cai-causal-graph
cai-causal-graph is a Statistical modelling skill for AI agents, published by causalens in the cai-causal-graph repository on GitHub. Skillspace rates cai-causal-graph D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Define, manipulate, and serialize causal graph structures (DAG, CPDAG, MAG, PAG) using cai-causal-graph. Provides CausalGraph, Node, Edge, Skeleton classes with variable type support (CONTINUOUS, BINARY, MULTICLASS, ORDINAL). Use when user imports from cai_causal_graph, works with causal graphs, needs graph conversion (NetworkX, adjacency matrix, dict), or mentions DAG/CPDAG/PAG/MAG representations. Do NOT trigger for graph neural networks or knowledge graphs unrelated to causality.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 61
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
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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