causal-dag
causal-dag is an Experimentation & statistics skill for AI agents, published by RobsonTigre in the everyday-causal-skills repository on GitHub. Skillspace rates causal-dag D (64/100) and it passed the safety scan; the rating is based on static analysis only.
Guides DAG construction and causal identification through structured conversation. Generates dagitty (R) or DoWhy (Python) code for adjustment sets, testable implications, and visualization. Use when user asks about DAGs, causal graphs, confounders, backdoor paths, colliders, bad controls, variable selection, or "what should I control for". Not for estimating causal effects (hand off to method skills).
- Grade
- D
- Quality
- 64 / 100
- Safety
- Passed
- GitHub stars
- 53
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
- 93 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- 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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