Learning-Bayesian-Statistics

Learning-Bayesian-Statistics / baygent-skills

Public

A set of skills to call your agent Bayes. Thomas Bayes.

41
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100% credibility
Found Mar 12, 2026 at 41 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A pack of instructions that empower AI coding assistants to guide users through complete, reliable Bayesian statistical modeling from planning to reporting.

How It Works

1
🔍 Discover baygent-skills

You come across baygent-skills, a helpful set of guides that teach your AI coding friend how to handle tricky statistics with smart uncertainty checks.

2
📥 Add the guides

You grab the pack and slip it into your AI assistant's special toolbox folder, unlocking new powers.

3
💻 Prepare your data

You gather your numbers, like customer info or test scores, ready for analysis.

4
💬 Chat with your AI

You casually ask your AI buddy, 'Help me understand patterns in this data with uncertainties,' and it gets to work.

5
🔄 Watch the magic

Your AI follows a careful routine: dreaming up possibilities, testing them, crunching numbers, and double-checking everything.

6
✅ Smart quality checks

It runs built-in tests to spot issues and confirm the results are solid and reliable.

📊 Celebrate insights

You get a clear, trustworthy report with ranges of possibilities, perfect for decisions or sharing.

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AI-Generated Review

What is baygent-skills?

baygent-skills equips AI coding agents—like those in Claude Code, Cursor, or Gemini CLI—with specialized skills for Bayesian modeling via the Agent Skills spec. Written in Python, it leverages PyMC for sampling and ArviZ for diagnostics, guiding agents through full workflows: prior checks, MCMC inference, convergence tests, calibration plots, and reports. Users get reliable stats outputs from natural prompts, without agents skipping guardrails like HDIs or LOO-CV.

Why is it gaining traction?

Unlike generic code gen, it enforces opinionated sequences—prior predictives first, auto-save InferenceData, non-centered hierarchies—that agents ignore alone, yielding trustworthy results fast. The lean skills mean focused tools for diagnostics or comparisons, with CLI scripts for calibration checks and model reports. Devs hook on prompts like "fix my divergent model" spitting out energy plots and fixes.

Who should use this?

Data scientists prompting agents for logistic regressions on churn data or hierarchical models across schools. ML engineers diagnosing MCMC issues in production pipelines. Stats teams set skills for agents handling A/B tests or causal inference, skipping manual PyMC boilerplate.

Verdict

Early maturity at 41 stars and 1.0% credibility score, but strong READMEs and MIT license make it low-risk to clone and set skills via terminal. Grab it if Bayesian agent workflows fit—solid foundation, more skills incoming.

(178 words)

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