Deep Debug Skill — Ensemble Variant v1

Benchmark variant. Structurally identical to deep-debug except for Phase 3 step 3b (spawn) and step 3e (drain + aggregate). Both phases now spawn a three-model judge panel per batch — Sonnet 4.6 via Task + GPT-5.4 via mcp__pal__chat + Gemini 2.5 Pro via mcp__pal__chat — with explicit plausibility aggregation via aggregate_ensemble_judges_debug.py. All other phases (Phase 3a, 3a-coherence, 3c, 3d, 3f critic/rebuttal, Phases 4/5/6/7/8) unchanged from baseline.

Panel composition: same 3 providers as deep-qa-ensemble-v1. See that skill's Ensemble Judge Panel section for the full rationale — panel composition, canonical pal-subagent prompt template, and watchdog thresholds are identical.

Aggregation: majority vote on PLAUSIBILITY. Tie-break on 3-way split is disputed (NOT fail-up) — promoting unvetted hypotheses to leading on disagreement would feed weak hypotheses into Phase 4 probe generation. Evidence tier = min across parseable judges (most conservative). Pass-2 verdict = majority vote, default to CONFIRM on tie. All per-model verdicts preserved in state.

Aggregator script: aggregate_ensemble_judges_debug.py in this skill's directory. Invoke via Bash after draining each batch. See the invocation example in step 3e below.

Adversarial hypothesis-driven debugging. Given a bug, generate competing hypotheses across orthogonal dimensions, judge each independently (two-pass blind), run discriminating probes that falsify the weaker, fix the survivor with test-first discipline, and escalate to architectural review after 3 failed attempts. Output is a verified fix with a failing-test-now-passes proof, OR an honest termination label naming why the bug resisted a code-level fix.

Execution Model

All operations use Claude Code primitives. These contracts are non-negotiable: