pymc-bayesian-modeling
pymc-bayesian-modeling is a Statistical modelling skill for AI agents, published by foryourhealth111-pixel in the Vibe-Skills repository on GitHub. Skillspace rates pymc-bayesian-modeling B (83/100) and it passed the safety scan; a judge model has also reviewed it.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
- Grade
- B
- Quality
- 83 / 100
- Safety
- Passed
- GitHub stars
- 2,381
Placed #2 of 6 in the judged category final for Statistical modelling.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 93 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 1 collections
- Scope
- Vendor-specific: built around one product. Teaches using the public PyMC library for Bayesian modeling.
Judge review
Placed #2 of 6 in the judged category final for Statistical modelling.
- Trigger
90 90 - Instructions
94 94 - Specificity
95 95 - Safety
85 85 - Disclosure
68 68 - Maintenance
75 75
Exceptionally thorough Bayesian workflow with concrete convergence thresholds and remedies for specific failure modes, kept from over-firing by an explicit routing boundary.
Not checked yet
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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