benchmarking

Use this skill when you need to:

• benchmark models or agents
• compare providers for real work
• evaluate which model should own cron/ops/coding/research tasks
• turn real work into reusable evaluation packs
• create league tables, scorecards, or benchmark infographics

Goal

Benchmark operator leverage, not just output prettiness.

A good benchmark should tell you:

• who chooses the right tool/runtime
• who respects hidden constraints
• who recovers from failure intelligently
• who verifies before claiming success
• who is worth routing real work to

Benchmark modes

1) Design mode

Use when you need to create a benchmark or full suite.

Expected outputs:

• README.md
• tasks.json
• answer-key.json or answer-key guidelines
• rubric.md
• optional judge-notes.md

2) Execution mode

Use when you need to run models through an existing benchmark.

Expected outputs:

• results-raw.json
• results-scored.json
• README.md
• optional infographic / league-table PNG

3) Expansion mode

Use when you want to make a benchmark harder or add new tracks. Do not reinvent baseline tasks unless needed.

Benchmark design rules

1. Ground tasks in real work, not toy prompts.
2. Hide important constraints in environment/context, not all in the prompt.
3. Weight judgment above syntax.
4. Include at least one task where the right move is not to act now.
5. Include at least one task about tool/runtime choice.
6. Include at least one task about failure recovery.
7. Include at least one task requiring proof-oriented completion.
8. Separate model failure from provider/harness failure.

Large-run execution rules

1. Confirm the actual model roster from the environment.
2. Run in batches for large rosters.
3. Save raw outputs after each batch.
4. Score after raw outputs are locked.
5. Generate charts last.
6. Better a verified no-PNG pack than an incomplete run with pretty graphics.

Scoring rules

• Prefer deterministic scoring for structured parts.
• Add a human judge layer for operator judgment.
• Keep syntax \x3C=20% of score for hard benchmarks.
• Classify failures explicitly instead of treating all failures as model weakness.

Failure taxonomy

Use these classes when interpreting results:

• MF — Model failure: reasoning/tool choice/accuracy failure with valid harness
• HF — Harness failure: benchmark harness itself broke or mis-scored
• PF — Provider failure: rate limit, provider unsupported, transport failure, 404 model path
• CF — Context failure: prompt too large, missing required context, context-window collapse
• PB — Policy block: task blocked by approval/policy/tool restriction
• SF — Schema/format failure: invalid JSON/structure or repeated parsing failure
• DF — Delegation failure: subagent/runtime orchestration failure, bad handoff, missing proof

Proof rules

Before saying DONE, provide:

• benchmark pack path
• results path(s)
• score summary
• list of failed/skipped models and why
• note on anything still unverified

Recommended folder naming

Use:

• output/benchmarks/YYYY-MM-DD-\x3Cbenchmark-name>/
• keep machine-readable and human-readable files together

Suggested artifact pack

Every serious benchmark should produce most or all of:

• README.md
• tasks.json
• answer-key.json
• rubric.md
• judge-notes.md
• results-raw.json
• results-scored.json
• league-table.png or infographic
• harness.py or equivalent scorer if automation exists

If spawning sub-agents

• use a self-heal pattern
• require checkpoints
• require proof
• do not let agents claim success without file-path evidence

Success criterion

A good benchmark changes routing decisions. If the result would not alter which model you use for real work, the benchmark is probably too soft.