/agada-bench — Standing BrainLayer Quality Benchmark

Score live BrainLayer recall against the frozen 4-domain gold corpus. Run whenever BrainLayer changes (PR merges, FM fixes ship, enrichment updates) to detect regressions or confirm improvements.

The Mission

MISSION = a regression/improvement scorecard for the current BrainLayer build.
Not "ran some brain_search calls." Not "produced numbers."
Done = bench-report.md exists + per-domain breakdown + regression diff vs prior baseline + brain_store TASK_DONE.

Anything short of a complete scorecard with provenance-tagged hits is a partial run — flag it loudly, don't summarize over it.

Two modes

Mode 1 (default) — /agada-bench runs the bench

Score live BL recall against the standing gold. Use this 95% of the time. Triggers: post-PR-merge audit, weekly drift check, post-enrichment-schema-change verification, post-embedder-swap regression sweep.

See workflows/run-bench.md for the 6-wave procedure.

Mode 2 (rare subcommand) — /agada-bench build extends the standing corpus

Add a new user-domain to the frozen gold when a distinct retrieval topic becomes important enough to benchmark. Spawns 3 judges in cmux panes and runs the full 7-phase build pipeline.

See workflows/build-new-domain.md for the build pipeline.

What is and isn't

Invocation

Bench mode (default)

# Run-anywhere invocation from a Claude/Codex session with BL MCP
/agada-bench

# Score only a subset of domains (e.g., skip architecture's LOW-POWER noise explicitly)
/agada-bench --domains techgym,freelance,recruiting

# With baseline for regression diff
/agada-bench --baseline <artifact-dir>/previous-bench-summary.json

# Custom output dir + K values
/agada-bench --output-dir <artifact-dir>/ --k 1,3,5,10,20,50

# Include LOW-POWER architecture in aggregates (default: excluded)
/agada-bench --include-low-power

What the skill does under the hood:

1. scripts/run-bench.py prepare loads the 4 frozen gold paths + each domain's phase-0b-corpus/corpus.jsonl → emits bench-queries.jsonl with 67 queries + verbatim brain_search args.
2. Operator (Claude/Codex session) reads workflows/run-bench.md and executes Waves 1–6: fires live brain_search per query, classifies each returned chunk for provenance (anti-placebo), appends to bench-results.jsonl.
3. scripts/run-bench.py score reads results, computes recall@K / MRR / precision@5 / placebo rate / regression diff → emits <date>-brainlayer-quality-bench-results.md + <date>-bench-summary.json.
4. brain_store TASK_DONE chunk with headline metrics + verdict.

Build mode (rare subcommand)

# Add a new domain to the standing corpus
/agada-bench build --session ~/.claude/projects/.../<new-session>.jsonl --domain health-whoop

# With 4-way panel (Runs 2-4 historical setup)
/agada-bench build \
  --session <jsonl> \
  --domain family \
  --judges claude,codex,cursor,gemini \
  --schema v1.1-3p-1s \
  --primary-judges claude,codex,cursor \
  --shadow-judge gemini

See workflows/build-new-domain.md for the full 7-phase build pipeline (corpus extract → judge dispatch → liveness gate → crossref → κ → gold-lock → pending-RT).

The standing gold corpus (4 frozen domains)

LOW-POWER means recall@K is statistically meaningless on that domain (single positive sample). Excluded from cross-domain aggregates by default.

Verbatim brain_search args live in each domain's phase-0b-corpus/corpus.jsonl (sibling dir of gold). See references/prior-runs.md for the full provenance.

Locked decisions (bench mode)

Locked decisions (build mode)

When extending the standing corpus with a new domain, the same defaults from v1 build apply:

Resolve the actual cascade adjudicator through claude.judgment using node scripts/model-roles.mjs claude.judgment --field model from the golems checkout. Choose effort per /large-plan phase and pass it explicitly at dispatch.

Evidence: W3.3 cascade calibration

W3.3 recorded 12 of 13 v1.1 pending-RT cases as FM6-PreCompact single-judge outliers. Its original recipe said "cheap Opus resolves"; current adjudication uses the role above.

See references/judge-panel.md for the full κ rationale.

When to use bench mode

• After ANY BrainLayer change that could affect retrieval (PR merges, schema updates, embedder swaps, FM fixes shipping).
• Periodic drift checks (weekly / biweekly) even when nothing visible changed — to catch silent regressions.
• Pre-flight before a major BL refactor — establish the baseline summary that future runs compare against.

When NOT to use bench mode

• One-off "does brain_search work" check — just call brain_search directly.
• Domain-specific deep-dive that doesn't need the full 198-pair corpus — use brain_search ad-hoc.
• Testing a non-BrainLayer retrieval system — agada-bench is BL-specific.

When to use build mode

• A new user-domain has emerged. Build a fresh ~50-row gold corpus so the bench can score recall on it.
• A domain's source session is replaceable with a much-better representative session (rare — usually we add a new domain rather than rebuild an old one).

When NOT to use build mode

• The 4 existing domains cover your audit need — just run bench mode.
• You're tempted to "rebuild techgym with a fresher session." The standing gold is frozen for a reason — rebuilding breaks the prior-baseline regression chain.
• You're tempted to iterate the rubric. Don't. That's a v2 task.

v1 known limitations (deferred to v2)

Build-mode limits (from extract-corpus.py)

Auto-extraction from session JSONL only has access to what BL renders in the brain_search tool_result text:

1. chunk_id is a BL-rendered prefix (e.g., rt-0c2e3cb8-), not the full chunk_id. Tonight's hand-curated runs used a live BL DB lookup to resolve prefixes; v1 doesn't.
2. chunk_full_content is BL's one-line truncation, not the verbatim chunk body. Judges grade based on this truncation.
3. adjacent_chunks is always empty — surfacing neighbors requires a live DB lookup.

All gated by the same fix: extract-corpus.py needs a live brain_recall(chunk_id_prefix) lookup to resolve prefixes and pull full bodies. That's a v2 task; see references/roadmap-v2.md Phase A.5.

Bench-mode limits (from run-bench.py)

1. Python can't call MCP — live brain_search must happen in the operator's Claude/Codex session.
2. Anti-placebo classification is operator-driven — relies on the operator inspecting chunk_created_iso and source_session_id per the checklist in workflows/run-bench.md. Errors here corrupt every downstream metric.
3. Run 4 LOW-POWER: architecture (1 L2+L3 pair) is excluded from aggregates by default. Acceptable for v1; v2 may rebuild architecture with a richer source session.

v2 roadmap (deferred, documented)

Per W1.7's skeptical pass, v1.1 is SHIP. v2 triggers only when the flip conditions in references/roadmap-v2.md fire (FM12 > 0 on any run, second judge dies, etc.). v2 buildout sketches:

• Phase A: Platt-scale verbalized confidence + per-domain calibration (W1.1 + W1.3)
• Phase A.5: chunk_id-prefix-to-full resolution via live brain_recall (extract-corpus.py limitation)
• Phase B: Abstention τ=0.55 + risk-coverage curve (W1.5)
• Phase C: Reasoning-tree audit module (W1.2 ADOPT-LITE)
• Phase D: Production-modal hardening (W1.6 applicable subset)

Composability

This skill is referenced by:

• /orc — when sweeping multi-domain regressions.
• The post-PR-merge ritual after BrainLayer PRs land.

This skill references:

• /never-fabricate — anti-placebo classification is mandatory; no "true_hit" without verified chunk_created_iso vs query_asked_at.
• /cmux-agents — only build mode uses cmux pane spawn (for judge dispatch); bench mode runs entirely in the operator's session.
• the false-green-gate hook — bench-report.md verdict must say GREEN | YELLOW | RED based on actual numbers, not approximations.

Quick reference

Bench mode (default — the daily-use case)

# Run inside a Claude/Codex session with BL MCP connected
python3 "$GOLEMS_ROOT/skills/golem-powers/agada-bench/scripts/run-bench.py" prepare \
  --gold techgym:<gold-root>/techgym/phase-3-gold/gold.jsonl \
  --gold freelance:<gold-root>/freelance/phase-3-gold/gold.jsonl \
  --gold recruiting:<gold-root>/recruiting/phase-3-gold/gold.jsonl \
  --gold architecture:<gold-root>/architecture/phase-3-gold/gold.jsonl \
  --output <artifact-dir>/$(date +%Y-%m-%d)-bench-queries.jsonl

# Operator: read workflows/run-bench.md, do the 6 waves of brain_search, write bench-results.jsonl

python3 "$GOLEMS_ROOT/skills/golem-powers/agada-bench/scripts/run-bench.py" score \
  --queries <artifact-dir>/$(date +%Y-%m-%d)-bench-queries.jsonl \
  --results <artifact-dir>/$(date +%Y-%m-%d)-bench-results.jsonl \
  --output <artifact-dir>/$(date +%Y-%m-%d)-brainlayer-quality-bench-results.md \
  --json-out <artifact-dir>/$(date +%Y-%m-%d)-bench-summary.json \
  --baseline <previous-summary.json>

Or via the top-level driver:

bash "$GOLEMS_ROOT/skills/golem-powers/agada-bench/scripts/run-agada.sh"

Build mode (rare subcommand)

# Add a new domain (run once per new domain — a few times a year)
bash "$GOLEMS_ROOT/skills/golem-powers/agada-bench/scripts/run-agada.sh" build \
  --session <new-domain-session.jsonl> \
  --domain <new-domain-name> \
  --output <artifact-dir>/2026-MM-DD-agada-<new-domain>/

Prior runs index

See references/prior-runs.md for the 4 standing-corpus domains and the dates they were locked.

End SKILL.md. Primary workflow: workflows/run-bench.md. Build workflow: workflows/build-new-domain.md. Per-judge briefs: adapters/. Standing-gold provenance: references/prior-runs.md.