subagent-eval

name	subagent-eval
description	Empirically evaluate and optimise a Claude Code sub-agent via a blinded head-to-head against the production baseline, then pick its model on data. Use when tuning a sub-agent's prompt, choosing or re-checking its model (e.g. a new model release), proving an agent change before committing, or testing whether a specialist beats native Explore. To author a new agent first, read AUTHORING.md.

Sub-agent eval & optimise

Prove a sub-agent change with evidence before shipping it. Run three times to date (codebase-*, thoughts-*, docs-*) — reproduce that loop. Quality is the deciding metric; cost/latency break ties only.

When to use

• Optimise — a sub-agent's prompt is being edited and the change needs proving.
• Re-validate — a new model (or effort level) shipped; re-check which model and effort each agent should run.
• Compare — does a specialist actually beat the native Explore baseline?
• Create — authoring a new agent: read AUTHORING.md first, then enter this loop.

This is a multi-agent Workflow (opt-in — it spends real tokens). Say "use a workflow" / confirm before launching.

Workflow

1. Frame & pick entry mode. State the agent(s) under test and the decision to make (ship this prompt? which model?). For create, do AUTHORING.md first.
2. Build the benchmark. Per archetype the agent serves (e.g. locate / analyze):
   • Mine real prompts: python3 .claude/eval/mine_subagents.py --print → pull from .claude/eval/mined/corpus.jsonl (shape rows into tasks with shape_tasks.py). Read-only context? shape_tasks.py writes YAML into the repo by default — pass --dry-run, or read corpus.jsonl rows directly (count by exact agent == field, not a substring grep -c, which over-counts routing fields).
   • Add a golden task (hand-written, clear oracle) and an adversarial task (zero-match / trap).
   • Write a verified oracle for each — confirm every expected path/value against the tree now.
   • ☐ Include at least one HARD task with headroom. Near-ceiling tasks don't discriminate — a well-curated repo makes even Explore@haiku score ~1.0 (see docs-* example).
3. Run the head-to-head. Adapt harness-template.js and launch via Workflow. Candidates per task: native baseline (Explore @ its production model, usually haiku), specialist @ sonnet, specialist @ opus.
   • ☐ Effort is a second axis, but not a per-spawn candidate. Unlike model, there is no per-spawn effort override (see REFERENCE.md § Model & effort decision), so you can't add effort cells to CANDS and sweep them in one run. To test effort today, run the head-to-head once per effort: frontmatter value — edit .claude/agents/<name>.md, restart to re-register, re-run — then compare the recorded per-run JSON (Path 2; mind the mid-run file-mutation/provenance caveat). The per-spawn effort opt that would let effort mirror the model sweep in one run is filed as a prerequisite.
   • ☐ Variance-control the deciding axis: ≥3 runs × ≥2 judges per cell for any model decision. 1 run scouts a locate, but never flip a model from a single-run cell — a lone run can fabricate (the ln incident) and swing the mean; the harness now flags an n<3 promotion as inconclusive. Set the harness DECIDING_KINDS to THIS agent's archetype — not literally analyze; a third archetype (e.g. pattern) also needs per-agent contract discriminators added to the judge (REFERENCE.md "Scoring a new agent's contract").
   • ☐ Judges are blinded (no candidate id) and verify every claimed path/Status/citation against the tree before scoring. See REFERENCE.md.
   • ☐ Registry gotcha: a just-created agent is NOT in the session's hot registry — either restart to register, or use the harness's adopt-on-disk bootstrap. See REFERENCE.md.
   • ☐ Script placement: paste the harness inline (or keep it in /tmp); never save an adapted copy under .claude/eval/ — its top-level return trips Biome and blocks the husky pre-commit. See REFERENCE.md.
   • ☐ Dry-run first (zero agents): validate the harness logic with stubbed hooks before launching — see REFERENCE.md "Validate the harness before launch". Re-run after every harness edit; it's free and catches syntax + aggregation bugs.
4. Decide each model and effort quality-first. Default sonnet at the model's tier-default effort. Adopt a costlier cell — a higher model class, or a higher effort level — ONLY if it clears the same bar: mean(candidate) ≥ mean(incumbent) + 0.05 AND it isn't dragged by over-templating. Among cells within 0.05 of the best, pick the cheapest: cost ranks by model tier first, then effort level. Model and effort choices are task-shaped, not class-shaped — see REFERENCE.md § Model & effort decision.
5. Wire routing (if creating/changing an agent's role). Add it to the command fan-outs (create_plan.md, brainstorm.md, iterate_plan.md) and tighten reciprocal when-not redirects in sibling agents' descriptions.
6. Verify + safety.
   • ☐ make check via @agent-codebase-verification (never run make directly).
   • ☐ After any multi-agent run: review the FULL git status + git diff; confirm the agent roster count is unchanged (ls .claude/agents/*.md | wc -l); git restore anything outside the intended seam. Verify provenance before blaming a sub-agent — git state that shifts mid-run is usually the operator's own concurrent commits/branch switches (check git reflog + author), not a stray Bash call.
7. Record. Write thoughts/research/<YYYY-MM-DD>-<family>-agent-headtohead.md, mirroring an existing example: TL;DR → Setup → Results → Decisions applied → Key findings → Per-run JSON → Caveats → Fast follow. Be honest — a tie or a null result is a real finding.

References

• REFERENCE.md — §6.3 rubric, §6.4 aggregation, model & effort decision rule, judge-prompt pattern, registry/bootstrap gotcha, safety checklist, lessons from the runs.
• AUTHORING.md — how to write a new agent before validating it.
• harness-template.js — parameterized Workflow head-to-head for the §6.3 execution pass; fill the tasks/oracles/candidates and launch.
• routing-harness-template.js — the §6.2 routing pass: scores descriptions with no candidate execution — blinded judges route probes among the on-disk descriptions and rate scope-clarity + verbosity. See REFERENCE.md §6.2.
• System of record: thoughts/designs/2026-04-21-sub-agent-eval-pipeline.md (§6.2 routing, §6.3 execution, §6.4 aggregation, §14.1 model floor).
• Worked examples (copy one): thoughts/research/2026-06-02-codebase-agent-headtohead.md, …/2026-06-03-thoughts-agent-headtohead.md, …/2026-06-03-docs-agent-headtohead.md.
• Corpus tooling: .claude/eval/{mine_subagents,shape_tasks,harvest_hook}.py.