# Exploring LLM Evaluations

> Investigate AI observability evaluations of both types — hog (deterministic code-based) and llmjudge (LLM-prompt-based). Find existing evaluations, inspect their configuration, run them against specific generations, query individual pass/fail results, and generate AI-powered summaries of patterns across many runs. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, or manage the evaluation lifecycle (create, update, enable/disable, delete).

## Facts
- Page: https://tashan.sh/capability/skill-posthog-exploring-llm-evaluations
- tashan id: skill:PostHog/exploring-llm-evaluations
- Source: https://github.com/PostHog/skills
- Type: skill
- Category: other
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 96.0
- Freshness: 92.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- Official: no

## Install

```sh
cp -r exploring-llm-evaluations ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-20 by tashan (https://tashan.sh) from public evidence. Scorer s5.