Deep QA Ensemble V1

Deep QA Skill — Ensemble Variant v1

Benchmark variant. This skill is structurally identical to deep-qa except for severity-judge batches in Phase 3 step 10 (pass-1 blind) and Phase 5.5.b (pass-2 informed). Both phases now spawn a three-model judge panel per batch — Sonnet 4.6 via Task tool + GPT-5.4 via mcp__pal__chat + Gemini 2.5 Pro via mcp__pal__chat — with explicit verdict aggregation (majority-vote severity, max confidence, fail-up tiebreaker, preserved per-model rationales). All other contracts — Phases 0, 1, 2, 3 steps 1–9, 4, 5, 5.5.a coherence, 5.6 rationalization audit, 6 — are unchanged from baseline. Ensemble logic lives below in the "Ensemble Judge Panel" section; diffs from baseline are marked with ▶ in the affected phases.

Systematically audit an existing artifact for defects using parallel critic agents across QA dimensions tailored to the artifact type. Unlike deep-design (which designs and iterates) or deep-research (which explores the web), deep-qa takes an artifact as-is and finds what's wrong with it.

No spec drafting. No redesign. Find and report.

Execution Model

Shares deep-design's core execution contracts:

• All data passed to agents via files, never inline. Artifact, known-defects list, angle definitions — all written to disk before spawning.
• State written before agent spawn, not after. spawn_time_iso written before Agent tool call. Spawn failure records spawn_failed status.
• Structured output is the contract; free-text is ignored. Severity judges produce machine-parseable structured lines. Unparseable output → fail-safe critical.
• No coordinator self-review of anything load-bearing. Severity classification delegated to independent judge agents.
• Termination labels are honest. Seven defined labels map to all reachable termination paths — never "no defects remain." See Phase 5 for the complete vocabulary.
• Hard stop is unconditional. hard_stop = max_rounds * 2 is set at initialization and checked at the start of every round before any user prompt. No extension can exceed it.

Shared contracts: this skill inherits the four execution-model contracts (files-not-inline, state-before-agent-spawn, structured-output, independence-invariant) from _shared/execution-model-contracts.md. The items listed above are the skill-specific elaborations; the shared file is authoritative for the base contracts.

Cross-finding coherence: this skill applies the coherence-integrator pattern from _shared/cross-finding-coherence.md at Phase 5.5.a-coherence — after draining pass-1 judges and BEFORE pass-2 informed judges. The integrator reads all deduped critic output files simultaneously and annotates each finding with cross-finding relationships (contradictions, emergent patterns, coverage gaps). These annotations are included in pass-2 judge input files so judges see the cross-finding context when confirming/upgrading/downgrading severity.

Subagent watchdog: every run_in_background=true spawn in this skill (severity judges, coordinator summaries, batched pass-2 judges) MUST be armed with a staleness monitor per _shared/subagent-watchdog.md. Use Flavor A (Monitor tail per spawn) with thresholds STALE=3 min, HUNG=10 min for Haiku judges and summaries — these are short-running tasks and a 30-min quiet period is always pathological. TaskOutput status field is not evidence of progress; output-file mtime is. This contract adds timed_out_heartbeat to this skill's termination vocabulary (per-lane watchdog kill) and stalled_watchdog / hung_killed to per-lane state — see shared doc §"State schema additions" + §"Termination-label addition".

Adversarial judging (3 of 4 mechanisms adopted)

See _shared/adversarial-judging.md for the full pattern: blind severity protocol, mandatory author counter-response, rationalization auditor, falsifiability drop.

Current deep-qa adoption status:

Ensemble Judge Panel (variant-specific)

This section is additive to baseline deep-qa. All baseline execution-model contracts still apply.

Panel composition

Each judge batch — pass 1 (blind) AND pass 2 (informed) — spawns three independent judges in parallel:

Why Task-wrap the pal calls: preserves run_in_background=true, Monitor-based watchdog, and the per-spawn state.json contracts from baseline. The subagent's prompt is: "Call mcp__pal__chat with model={M}, prompt=, write the structured output to {output_path}. Do not add commentary. Return when the write completes." One tool call, deterministic wrapper.

Per-judge output paths

For pass 1: deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_{judge_id}.md For pass 2: deep-qa-{run_id}/judges/batch_pass2_{batch_num}_{judge_id}.md

Each judge writes the same structured DEFECT_ID / SEVERITY / CONFIDENCE / RATIONALE[ / CALIBRATION] block format defined in SYNTHESIS.md — one file per (batch × judge). The panel of three produces three files per batch.

Aggregation rule (applied after batch drains)

Aggregation is deterministic and externalized to aggregate_ensemble_judges.py (in this skill's directory). The coordinator invokes it via Bash rather than computing vote tallies inline — this removes coordinator-arithmetic risk and keeps the semantics auditable + unit-testable.

Invocation (pass 1):

python3 ~/.claude/skills/deep-qa-ensemble-v1/aggregate_ensemble_judges.py \
  --batch-id {batch_id} \
  --pass 1 \
  --judge-file judge_claude=deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_judge_claude.md \
  --judge-file judge_openai=deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_judge_openai.md \
  --judge-file judge_gemini=deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_judge_gemini.md \
  --out-json deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_aggregated.json \
  --out-summary-md deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_aggregated.md

Invocation (pass 2): same shape, but --pass 2 and add --prior-pass1-json path/to/pass1-aggregated.json so the aggregator applies the forced-consistency calibration override.

Rules the script applies (canonical — do not duplicate inline):

1. Severity vote: critical > major > minor. ≥2 agree → that severity. 3-way split → max (fail-safe up). Partial panel of 2 → agreement or max. Partial panel of 1 → use it, flag partial. 0 parseable → retain critic-proposed severity (pass 1) or prior pass-1 aggregated severity (pass 2).
2. Confidence: max across judges whose verdict matched the aggregated severity.
3. Calibration (pass 2): vote + upgrade on 3-way split. Forced-consistency override then applied: aggregated pass-2 severity > pass-1 aggregated → upgrade; less → downgrade; equal → confirm. Override wins over vote when they disagree, and the override is logged in notes.
4. Rationale: concatenation of all parseable judges: [{judge_id}/{model}] ... joined with |.
5. Agreement rate: n_matching_majority / n_parseable.
6. All-fail handling: batch recorded as aggregation_status: "failed". Exit code is still 0; check stderr for batch ... failed=N partial=N summary.

The aggregator's output JSON is the authoritative record — the coordinator reads it to populate defects.{id}.judge_pass_{N}_verdict fields in state.json. Coordinator is NOT permitted to recompute severity/calibration from the per-judge files directly.

State schema extensions (additive — baseline STATE.md still valid)

background_tasks.judges[] entries add a judges sub-array per batch:

{
  "batch_id": "batch_1_1",
  "defect_ids": ["defect_001", "defect_002"],
  "round": 1,
  "pass": 1,
  "status": "running|completed|partial|timed_out|failed",
  "judges": [
    {"id": "judge_claude", "model": "sonnet-4.6", "provider": "anthropic", "task_id": "bg_...", "status": "running|completed|timed_out", "output_path": "..."},
    {"id": "judge_openai", "model": "gpt-5.4", "provider": "pal-custom", "task_id": "bg_...", "status": "running|completed|timed_out", "output_path": "..."},
    {"id": "judge_gemini", "model": "gemini-2.5-pro", "provider": "pal-custom", "task_id": "bg_...", "status": "running|completed|timed_out", "output_path": "..."}
  ],
  "aggregation_log_path": "deep-qa-{run_id}/judges/batch_1_1_pass1_aggregated.md"
}

defects.{id}.judge_pass_{N}_verdict adds two fields:

"judge_pass_1_verdict": {
  "severity": "major",
  "confidence": "high",
  "rationale": "[sonnet-4.6] ... | [gpt-5.4] ... | [gemini-2.5-pro] ...",
  "agreement_rate": 0.67,
  "per_model": [
    {"model": "sonnet-4.6", "severity": "major", "confidence": "high", "rationale": "..."},
    {"model": "gpt-5.4",    "severity": "major", "confidence": "medium", "rationale": "..."},
    {"model": "gemini-2.5-pro", "severity": "minor", "confidence": "low", "rationale": "..."}
  ]
}

Pass 2 additionally stores calibration (aggregated) and per_model[].calibration.

Canonical pal-subagent prompt (paste VERBATIM)

When spawning judge_openai or judge_gemini, use the Task tool with a general-purpose subagent and pass this exact prompt (with {MODEL}, {BATCH_INPUT_PATH}, {OUTPUT_PATH}, and {PASS_N_JUDGE_PROMPT} filled in). Do not paraphrase — the subagent's reliability depends on a tight, well-tested wrapper prompt.

You are a wrapper agent. Your single job is to call one MCP tool and write its raw response to a file. You MUST NOT critique, summarize, reformat, or add commentary.

Execute these steps in order:

1. Call `mcp__pal__chat` with these exact arguments:
   - model: "{MODEL}"
   - prompt: |
{PASS_N_JUDGE_PROMPT}

   - working_directory_absolute_path: "/root"

2. Extract the `content` field from the tool response (ignore `metadata`, `continuation_offer`, `content_type`).

3. Use the Write tool to save the extracted content VERBATIM to: {OUTPUT_PATH}
   Do not edit it. Do not add a header. Do not fix formatting. If the judge model's output doesn't match the expected DEFECT_ID/SEVERITY/CONFIDENCE format, the aggregator script handles that — your job is only to preserve the raw response.

4. Return a single line: `JUDGE_WRITE_OK {OUTPUT_PATH}` if successful, or `JUDGE_WRITE_FAILED {reason}` if any step failed.

Failure modes to handle:
- If `mcp__pal__chat` raises an error, do NOT retry. Write a placeholder file with the literal content `JUDGE_CALL_FAILED: {error_message}` to {OUTPUT_PATH} and return `JUDGE_WRITE_FAILED pal_call_error`. The aggregator treats this as `missing_file`.
- If the tool response is missing a `content` field, write `JUDGE_PARSE_FAILED: response had no content field` to {OUTPUT_PATH} and return `JUDGE_WRITE_FAILED no_content`.

Do not call any other tools. Do not write to any other path. Do not spawn any sub-tasks.

{PASS_N_JUDGE_PROMPT} substitution: paste the literal pass-1 or pass-2 judge prompt from SYNTHESIS.md verbatim, with {batch_input_path} replaced by {BATCH_INPUT_PATH}. Do not modify the judge prompt itself — the ensemble protocol depends on all three judges seeing the same instructions.

judge_claude uses a different template — it is a Task subagent running Sonnet natively (not via pal). Its prompt is just the pass-N judge prompt directly, with an added line Write your output verbatim to: {OUTPUT_PATH} at the end. No MCP wrapping needed.

Post-hoc analysis artifact

After all judge batches drain, write deep-qa-{run_id}/judges/ensemble-summary.md containing:

• Global agreement rate (across all defects, all batches, both passes)
• Per-pair agreement (sonnet↔gpt, sonnet↔gemini, gpt↔gemini) — surfaces which provider pairs decorrelate most
• Disagreement cases with the largest severity span (e.g., critical vs minor) — worth human review
• Per-model confidence distribution — are any models systematically over/under-confident?

This file is read by skill-bench's post-run parser (via the schema above) and feeds the benchmark.json run_summary.

Artifact Types

See DIMENSIONS.md for full dimension tables and angle examples.

Workflow

Phase 0: Input Validation Gate

--diff mode (fast post-commit QA)

When --diff [ref] is present, the artifact is built from the git diff rather than a full file. This costs ~10% as much as a full-repo QA and catches regressions in the changed code and its immediate callers.

Step 0a-diff — Build diff artifact:

1. Run git diff {ref} — include ALL tracked files, not just *.py. Frontend code (.svelte, .tsx, .ts, .js, .vue), templates, SQL, proto files, YAML manifests, and JSON fixtures are routinely consumers of data contracts that Python code changes, and must be in scope. (Previously this step filtered to *.py, which caused UI/frontend defects in multi-language projects to be invisible to diff-mode QA.)
2. If diff is empty: error "No changes found between HEAD and {ref}."
3. Extract changed files from the diff header lines (--- a/..., +++ b/...).
4. For each changed file, find callers of any added/modified function or method:
   • Use grep -rn "def <name>" <changed_file> to extract function names from + lines
   • Use grep -rn "<name>(" --include="*.py" to find call sites in the repo
   • Include the call-site file + ±10 lines of context for each hit (cap at 5 callers per function, 20 functions total)
5. Build artifact.md with three sections:

   # Diff QA Artifact
   ## Ref: {ref} → HEAD
   ## Changed files: {list}
   
   ## Section 1: The Diff
   {full git diff output, unified format}
   
   ## Section 2: Caller Context
   {per-function: function name, callers found, relevant snippets}
   
   ## Section 3: Pre-existing behavior (for context only)
   {unchanged surrounding code ±20 lines for each changed hunk}
   

6. Size check: same as normal mode (~80k token warning).

Automatic angle seeding in diff mode — in addition to normal dimension angles, always add these high-priority angles before round 1:

• "Legacy-symbol sweep (MANDATORY, CRITICAL priority). Enumerate every pre-change name, string literal, dict key, constant, attribute, env var, or magic value that this PR is replacing (e.g. old function names, hardcoded strings like \"start\"/\"end\", old config keys, old sentinels). Then grep -rn the entire repository — NOT just changed files — for each one. For every remaining occurrence, classify as: (a) correctly updated in this PR, (b) legitimate legacy-compat path with a documented fallback, (c) stale docstring/comment referencing the old contract, or (d) a MISSED UPDATE. Report every (c) and (d). This angle must spawn its own critic; do not fold it into another dimension. Missed updates in unchanged files are the highest-impact latent defects and they are invisible to diff-scope review."
• "Contract fanout audit (MANDATORY, CRITICAL priority whenever the PR changes ANY named or shared contract). A contract here is anything that connects code that changes to code that doesn't: an API signature, a data-shape (dict keys, schema fields, enum values, wire format), a calling convention (how a command is invoked, how a process re-enters itself, how a handler is registered), a named symbol used as an identifier, a protocol, or a configuration key. For every changed contract: (1) enumerate every producer of the contract — every place that emits, constructs, serializes, or writes the contract value; (2) enumerate every consumer — every place that parses, reads, introspects, or renders it; (3) scope the search across the ENTIRE artifact surface — every file, every language, every format the repo contains — not just the files the diff touches and not just the language the diff is written in; (4) for each location classify as: correctly updated / legitimate legacy-compat with documented fallback / stale docstring-comment-message / MISSED UPDATE, and report every stale and missed as separate defects. This principle specializes to many concrete patterns depending on the contract — for illustration only: if the contract is how subprocesses re-enter an entrypoint, producers are every command-builder across orchestrators/runtimes/sidecars/CLI-wrappers; if the contract is the shape of a persisted artifact, consumers include every reader across languages (frontend UIs, generated types, schemas, views, fixtures, dashboards); if the contract is a named identifier used as a dict key, both producers and consumers span the repo. The failure mode being defended against is that refactors reliably update the two or three closest-to-hand producers and consumers and miss the rest — that set of 'the rest' is where the highest-leverage latent defects live. Adapt the concrete search to the actual contract under review; the invariant is the breadth, not any specific command. Reasonable starting points are grep-based symbol searches, git-log-based caller traces, and type/schema-tool searches; if the artifact has non-text components (binaries, generated files), note the gap explicitly rather than skipping."
• "Docstring / comment contract consistency. Grep for docstrings, inline comments, and user-facing error messages that reference the OLD contract by name. For a PR making a backward-compat claim, docstrings that still say run['end'] when end steps can now be renamed ARE user-facing defects — they mislead readers who trust documented contracts. File as minor but DO file."
• "For every new conditional expression in the diff (if, elif, while): what are the False/empty/None/zero branches? Are they all safe?"
• "For every changed function signature or return type: do all callers handle the new contract?"
• "For every attribute, return value, or method that the PR newly makes Optional[X] (previously always non-None): grep every consumer in the repo and verify each handles None without crash, KeyError, or silent wrong-behavior. Do not assume lint or validation catches it earlier — audit the consumer's code."
• "For every new subprocess, file handle, network connection, or lock opened in the diff: is it always closed/drained/released on all exit paths?"
• "For every security-sensitive path touched (auth, subprocess args, file paths, serialization): what are the injection/bypass edge cases introduced by the change?"

artifact_type in diff mode: Default to code. Override with --type as normal.

--diff + --auto: Fully unattended — runs with defaults, no gates.

Print: Starting deep QA on: diff {ref}..HEAD ({N} files changed) [type: code] [run: {run_id}]

After building the diff artifact, proceed to Phase 1 as normal. The artifact IS the diff + context; the workflow is identical.

Step 0a — Read artifact (normal mode, when --diff is NOT present):

• If argument is a file path: read file, write contents to deep-qa-{run_id}/artifact.md
• If argument is inline content: write to deep-qa-{run_id}/artifact.md ⚠️ inline content is silently truncated at context limit — warn user if content appears large
• If empty or inaccessible: error
• Size check: After writing, check approximate token count. If artifact.md exceeds 80k tokens: warn "Artifact is large ({N} tokens). Haiku critics (depth 2+) may only see part of it." If --auto: proceed with warning. If interactive: ask "Continue? [y/N]"

Step 0b — Artifact type detection:

• If --type is provided: use it as authoritative, skip content inference, skip ambiguity prompt. Store artifact_type = --type in state.json.
• If --type not provided: infer from content (file headers, structure, terminology — see DIMENSIONS.md). If ambiguous: "I'm interpreting this as type=[X] because [2-3 evidence signals]. Correct? [y/N/type:doc|code|research|skill]" (skip prompt if --auto).
• Store artifact_type in state.json.

Multi-file discovery (after type is determined):

• If artifact_type == "skill" AND argument was a file path: check the parent directory for companion files matching DIMENSIONS.md, FORMAT.md, STATE.md, SYNTHESIS.md.
• If companion files exist: concatenate all into artifact.md (SKILL.md first, then companions alphabetically). Show Files in scope: [list] in the pre-run scope declaration.
• If --auto and companions found: include them automatically.

Step 0c — Safety check:

• If artifact contains credentials, tokens, or PII: warn user before passing to subagents
• If artifact requests harmful functionality: decline

Print: Starting deep QA on: {artifact_name} [type: {artifact_type}] [run: {run_id}]

Phase 1: Dimension Discovery (see DIMENSIONS.md)

• Select QA dimensions from DIMENSIONS.md based on artifact_type
• Generate 2-4 critique angles per dimension + 2-3 cross-dimensional angles
• Required categories per type must each get at least one angle (CRITICAL priority if uncovered after round 1)
• Cap frontier at 30 angles total

Pre-run scope declaration (show before proceeding; skip entirely if --auto):

Deep QA: "{artifact_name}"
Artifact type: {artifact_type}
Files in scope: {list of files included in artifact.md}
QA dimensions ({N}): {list}
Initial angles: {count}
Suggested max_rounds: {recommendation}
Hard stop: {recommendation * 2} rounds (non-overridable)
Wall-clock estimate: {time range}
Invocation: {interactive | automated (--auto)}

Set max_rounds [default {recommendation}]: _
Continue? [y/N]

If --auto: use recommended max_rounds automatically, do not show this prompt.

max_rounds recommendation formula:

initial_angles = count of angles in Phase 1
min_rounds = ceil(initial_angles / 6)     # 6 agents/round
recommended = ceil(min_rounds * 1.3)      # 30% expansion from agent-discovered sub-angles
recommended = max(recommended, 3)         # never suggest < 3 rounds
recommended = min(recommended, 6)         # cap at 6 for typical artifacts

Phase 2: Initialize State

• Generate run_id: $(date +%Y%m%d-%H%M%S)
• Create directory structure:
  • deep-qa-{run_id}/state.json — run state (see STATE.md)
  • deep-qa-{run_id}/critiques/ — one file per critique angle
  • deep-qa-{run_id}/angles/ — per-angle input files for critics
  • deep-qa-{run_id}/judge-inputs/ — per-defect input files for severity judges
  • deep-qa-{run_id}/artifact.md — copy of artifact content
  • deep-qa-{run_id}/qa-report.md — written at Phase 6
• Write lock file: deep-qa-{run_id}.lock — verify write succeeded before proceeding
• Store hard_stop = max_rounds * 2 in state.json — immutable after initialization

Phase 3: QA Rounds

Hard stop check (fires BEFORE prospective gate, every round, unconditionally):

if current_round >= state.hard_stop:
    → terminate immediately; no prompt; no extension offered
    → label: "Hard stop at round {hard_stop}"
    → proceed directly to Phase 4/Phase 5.5/Phase 6

This check cannot be bypassed. Extensions update max_rounds but never hard_stop.

Prospective gate (fires after hard stop check; skipped if --auto):

About to run QA Round {N}: {frontier_size} angles queued
Critics this round: up to 6 | Potential judge agents: up to {frontier_pop × 5}
Estimated cost: ~${critics_cost + judges_cost + summary_cost} ({running_total} spent so far)
Continue? [y/N/redirect:<focus>]

• If N: stop → label: "User-stopped at round N"
• If redirect: add high-priority angle targeting the specified focus, then proceed
• Skip if --auto

Per round:

1. Pop up to max_agents_per_round (6) highest-priority angles from frontier; enforce frontier cap (see STATE.md)

2. Write all required data to files BEFORE spawning, then verify each write (file exists + non-empty):

   • Known defects file: deep-qa-{run_id}/known-defects.md
   • Angle files: deep-qa-{run_id}/angles/{angle.id}.md
   • If any verification fails: halt with error, do not spawn

3. For each angle, write status: "in_progress" and spawn_time_iso to state.json BEFORE calling Agent tool. After writing, re-read state.json and verify generation == N+1. If mismatch: log conflict, retry once with fresh read, then halt.

4. Spawn critic agents in parallel (120s timeout)

5. On timeout: mark timed_out, write "generation": += 1, do NOT re-queue, do NOT increment dedup counter

6. Collect new angles from ALL completed agents BEFORE running dedup

7. Apply dedup against stable pre-round snapshot. Assign depth = parent.depth + 1 to each critic-reported angle. Reject angles where depth > max_depth. Enforce frontier cap with required-category protection (see STATE.md).

8. For each new defect: Dimension cross-check (synchronous): verify the critique file's declared **QA Dimension:** header matches the angle's assigned dimension in state.json. If mismatch: flag as potential injection, do NOT set required_categories_covered.{category} true. Create defect in state.json with critic-proposed severity and judge_status: "pending".

9. Run coverage evaluation: read required_categories_covered from state.json (not coordinator-summary.md). For any uncovered required category: generate CRITICAL-priority angle. Write "generation": += 1 after updating coverage_gaps and rounds_without_new_dimensions.

10. ▶ Background severity judges (pass-1 blind) — ENSEMBLE PANEL: Batch new defects into groups of up to 5. For each batch: write combined defect data to deep-qa-{run_id}/judge-inputs/batch_{round}_{batch_num}.md with the critic-proposed severity STRIPPED from each defect entry (blind-severity protocol; see _shared/adversarial-judging.md §1). Then spawn a three-model judge panel in parallel — all with run_in_background=true:

11. judge_claude — Task tool spawn of a general-purpose subagent running Sonnet 4.6 (project Claude). Prompt is the pass-1 blind judge template from SYNTHESIS.md with {batch_input_path} and instruction to write the structured output to deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_judge_claude.md.

12. judge_openai — Task tool spawn of a general-purpose subagent whose sole task is: mcp__pal__chat({model: "gpt-5.4", prompt: <verbatim pass-1 blind judge prompt with {batch_input_path} resolved>, working_directory_absolute_path: "/root"}) — then write the returned text verbatim to deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_judge_openai.md. Subagent prompt explicitly forbids commentary, JSON reformatting, or summarization — preserve the pal response as-is.

13. judge_gemini — identical to judge_openai but with model: "gemini" (alias for gemini-2.5-pro) and output to ...pass1_judge_gemini.md.

Record the batch in background_tasks.judges in state.json with pass: 1 and the extended judges[] sub-array (see "State schema extensions" in the Ensemble Judge Panel section). Each entry captures {id, model, provider, task_id, status, output_path} for all three spawns.

Aggregated judge_pass_1_verdict fields are populated during Phase 5.5.a drain — NOT at spawn time.

Watchdog (per-judge): each of the three spawns is armed with a Monitor-based staleness watchdog per _shared/subagent-watchdog.md §"Flavor A" with STALE=3 min, HUNG=10 min. If any single judge watchdog fires, its output path is marked timed_out_heartbeat in background_tasks.judges[].judges[] — the remaining two continue. If two or more fire, the batch is flagged partial and aggregation handles it per the "partial results" branch in the aggregation rule. 11. Background coordinator summary: Spawn Haiku subagent with run_in_background=true to write a cumulative coordinator summary (see SYNTHESIS.md). Record in background_tasks.summaries in state.json. 12. Increment round → immediately proceed to next round's step 1 (do not wait for background tasks)

Pipelining rationale: Severity judges and coordinator summaries are reporting artifacts consumed only in Phase 6. They do not affect angle selection, dedup, or coverage evaluation. Running them in the background while the next round's critics execute hides their latency entirely.

No redesign phase. Defects are catalogued with severity; status remains open unless disputed by validation.

Phase 4: Fact Verification (research artifacts only)

For artifact_type == "research", run before final synthesis. Skip entirely for other types.

• Spawn Haiku verification agent
• Extract top-N factual claims (N = min(20, total claims found))
• Risk-stratified sampling: single-source primary → numerical/statistical → contested → corroboration candidates
• Spot-check citation URLs: accessible? attributed claim present in source text?
• For numerical claims: compare EXACT numbers — flag mismatch even if semantically similar
• Output: deep-qa-{run_id}/verification.md
• See SYNTHESIS.md for full protocol

Phase 5: Termination Check

Note: The hard stop check at the start of Phase 3 fires unconditionally before this check is evaluated. All labels below apply to paths that reach Phase 5.

Any-of-4 — evaluate in order, stop when FIRST is true:

1. User-stopped: User chose N at a prospective gate → label: "User-stopped at round N"
2. Coverage plateau: rounds_without_new_dimensions >= 2 AND all explored angles in state.json have exhaustion_score >= 4 → label: "Coverage plateau — frontier saturated"
3. Budget soft gate: current_round >= max_rounds with non-empty frontier. Show gate (skip if --auto):

   Budget limit reached (max_rounds={N}). Frontier still has {M} unexplored angles.
   Hard stop at round {hard_stop} — remaining headroom: {hard_stop - current_round} rounds.
   Options: [y] Extend by {min(recommended, hard_stop - current_round)} more rounds  [+N] Custom  [n] Stop
   

   • Extension validation: cap any extension at hard_stop - current_round; reject extensions that would reach or exceed hard_stop
   • User chooses n → label: "Max Rounds Reached — user stopped"
   • User extends → update max_rounds, continue; hard_stop is NOT updated
   • --auto: stop immediately → label: "Max Rounds Reached"
4. Frontier empty: evaluate "Conditions Met" check below

"Conditions Met" check (only when condition 4 fires):

• Read required_categories_covered from state.json
• ALL three must be true: (1) frontier empty, (2) all required categories covered, (3) rounds_without_new_dimensions >= 2
• All true → label: "Conditions Met"
• Any false → label: "Convergence — frontier exhausted before full coverage" (list uncovered required categories in report)

Complete label vocabulary — all reachable paths:

Never use a label not in this table. Never write "no defects remain."

Phase 5.5: Drain Background Tasks + Pass-2 Informed Severity

Before proceeding to Phase 5.6 (rationalization audit) and Phase 6 (final report), all background tasks from the pipelined rounds must complete, AND the blind-severity protocol must finish pass 2.

5.5.a — Drain pass-1 blind judges:

1. ▶ Wait for ALL per-judge spawns across all batches. For each entry in background_tasks.judges[] where pass == 1, iterate its judges[] sub-array and call TaskOutput with block=true on each task_id. A batch is considered drained when every one of its three per-judge spawns has either completed or timed out.
2. Wait for ALL background coordinator summaries (from background_tasks.summaries). Use TaskOutput with block=true for each.
3. ▶ Aggregate pass-1 judge results across the three-model panel. For each batch: a. Read each of the three per-judge output files (batch_{round}_{batch_num}_pass1_judge_{id}.md). Parse structured DEFECT_ID / SEVERITY / CONFIDENCE / RATIONALE blocks from each. b. If a file is missing, empty, or unparseable, mark that judge as timed_out or unparseable in background_tasks.judges[].judges[].status. Do NOT halt — proceed with the remaining judges and apply the partial-results rule from "Aggregation rule" §1. c. For each defect in the batch, collect up to 3 per-judge verdicts and apply the aggregation rule (severity vote + max confidence + concat rationale, see "Ensemble Judge Panel / Aggregation rule"). Store the aggregated result as defects.{id}.judge_pass_1_verdict with the per_model sub-array populated from the three parsed verdicts (including timed_out stubs for missing ones). d. Write the aggregation summary to deep-qa-{run_id}/judges/batch_{round}_{batch_num}_pass1_aggregated.md for post-hoc inspection — one section per defect showing all 3 per-judge lines + the aggregated line + agreement rate. e. Set defects.{id}.judge_status: "pass_1_completed". f. Set background_tasks.judges[].status: completed if all 3 judges parseable, partial if 1–2 timed out, failed if all 3 timed out. g. Write "generation": += 1 after each batch is processed.
4. ▶ Handle panel-level pass-1 failures: A defect whose batch aggregated as failed (all 3 judges timed out/unparseable) retains critic-proposed severity. Set judge_status: "pass_1_timed_out". Log JUDGE_PANEL_TOTAL_FAILURE_PASS_1: {batch_id} defect_ids={...}. A batch that aggregated as partial uses the aggregation rule's partial-results branch and proceeds normally to pass 2 — no special handling.

5.5.a-coherence — Cross-finding coherence integrator (fires after pass-1 drain, before pass-2):

Per _shared/cross-finding-coherence.md:

1. Collect all parseable critic output files from all rounds (post-dedup).
2. Write integrator input manifest to deep-qa-{run_id}/coherence/input-manifest.md containing: list of all critic output file paths, artifact path, known-defects path, dimension taxonomy.
3. Spawn Sonnet coherence-integrator agent with the manifest. Output: deep-qa-{run_id}/coherence/round-all-coherence.md. Timeout: 120s.
4. Parse STRUCTURED_OUTPUT block for FINDING|, GAP|, and PATTERN| lines.
5. For each FINDING|{id}|{annotation} line: attach annotation to defects.{id}.coherence_annotation in state.json. Write generation += 1.
6. For each GAP|{dim_a}|{dim_b}|{description}|{angle} line: create a CRITICAL-priority angle in the frontier for the next round (if rounds remain) or flag in the final report (if this is the last round).
7. For each PATTERN|{pattern_id}|{finding_ids}|{root_cause}|{severity_suggestion} line: store in state.json.emergent_patterns[] for Phase 6 final report.
8. If integrator output is unparseable or timed out: log COHERENCE_PARSE_FAILED or COHERENCE_TIMED_OUT. Proceed without annotations — pass-2 judges run normally (degraded mode). Flag in Phase 6 report.
9. If STANDALONE rate is 100% across 6+ findings: log COHERENCE_SHALLOW warning — include in Phase 6 caveats.

Why between pass-1 and pass-2: Pass-1 judges classify severity blind (without critic or cross-finding context). The integrator runs on critic output, not judge output — it identifies relationships between findings, not between severities. Pass-2 judges then receive BOTH the pass-1 verdict AND the coherence annotation, allowing them to upgrade severity for pattern-members or scrutinize contradicted findings.

5.5.b — Spawn pass-2 informed judges (ENSEMBLE PANEL):

For each defect where judge_pass_1_verdict exists: 5. Write deep-qa-{run_id}/judge-inputs/batch_pass2_{batch_num}.md containing: the full defect (INCLUDING critic-proposed severity this time), the aggregated pass-1 verdict (from the three-judge panel; include the aggregated severity + agreement_rate + a compact per-model summary line so the pass-2 panel sees how much the pass-1 panel diverged), any coherence annotation from Phase 5.5.a-coherence (contradiction/pattern/standalone status), and the pass-2 prompt asking the judge to confirm, upgrade, or downgrade with rationale. Coherence annotations give the judge cross-finding context: a PATTERN_MEMBER annotation suggests the judge should consider aggregate severity; a CONTRADICTS annotation suggests the judge should scrutinize the finding's evidence base. 6. ▶ Spawn three pass-2 judges in parallel — same panel composition as pass 1 (judge_claude Sonnet 4.6 via Task tool, judge_openai GPT-5.4 via mcp__pal__chat

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