benchmark-optimization-loop
benchmark-optimization-loop is a Backend performance & caching skill for AI agents, published by affaan-m in the ECC repository on GitHub. Skillspace rates benchmark-optimization-loop C (74/100) and it passed the safety scan; a judge model has also reviewed it.
Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 255,679
Placed #3 of 6 in the judged category final for Backend performance & caching.
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
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Judge review
Placed #3 of 6 in the judged category final for Backend performance & caching.
- Trigger
85 85 - Instructions
85 85 - Specificity
75 75 - Safety
88 88 - Disclosure
55 55 - Maintenance
55 55
Well-ordered eight-step loop with a genuine promotion gate requiring correctness and rollback, though more generic than the tool-specific candidates.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- 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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