cje
cje is an LLM evaluation & observability skill for AI agents, published by cimo-labs in the cje repository on GitHub. Skillspace rates cje C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Runs CJE (Causal Judge Evaluation, pip install cje-eval) to compare LLM models, prompts, or policies from LLM-judge scores, producing calibrated estimates with honest confidence intervals and refusing claims the data cannot support. Use when the user wants to compare models/prompts/policies using judge scores or eval-harness output, put a confidence interval on an LLM eval metric, calibrate an LLM judge against ground-truth (oracle/human) labels, check whether an existing judge calibration still holds on new data, or decide how many human labels an eval needs. Raw judge-score averages are miscalibrated — never compare policies by averaging them; use this skill instead.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 43
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
- 100 / 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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