Ipynb credential check — check your notebook free

Audits saved .ipynb cell outputs for live credentials, customer personal data and internal hostnames before you share, commit or attach the notebook. Runs entirely in your browser — nothing is uploaded.

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3 churn model — customer extract\nRun top to bottom, then send to the risk team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "import os, json\n",
    "print(json.dumps({k: v for k, v in os.environ.items() if \"KEY\" in k or \"TOKEN\" in k}, indent=0))\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'STRIPE_API_KEY': 'sk-live-9f2Hq4RtVbN0pLxZ7mCe31',\n",
      "'AWS_ACCESS_KEY_ID': 'AKIA7Y3QWXZM4RLPB2VD',\n",
      "'GH_BUILD_TOKEN': 'ghp_8sKd02QmNzVxLa91TrYbUw44JhEoPc7Mi3Zq'}\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "source": [
    "password = \"Rh7!kappaNord22\"\n",
    "conn = f\"postgresql://analytics:{password}@db-risk-02.corp:5432/customers\"\n",
    "print(\"connecting ->\", conn)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "connecting -> postgresql://analytics:Rh7!kappaNord22@db-risk-02.corp:5432/customers\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "source": [
    "df = pd.read_sql(\"select * from customers limit 3\", conn)\n",
    "df.head()\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": null,
     "metadata": {},
     "data": {
      "text/plain": [
       "        name            contact_email                     iban          card_number\n",
       "0  J. Larsson    jonas.larsson@nordbank.de   DE44500105175407324931   4539 8812 7745 1002\n",
       "1   M. Keller     m.keller@heilbronn-gmbh.de  DE02120300000000202051   5425 2334 3010 9903\n",
       "2    A. Dupont       a.dupont@cabinet-ld.fr   FR7630006000011234567890189   4916 7712 3344 5566\n"
      ]
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "source": [
    "r = requests.post(AUTH, data=creds)\n",
    "print(r.json()[\"access_token\"])\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJzdmMtcmlzay0wMiIsImV4cCI6MTc5MjM0NTYwMH0.Qm4bTpLx91VsKdEa02NcRwZgYhUoJf7M\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "source": [
    "score = model.predict(features)\n"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/jlarsson/risk/model_v7.pkl'",
     "traceback": [
      "Traceback (most recent call last):",
      "  File \"/home/jlarsson/risk/score.py\", line 41, in <module>",
      "  Retrying against fallback host mlflow-01.internal (10.42.7.18)",
      "FileNotFoundError: [Errno 2] No such file or directory: '/home/jlarsson/risk/model_v7.pkl'"
     ]
    }
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.12.4"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "a1b2": {
      "model_name": "FloatProgressModel",
      "state": {
       "description": "scoring customers"
      }
     }
    },
    "version_major": 2
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

as of

Free here: Audit the notebook you have open and name every leaking output cell, line by line.. In the editor: Sweep every .ipynb in the workspace and export the dated audit record you keep on file. — that part asks for a licence key.

Same engine as the VS Code extension, byte for byte.