AI tools that make you a better data scientist, not a redundant one.

Catch blind spots, stress-test your analysis, and level up your craft - inside your Jupyter notebook.

$ pip install bridgekit
View on GitHub PyPI
What's included
Four focused tools

No new interface to learn. Just better work - one function call from your existing notebook.

🔍
Analysis Reviewer
evaluate(text)
Write your findings the way you normally would. Get the feedback a senior data scientist would give you before you walk into the meeting.
🗂️
Analysis Search
ask(question, source=)
Ask questions across your past reports and notebooks. Semantic search across .txt, .md, .pdf, .docx, .pptx, and .ipynb files.
🗺️
Analysis Planner
plan(question)
Describe your analytical problem and get a structured plan - recommended method, assumptions, pitfalls, and alternatives - before you start.
⚡
Red Team
redteam(text)
Simulate a skeptical stakeholder challenging your work. Prepare for the questions you hope no one asks - before the meeting.
Example
See it in action

Paste your analysis. Get structured feedback in seconds.

from bridgekit import evaluate text = """ Users who engaged with the reporting feature within their first week were 3x more likely to upgrade within 30 days. I recommend we prioritize onboarding users to reporting as a growth lever. """ print(evaluate(text))
BRIDGEKIT ANALYSIS REVIEW
─────────────────────────────
1. CLARITY
✅ STRONG - Clean and jargon-free. Any stakeholder could read this and immediately understand the claim.
2. STATISTICAL RIGOR
⚠️ NEEDS WORK - "3x more likely" is compelling but critical context is missing. No confidence interval or sample sizes.
3. METHODOLOGY
❌ MISSING - This reads as a pure correlation finding, but the recommendation implies causation. The self-selection problem is not addressed.
4. BUSINESS IMPACT
⚠️ NEEDS WORK - "Growth lever" is directional, not quantified. Translate the 3x lift into projected revenue.
BOTTOM LINE
Address the correlation-vs-causation gap before presenting.
Why Bridgekit
Built for how data scientists actually work

No context switching. No prompt engineering. Just faster, sharper analysis.

Lives in your notebook

Your analysis already lives in Jupyter. Bridgekit meets you there - one import, one function call.

No prompt engineering required

Bridgekit knows you're a data scientist presenting findings. It asks the right questions automatically.

Consistent and reproducible

A chatbot asks you to re-explain your work every time. Bridgekit is one function call - same interface, every time.

About
Why this exists

Data scientists aren't being replaced - they're being asked to do more with less time and more pressure to be right. Bridgekit is a growing suite of small, focused tools that bring AI into your existing workflow to sharpen your thinking, catch your blind spots, and level up your craft.

Each tool is small, focused, and built for the way data scientists actually work. More are on the way.

Have a question or suggestion? Open an issue on GitHub