextDecoder();
let buf = "", out = "";
for (;;) {
  const { done, value } = await reader.read();
  if (done) break;
  buf += dec.decode(value, { stream: true });
  const lines = buf.split("\n");
  buf = lines.pop();
  for (const line of lines) {
    if (!line.startsWith("data:")) continue;
    const evt = JSON.parse(line.slice(5).trim());
    if (evt.type === "delta") out += evt.text;
  }
}
console.log(JSON.parse(out).verdict);

// Same request as /run, with Accept: text/event-stream and an
// Idempotency-Key header. Read the response body line by line and
// concatenate the `text` of every `delta` event; the concatenation is
// the JSON object documented above.
package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	payload := `{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}`

	var rdr io.Reader
	rdr = bytes.NewBufferString(payload)
	req, _ := http.NewRequest("POST", base+"/run-stream", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

// Same request as /run, with Accept: text/event-stream and an
// Idempotency-Key header. Read the response body line by line and
// concatenate the `text` of every `delta` event; the concatenation is
// the JSON object documented above.
import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        String payload = """
            {
              "task": "audit",
              "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
              "depth": "standard",
              "audience": "author",
              "python": "3.12",
              "context": "",
              "prescan_facts": {
                "flags": [
                  {
                    "id": "nb-hidden-state",
                    "severity": "critical",
                    "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
                  },
                  {
                    "id": "nb-out-of-order",
                    "severity": "high",
                    "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
                  },
                  {
                    "id": "nb-no-seed",
                    "severity": "high",
                    "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
                  },
                  {
                    "id": "nb-absolute-path",
                    "severity": "high",
                    "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
                  }
                ],
                "resources": [
                  {
                    "id": "nb-format",
                    "detail": "document read as ipynb (4.5)"
                  },
                  {
                    "id": "nb-cells",
                    "detail": "4 code cells, 1 markdown cells, 18 lines"
                  }
                ],
                "stats": {
                  "cellCount": 5,
                  "codeCells": 4,
                  "importCount": 2,
                  "topLevelNames": 4
                }
              }
            }""";

        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/run-stream"))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

# Same request as /run, with Accept: text/event-stream and an
# Idempotency-Key header. Read the response body line by line and
# concatenate the `text` of every `delta` event; the concatenation is
# the JSON object documented above.
require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

payload = <<~JSON
  {
    "task": "audit",
    "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
    "depth": "standard",
    "audience": "author",
    "python": "3.12",
    "context": "",
    "prescan_facts": {
      "flags": [
        {
          "id": "nb-hidden-state",
          "severity": "critical",
          "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
        },
        {
          "id": "nb-out-of-order",
          "severity": "high",
          "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
        },
        {
          "id": "nb-no-seed",
          "severity": "high",
          "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
        },
        {
          "id": "nb-absolute-path",
          "severity": "high",
          "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
        }
      ],
      "resources": [
        {
          "id": "nb-format",
          "detail": "document read as ipynb (4.5)"
        },
        {
          "id": "nb-cells",
          "detail": "4 code cells, 1 markdown cells, 18 lines"
        }
      ],
      "stats": {
        "cellCount": 5,
        "codeCells": 4,
        "importCount": 2,
        "topLevelNames": 4
      }
    }
  }
JSON

uri = URI(BASE.to_s + '/run-stream')
req = Net::HTTP::Post.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
req['Content-Type'] = 'application/json'
req.body = payload
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

# Same request as /run, with Accept: text/event-stream and an
# Idempotency-Key header. Read the response body line by line and
# concatenate the `text` of every `delta` event; the concatenation is
# the JSON object documented above.
<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$payload = <<<'JSON'
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
JSON;

$ch = curl_init($base . '/run-stream');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'POST',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS     => $payload,
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

// Same request as /run, with Accept: text/event-stream and an
// Idempotency-Key header. Read the response body line by line and
// concatenate the `text` of every `delta` event; the concatenation is
// the JSON object documented above.
using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

var payload = """
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
""";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.PostAsync(Base + "/run-stream",
    new StringContent(payload, Encoding.UTF8, "application/json"));
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));

curl -s -X POST "https://api.skillsafe.ai/v1/app-api/run" \
  -H "Authorization: Bearer $NB_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}'

import json, urllib.request

TOKEN = "YOUR_TOKEN"   # from https://nb-shift.skillsafe.ai/tokens.html
BASE = "https://api.skillsafe.ai/v1/app-api"

def call(path, payload=None):
    data = json.dumps(payload).encode() if payload is not None else None
    req = urllib.request.Request(BASE + path, data=data, method="POST")
    req.add_header("Authorization", "Bearer " + TOKEN)
    if data:
        req.add_header("Content-Type", "application/json")
    with urllib.request.urlopen(req) as r:
        env = json.load(r)
    if not env.get("ok"):
        raise RuntimeError(env["error"]["code"] + ": " + env["error"]["message"])
    return env["data"]

payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}

print(call("/run", payload))

const TOKEN = "YOUR_TOKEN";   // from https://nb-shift.skillsafe.ai/tokens.html
const BASE = "https://api.skillsafe.ai/v1/app-api";

async function call(path, payload) {
  const res = await fetch(BASE + path, {
    method: payload ? "POST" : "GET",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      ...(payload ? { "Content-Type": "application/json" } : {})
    },
    body: payload ? JSON.stringify(payload) : undefined
  });
  const env = await res.json();
  if (!env.ok) throw new Error(`${env.error.code}: ${env.error.message}`);
  return env.data;
}

const payload = {
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
};

console.log(await call("/run", payload));

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	payload := `{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}`

	var rdr io.Reader
	rdr = bytes.NewBufferString(payload)
	req, _ := http.NewRequest("POST", base+"/run", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        String payload = """
            {
              "task": "audit",
              "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
              "depth": "standard",
              "audience": "author",
              "python": "3.12",
              "context": "",
              "prescan_facts": {
                "flags": [
                  {
                    "id": "nb-hidden-state",
                    "severity": "critical",
                    "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
                  },
                  {
                    "id": "nb-out-of-order",
                    "severity": "high",
                    "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
                  },
                  {
                    "id": "nb-no-seed",
                    "severity": "high",
                    "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
                  },
                  {
                    "id": "nb-absolute-path",
                    "severity": "high",
                    "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
                  }
                ],
                "resources": [
                  {
                    "id": "nb-format",
                    "detail": "document read as ipynb (4.5)"
                  },
                  {
                    "id": "nb-cells",
                    "detail": "4 code cells, 1 markdown cells, 18 lines"
                  }
                ],
                "stats": {
                  "cellCount": 5,
                  "codeCells": 4,
                  "importCount": 2,
                  "topLevelNames": 4
                }
              }
            }""";

        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/run"))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

payload = <<~JSON
  {
    "task": "audit",
    "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
    "depth": "standard",
    "audience": "author",
    "python": "3.12",
    "context": "",
    "prescan_facts": {
      "flags": [
        {
          "id": "nb-hidden-state",
          "severity": "critical",
          "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
        },
        {
          "id": "nb-out-of-order",
          "severity": "high",
          "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
        },
        {
          "id": "nb-no-seed",
          "severity": "high",
          "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
        },
        {
          "id": "nb-absolute-path",
          "severity": "high",
          "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
        }
      ],
      "resources": [
        {
          "id": "nb-format",
          "detail": "document read as ipynb (4.5)"
        },
        {
          "id": "nb-cells",
          "detail": "4 code cells, 1 markdown cells, 18 lines"
        }
      ],
      "stats": {
        "cellCount": 5,
        "codeCells": 4,
        "importCount": 2,
        "topLevelNames": 4
      }
    }
  }
JSON

uri = URI(BASE.to_s + '/run')
req = Net::HTTP::Post.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
req['Content-Type'] = 'application/json'
req.body = payload
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$payload = <<<'JSON'
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
JSON;

$ch = curl_init($base . '/run');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'POST',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS     => $payload,
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

var payload = """
{
  "task": "audit",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
""";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.PostAsync(Base + "/run",
    new StringContent(payload, Encoding.UTF8, "application/json"));
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));

curl -s -X POST "https://api.skillsafe.ai/v1/app-api/run" \
  -H "Authorization: Bearer $NB_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "port",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}'

import json, urllib.request

TOKEN = "YOUR_TOKEN"   # from https://nb-shift.skillsafe.ai/tokens.html
BASE = "https://api.skillsafe.ai/v1/app-api"

def call(path, payload=None):
    data = json.dumps(payload).encode() if payload is not None else None
    req = urllib.request.Request(BASE + path, data=data, method="POST")
    req.add_header("Authorization", "Bearer " + TOKEN)
    if data:
        req.add_header("Content-Type", "application/json")
    with urllib.request.urlopen(req) as r:
        env = json.load(r)
    if not env.get("ok"):
        raise RuntimeError(env["error"]["code"] + ": " + env["error"]["message"])
    return env["data"]

payload = {
  "task": "port",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}

print(call("/run", payload))

const TOKEN = "YOUR_TOKEN";   // from https://nb-shift.skillsafe.ai/tokens.html
const BASE = "https://api.skillsafe.ai/v1/app-api";

async function call(path, payload) {
  const res = await fetch(BASE + path, {
    method: payload ? "POST" : "GET",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      ...(payload ? { "Content-Type": "application/json" } : {})
    },
    body: payload ? JSON.stringify(payload) : undefined
  });
  const env = await res.json();
  if (!env.ok) throw new Error(`${env.error.code}: ${env.error.message}`);
  return env.data;
}

const payload = {
  "task": "port",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
};

console.log(await call("/run", payload));

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	payload := `{
  "task": "port",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}`

	var rdr io.Reader
	rdr = bytes.NewBufferString(payload)
	req, _ := http.NewRequest("POST", base+"/run", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        String payload = """
            {
              "task": "port",
              "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
              "depth": "standard",
              "audience": "author",
              "python": "3.12",
              "context": "",
              "prescan_facts": {
                "flags": [
                  {
                    "id": "nb-hidden-state",
                    "severity": "critical",
                    "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
                  },
                  {
                    "id": "nb-out-of-order",
                    "severity": "high",
                    "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
                  },
                  {
                    "id": "nb-no-seed",
                    "severity": "high",
                    "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
                  },
                  {
                    "id": "nb-absolute-path",
                    "severity": "high",
                    "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
                  }
                ],
                "resources": [
                  {
                    "id": "nb-format",
                    "detail": "document read as ipynb (4.5)"
                  },
                  {
                    "id": "nb-cells",
                    "detail": "4 code cells, 1 markdown cells, 18 lines"
                  }
                ],
                "stats": {
                  "cellCount": 5,
                  "codeCells": 4,
                  "importCount": 2,
                  "topLevelNames": 4
                }
              }
            }""";

        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/run"))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

payload = <<~JSON
  {
    "task": "port",
    "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
    "depth": "standard",
    "audience": "author",
    "python": "3.12",
    "context": "",
    "prescan_facts": {
      "flags": [
        {
          "id": "nb-hidden-state",
          "severity": "critical",
          "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
        },
        {
          "id": "nb-out-of-order",
          "severity": "high",
          "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
        },
        {
          "id": "nb-no-seed",
          "severity": "high",
          "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
        },
        {
          "id": "nb-absolute-path",
          "severity": "high",
          "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
        }
      ],
      "resources": [
        {
          "id": "nb-format",
          "detail": "document read as ipynb (4.5)"
        },
        {
          "id": "nb-cells",
          "detail": "4 code cells, 1 markdown cells, 18 lines"
        }
      ],
      "stats": {
        "cellCount": 5,
        "codeCells": 4,
        "importCount": 2,
        "topLevelNames": 4
      }
    }
  }
JSON

uri = URI(BASE.to_s + '/run')
req = Net::HTTP::Post.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
req['Content-Type'] = 'application/json'
req.body = payload
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$payload = <<<'JSON'
{
  "task": "port",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
JSON;

$ch = curl_init($base . '/run');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'POST',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS     => $payload,
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

var payload = """
{
  "task": "port",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
""";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.PostAsync(Base + "/run",
    new StringContent(payload, Encoding.UTF8, "application/json"));
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));

curl -s -X POST "https://api.skillsafe.ai/v1/app-api/run" \
  -H "Authorization: Bearer $NB_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "wasm",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}'

import json, urllib.request

TOKEN = "YOUR_TOKEN"   # from https://nb-shift.skillsafe.ai/tokens.html
BASE = "https://api.skillsafe.ai/v1/app-api"

def call(path, payload=None):
    data = json.dumps(payload).encode() if payload is not None else None
    req = urllib.request.Request(BASE + path, data=data, method="POST")
    req.add_header("Authorization", "Bearer " + TOKEN)
    if data:
        req.add_header("Content-Type", "application/json")
    with urllib.request.urlopen(req) as r:
        env = json.load(r)
    if not env.get("ok"):
        raise RuntimeError(env["error"]["code"] + ": " + env["error"]["message"])
    return env["data"]

payload = {
  "task": "wasm",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}

print(call("/run", payload))

const TOKEN = "YOUR_TOKEN";   // from https://nb-shift.skillsafe.ai/tokens.html
const BASE = "https://api.skillsafe.ai/v1/app-api";

async function call(path, payload) {
  const res = await fetch(BASE + path, {
    method: payload ? "POST" : "GET",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      ...(payload ? { "Content-Type": "application/json" } : {})
    },
    body: payload ? JSON.stringify(payload) : undefined
  });
  const env = await res.json();
  if (!env.ok) throw new Error(`${env.error.code}: ${env.error.message}`);
  return env.data;
}

const payload = {
  "task": "wasm",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
};

console.log(await call("/run", payload));

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	payload := `{
  "task": "wasm",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}`

	var rdr io.Reader
	rdr = bytes.NewBufferString(payload)
	req, _ := http.NewRequest("POST", base+"/run", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        String payload = """
            {
              "task": "wasm",
              "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
              "depth": "standard",
              "audience": "author",
              "python": "3.12",
              "context": "",
              "prescan_facts": {
                "flags": [
                  {
                    "id": "nb-hidden-state",
                    "severity": "critical",
                    "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
                  },
                  {
                    "id": "nb-out-of-order",
                    "severity": "high",
                    "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
                  },
                  {
                    "id": "nb-no-seed",
                    "severity": "high",
                    "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
                  },
                  {
                    "id": "nb-absolute-path",
                    "severity": "high",
                    "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
                  }
                ],
                "resources": [
                  {
                    "id": "nb-format",
                    "detail": "document read as ipynb (4.5)"
                  },
                  {
                    "id": "nb-cells",
                    "detail": "4 code cells, 1 markdown cells, 18 lines"
                  }
                ],
                "stats": {
                  "cellCount": 5,
                  "codeCells": 4,
                  "importCount": 2,
                  "topLevelNames": 4
                }
              }
            }""";

        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/run"))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

payload = <<~JSON
  {
    "task": "wasm",
    "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
    "depth": "standard",
    "audience": "author",
    "python": "3.12",
    "context": "",
    "prescan_facts": {
      "flags": [
        {
          "id": "nb-hidden-state",
          "severity": "critical",
          "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
        },
        {
          "id": "nb-out-of-order",
          "severity": "high",
          "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
        },
        {
          "id": "nb-no-seed",
          "severity": "high",
          "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
        },
        {
          "id": "nb-absolute-path",
          "severity": "high",
          "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
        }
      ],
      "resources": [
        {
          "id": "nb-format",
          "detail": "document read as ipynb (4.5)"
        },
        {
          "id": "nb-cells",
          "detail": "4 code cells, 1 markdown cells, 18 lines"
        }
      ],
      "stats": {
        "cellCount": 5,
        "codeCells": 4,
        "importCount": 2,
        "topLevelNames": 4
      }
    }
  }
JSON

uri = URI(BASE.to_s + '/run')
req = Net::HTTP::Post.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
req['Content-Type'] = 'application/json'
req.body = payload
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$payload = <<<'JSON'
{
  "task": "wasm",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
JSON;

$ch = curl_init($base . '/run');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'POST',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS     => $payload,
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

var payload = """
{
  "task": "wasm",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "author",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
""";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.PostAsync(Base + "/run",
    new StringContent(payload, Encoding.UTF8, "application/json"));
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));

curl -s -X POST "https://api.skillsafe.ai/v1/app-api/run" \
  -H "Authorization: Bearer $NB_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "batch",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "ops",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}'

import json, urllib.request

TOKEN = "YOUR_TOKEN"   # from https://nb-shift.skillsafe.ai/tokens.html
BASE = "https://api.skillsafe.ai/v1/app-api"

def call(path, payload=None):
    data = json.dumps(payload).encode() if payload is not None else None
    req = urllib.request.Request(BASE + path, data=data, method="POST")
    req.add_header("Authorization", "Bearer " + TOKEN)
    if data:
        req.add_header("Content-Type", "application/json")
    with urllib.request.urlopen(req) as r:
        env = json.load(r)
    if not env.get("ok"):
        raise RuntimeError(env["error"]["code"] + ": " + env["error"]["message"])
    return env["data"]

payload = {
  "task": "batch",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "ops",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}

print(call("/run", payload))

const TOKEN = "YOUR_TOKEN";   // from https://nb-shift.skillsafe.ai/tokens.html
const BASE = "https://api.skillsafe.ai/v1/app-api";

async function call(path, payload) {
  const res = await fetch(BASE + path, {
    method: payload ? "POST" : "GET",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      ...(payload ? { "Content-Type": "application/json" } : {})
    },
    body: payload ? JSON.stringify(payload) : undefined
  });
  const env = await res.json();
  if (!env.ok) throw new Error(`${env.error.code}: ${env.error.message}`);
  return env.data;
}

const payload = {
  "task": "batch",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "ops",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
};

console.log(await call("/run", payload));

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

const token = "YOUR_TOKEN" // from https://nb-shift.skillsafe.ai/tokens.html
const base = "https://api.skillsafe.ai/v1/app-api"

func main() {
	payload := `{
  "task": "batch",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "ops",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}`

	var rdr io.Reader
	rdr = bytes.NewBufferString(payload)
	req, _ := http.NewRequest("POST", base+"/run", rdr)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer res.Body.Close()

	var env struct {
		OK    bool            `json:"ok"`
		Data  json.RawMessage `json:"data"`
		Error struct {
			Code    string `json:"code"`
			Message string `json:"message"`
		} `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if !env.OK {
		panic(env.Error.Code + ": " + env.Error.Message)
	}
	fmt.Println(string(env.Data))
}

import java.net.URI;
import java.net.http.*;

public class NbShift {
    static final String TOKEN = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
    static final String BASE = "https://api.skillsafe.ai/v1/app-api";

    public static void main(String[] args) throws Exception {
        String payload = """
            {
              "task": "batch",
              "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
              "depth": "standard",
              "audience": "ops",
              "python": "3.12",
              "context": "",
              "prescan_facts": {
                "flags": [
                  {
                    "id": "nb-hidden-state",
                    "severity": "critical",
                    "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
                  },
                  {
                    "id": "nb-out-of-order",
                    "severity": "high",
                    "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
                  },
                  {
                    "id": "nb-no-seed",
                    "severity": "high",
                    "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
                  },
                  {
                    "id": "nb-absolute-path",
                    "severity": "high",
                    "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
                  }
                ],
                "resources": [
                  {
                    "id": "nb-format",
                    "detail": "document read as ipynb (4.5)"
                  },
                  {
                    "id": "nb-cells",
                    "detail": "4 code cells, 1 markdown cells, 18 lines"
                  }
                ],
                "stats": {
                  "cellCount": 5,
                  "codeCells": 4,
                  "importCount": 2,
                  "topLevelNames": 4
                }
              }
            }""";

        HttpRequest req = HttpRequest.newBuilder()
            .uri(URI.create(BASE + "/run"))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> res = HttpClient.newHttpClient()
            .send(req, HttpResponse.BodyHandlers.ofString());
        System.out.println(res.body());
    }
}

require 'json'
require 'net/http'

TOKEN = 'YOUR_TOKEN' # from https://nb-shift.skillsafe.ai/tokens.html
BASE = URI('https://api.skillsafe.ai/v1/app-api')

payload = <<~JSON
  {
    "task": "batch",
    "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
    "depth": "standard",
    "audience": "ops",
    "python": "3.12",
    "context": "",
    "prescan_facts": {
      "flags": [
        {
          "id": "nb-hidden-state",
          "severity": "critical",
          "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
        },
        {
          "id": "nb-out-of-order",
          "severity": "high",
          "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
        },
        {
          "id": "nb-no-seed",
          "severity": "high",
          "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
        },
        {
          "id": "nb-absolute-path",
          "severity": "high",
          "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
        }
      ],
      "resources": [
        {
          "id": "nb-format",
          "detail": "document read as ipynb (4.5)"
        },
        {
          "id": "nb-cells",
          "detail": "4 code cells, 1 markdown cells, 18 lines"
        }
      ],
      "stats": {
        "cellCount": 5,
        "codeCells": 4,
        "importCount": 2,
        "topLevelNames": 4
      }
    }
  }
JSON

uri = URI(BASE.to_s + '/run')
req = Net::HTTP::Post.new(uri)
req['Authorization'] = "Bearer #{TOKEN}"
req['Content-Type'] = 'application/json'
req.body = payload
res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(req) }
env = JSON.parse(res.body)
raise "#{env['error']['code']}: #{env['error']['message']}" unless env['ok']
puts JSON.pretty_generate(env['data'])

<?php
$token = 'YOUR_TOKEN'; // from https://nb-shift.skillsafe.ai/tokens.html
$base  = 'https://api.skillsafe.ai/v1/app-api';

$payload = <<<'JSON'
{
  "task": "batch",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "ops",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
JSON;

$ch = curl_init($base . '/run');
curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_CUSTOMREQUEST  => 'POST',
    CURLOPT_HTTPHEADER     => [
        'Authorization: Bearer ' . $token,
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS     => $payload,
]);
$env = json_decode(curl_exec($ch), true);
curl_close($ch);

if (empty($env['ok'])) {
    throw new RuntimeException($env['error']['code'] . ': ' . $env['error']['message']);
}
print_r($env['data']);

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

const string Token = "YOUR_TOKEN"; // https://nb-shift.skillsafe.ai/tokens.html
const string Base  = "https://api.skillsafe.ai/v1/app-api";

var payload = """
{
  "task": "batch",
  "notebook": "# document: ipynb (nbformat 4.5)\n# kernel: Python 3 (ipykernel)\n# language: python 3.9.6\n# cells: 5 total, 4 code, 1 markdown, 18 lines\n# stored output: 3 KB (not included below)\n\n# --- cell 0 [markdown]\n# Churn experiments\n\n# --- cell 1 [code] exec=[1] defines=pd,np\nimport pandas as pd\nimport numpy as np\n\n# --- cell 2 [code] exec=[2] defines=df needs=pd\ndf = pd.read_csv(\"/Users/priya/Downloads/churn.csv\")\n\n# --- cell 3 [code] exec=[7] needs=scores\nprint(scores.mean())\n\n# --- cell 4 [code] exec=[4] defines=scores needs=np\nscores = np.random.rand(len(df))\n",
  "depth": "standard",
  "audience": "ops",
  "python": "3.12",
  "context": "",
  "prescan_facts": {
    "flags": [
      {
        "id": "nb-hidden-state",
        "severity": "critical",
        "detail": "1 cell references a name defined only in a LATER cell (scores: used in cell 3, defined in cell 4), so the notebook cannot run top to bottom as written"
      },
      {
        "id": "nb-out-of-order",
        "severity": "high",
        "detail": "1 cell carries an execution count lower than the cell above (cell 4 ran as [4] after cell 3 ran as [7]), so this notebook was not executed top to bottom"
      },
      {
        "id": "nb-no-seed",
        "severity": "high",
        "detail": "randomness is used in cell 4 with no seed set anywhere, so two runs cannot be compared"
      },
      {
        "id": "nb-absolute-path",
        "severity": "high",
        "detail": "1 absolute path (/Users/priya/Downloads/churn.csv) ties this notebook to one machine"
      }
    ],
    "resources": [
      {
        "id": "nb-format",
        "detail": "document read as ipynb (4.5)"
      },
      {
        "id": "nb-cells",
        "detail": "4 code cells, 1 markdown cells, 18 lines"
      }
    ],
    "stats": {
      "cellCount": 5,
      "codeCells": 4,
      "importCount": 2,
      "topLevelNames": 4
    }
  }
}
""";

using var http = new HttpClient();
http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", Token);

var res = await http.PostAsync(Base + "/run",
    new StringContent(payload, Encoding.UTF8, "application/json"));
var body = await res.Content.ReadAsStringAsync();
using var doc = JsonDocument.Parse(body);
if (!doc.RootElement.GetProperty("ok").GetBoolean())
{
    var err = doc.RootElement.GetProperty("error");
    throw new Exception($"{err.GetProperty("code")}: {err.GetProperty("message")}");
}
Console.WriteLine(doc.RootElement.GetProperty("data"));