SkillBench

There's HumanEval for code and MMLU for knowledge — nothing measures whether a model can write a PRD your team can execute, a postmortem that finds the real root cause, or a board update that survives the room. SkillBench is that benchmark: a fixed set of realistic professional tasks, a published rubric, and a pinned LLM judge — fully reproducible from this repo.

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🏆 Model rankings

🔬 How it's scored

Each model runs every task two ways — bare (task only) and skilled (task + the matching SKILL.md). The judge scores each output 1–5 on the same fixed rubric the library uses — structure, completeness, usefulness, grounding — with the judge model + version pinned per release. The headline SkillBench score is the mean skilled score; skill lift is skilled minus bare.

▶ Run it yourself

SkillBench is open and reproducible — add a model to the board by running the harness and committing the result. Estimate cost first with --dry-run.

# Score one model (Anthropic / OpenAI / Gemini via env keys)
ANTHROPIC_API_KEY=… node skillbench/run-skillbench.mjs --models claude-sonnet-4-6

# Compare across providers
ANTHROPIC_API_KEY=… OPENAI_API_KEY=… GEMINI_API_KEY=… \
  node skillbench/run-skillbench.mjs --models claude-opus-4-8,gpt-4o,gemini-2.0-flash