eval-loop
eval-loop is an Execution & autonomous loops skill for AI agents, published by jacob-dietle in the context-os repository on GitHub. Skillspace rates eval-loop C (73/100) and it passed the safety scan; the rating is based on static analysis only.
This skill should be used when a specific quality problem (UX, data, architecture, feature) needs systematic diagnosis and iterative fixing toward a defined target. Traces symptoms to root causes, sets measurable targets with automated backpressure (unit tests, Playwright, LLM-as-judge, or rubric scoring), and iterates until targets pass. Use when user reports a quality gap ("this is a 3/10"), when shipping a feature that needs a quality bar ("what would 10/10 look like?"), or when a class of problems keeps recurring. Core pattern — symptom → generalize → root causes → targets → fix → verify → loop. Step 0 routes predictive/scoring problems OUT to eval-driven-scoring.
- Grade
- C
- Quality
- 73 / 100
- Safety
- Passed
- GitHub stars
- 101
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
- 97 / 100
- Maturity
- Structured: ships references or scripts
- 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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