ExpertPack Eval

Measure ExpertPack EK (Esoteric Knowledge) ratio and run automated quality evals. Use when: (1) Measuring what percentage of a pack's content frontier LLMs cannot produce on their own, (2) Running automated eval sets against a pack-powered agent with LLM-as-judge scoring. Requires OpenRouter API key (auto-resolved from OpenClaw auth or OPENROUTER_API_KEY env var). Companion to the main expertpack skill. Triggers on: 'EK ratio', 'measure EK', 'blind probe', 'eval expertpack', 'pack quality eval', 'run eval', 'esoteric knowledge ratio'. Note: packs are Obsidian-compatible — eval results (ek_score) can be added to file frontmatter and queried in Obsidian via Dataview.

Measure and evaluate ExpertPack quality. Companion to the core expertpack skill.

Note: This skill makes external API calls to OpenRouter for blind probing and LLM-as-judge scoring. Requires an API key.

1. Measure EK Ratio

Blind-probe frontier models to measure what percentage of a pack's propositions they cannot answer without the pack loaded:

python3 {skill_dir}/scripts/eval-ek.py <pack-path> [--models model1,model2] [--sample N] [--output FILE]

• Default models: GPT-4.1-mini, Claude Sonnet 4.6, Gemini 2.0 Flash (via OpenRouter)
• API key: Auto-resolves from OpenClaw auth profiles or OPENROUTER_API_KEY env var
• Judge model: Claude Sonnet (GPT-4.1-mini is unreliable as judge — defaults to "partial")
• Output: YAML with per-proposition scores and aggregate ratio

Interpretation:

EK Ratio	Meaning
0.80+	Exceptional — almost entirely esoteric
0.60–0.79	Strong — majority esoteric
0.40–0.59	Mixed — significant GK padding
0.20–0.39	Weak — most content already in weights
< 0.20	Minimal value-add

Add measured ratio to manifest.yaml:

ek_ratio:
  value: 0.72
  measured: "2026-03-12"
  models: ["gpt-4.1-mini", "claude-sonnet-4-6", "gemini-2.0-flash"]
  propositions_tested: 142

2. Run Quality Eval

Automated eval against a pack-powered agent endpoint:

python3 {skill_dir}/scripts/run-eval.py \
  --questions <eval-set.yaml> \
  --endpoint <ws://host:port/path> \
  --output <results.yaml> \
  --label "baseline"

• Build eval set: 30+ questions (basic, intermediate, advanced, out-of-scope)
• Fix one dimension at a time: structure → agent training → model
• Re-run after each change to verify improvement

Learn more: expertpack.ai · GitHub