pathmc
pathmc is a Statistical modelling skill for AI agents, published by pymc-labs in the pathmc repository on GitHub. Skillspace rates pathmc D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Bayesian path analysis (observed-variable SEM) in PyMC. Compiles a lavaan-inspired formula DSL into a generative PyMC model, then layers introspection, identification diagnostics, the `do()` operator, and causal estimands (ATE/CATE/ATT/ATU/prob) on top. Use when the user asks to specify, fit, or query a Bayesian structural causal model; estimate average treatment effects via g-computation; check identification with adjustment sets or the front-door criterion; or simulate panel/longitudinal counterfactuals.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 97
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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