eval

eval is an LLM evaluation & observability skill for AI agents, published by mifunedev in the openharness repository on GitHub. Skillspace rates eval D (64/100) and it passed the safety scan; the rating is based on static analysis only.

Run the context fitness-function probe suite (.oh/evals/probes/*.sh) against real state and write the .oh/evals/RESULTS.md benchmark. Each probe is a deterministic 3-state oracle (PASS/REGRESSION/SKIPPED); a green→red transition is surfaced as a REGRESSION naming the lesson it closes. Tier-B behavioral evals are out of scope. TRIGGER when: asked to run evals, check the probe suite, "run /eval", verify a lesson's probe is green, benchmark the harness, or before/after editing a rule/skill that a probe guards.

Grade
D
Quality
64 / 100
Safety
Passed
GitHub stars
24

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.

View source on GitHubmifunedev/openharness

Our review

Architecture
82 / 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

  • 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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