data-analysis
data-analysis is an Agent behaviour & coding discipline skill for AI agents, published by gaasher in the Agent-Loop-Skills repository on GitHub. Skillspace rates data-analysis D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Use when the user wants an iterative, self-checking exploratory analysis of a dataset — surfacing findings that are each verified by re-running the computation, not asserted. Proposes one specific hypothesis at a time, writes and runs analysis code to test it, and records the finding only if the numbers support it at a meaningful effect size; loops until no new verified finding appears or the budget is hit. The result is a findings report where every claim is backed by a reproducible number. Not for diagnosing a single known anomaly or pipeline failure, and not for verifying an external claim against sources (that is a claim-verification task) — this is open-ended discovery over a bound dataset.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 132
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
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Category is a classifier prediction; no model has read this skill 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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