effort-bench
effort-bench is an LLM evaluation & observability skill for AI agents, published by nagisanzenin in the effortmining repository on GitHub. Skillspace rates effort-bench D (61/100) and it passed the safety scan; the rating is based on static analysis only.
Drive the effortmining A/B benchmark harness, the deterministic Python core (bench/effort.py) that measures pass-rate and token cost per (task-class, effort-tier) and fits the calibration table the effortmine skill dispatches from. Use to validate the instrument, run the matrix, grade, analyze, report, or refit. This skill shells out to the harness; it does not compute results itself.
- Grade
- D
- Quality
- 61 / 100
- Safety
- Passed
- GitHub stars
- 30
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
- 94 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
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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