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.