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Debug conductor

Skill bordenet/superpowers-plus/skills/engineering/debug-conductor

PREVIEW - Conductor-led bounded investigation for complex distributed system incidents. Serial or parallel branches. Produces incident packets. Persistence tooling not yet implemented.From its SKILL.md

Install
npx -y skills add bordenet/superpowers-plus --skill debug-conductor

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SKILL.md

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Debug Conductor

Research basis: Kim et al. (2025) — centralized orchestration contains errors to 4.4× vs 17.2× for independent agents. Optimal team: 3–4 investigators. Fork only when single-agent is insufficient.

When to Use

  • Complex incident crossing service boundaries
  • Debugging stalled after systematic-debugging + think-twice
  • Multiple plausible hypothesis domains (telephony, LLM, state, infra)
  • Production impact requiring faster resolution than serial investigation

When NOT to Use

  • Single service, clear error message → use systematic-debugging
  • First investigation attempt (always start serial)
  • Budget exhausted (>80% consumed — see fork-readiness-rubric.md)

Example Invocation

Investigate: payment failures across checkout and fulfillment services since 2pm UTC
Evidence: error logs, DB records for 3 failed transactions, user reports
Hypothesis domains: API gateway routing, payment provider responses, state persistence

The Conductor Protocol

Phase 1: Incident Triage

  1. Receive incident description from user or escalation from systematic-debugging
  2. Extract structured context:
    • Affected systems and services
    • Timeline (when detected, any mitigation in place)
    • Available evidence (logs, traces, metrics, user reports)
    • Recent changes (deployments, config, code)
  3. Classify hypothesis domains:
    • Timeline / trace gaps
    • Telephony call flow
    • LLM / prompt behavior
    • State consistency
    • Infrastructure / config / deployment
  4. Initialize incident packet in working context (schema: skills/_shared/incident-packet-schema.md; protocol-only — no persistent store)

Phase 2: Fork Decision

Apply the Fork-Readiness Rubric (see skills/_shared/fork-readiness-rubric.md).

ScoreDecision
< 6SERIAL — continue with systematic-debugging
≥ 6, anti-fork signal presentSERIAL — log why fork was blocked
≥ 6, no anti-fork signalsFORK — proceed to Phase 3

Record the rubric score and rationale in the incident packet.

Operator checkpoint: Before forking, present the rubric score, proposed investigator mix, and estimated budget to the user.

ResponseAction
Approve (yes, go, LGTM)Proceed to Phase 3
Redirect (change investigators, adjust hypotheses)Apply user's adjustments, re-present for approval
Reject (no, don't fork, stay serial)Fall back to serial systematic-debugging
Conditional ("yes but limit to 2 branches")Apply constraints, proceed
Explain more ("why these investigators?")Provide rationale, re-present; do NOT auto-proceed
Off-topic / unclearClarify once: "Should I proceed with forked investigation?" If still unclear → single-agent fallback
Echo (repeats the plan back)Treat as approval
No response / silenceStay serial (systematic-debugging); log that fork was available but not approved

Phase 3: Investigator Assignment (Fork Path)

  1. Select investigators based on classified domains (max 4):
    • Timeline & Trace Investigator — always included if trace data available
    • Domain-specific investigator(s) — telephony, LLM, state, infra
    • Reproduction & Experiment — only if hypothesis is testable
  2. Scope each mandate:
    • Specific hypothesis to investigate
    • Allowed tools (constrained to domain)
    • Token budget (25% of remaining per branch)
    • Wall-clock limit (5 minutes per branch)
    • Required output: both supporting AND disconfirming evidence
  3. Dispatch investigators as parallel sub-agents
    • Each investigator gets: incident context + scoped mandate + evidence schema
    • Investigators do NOT communicate with each other (centralized only)

Phase 4: Evidence Collection & Validation

As investigators return evidence:

  1. Validate — is the evidence properly structured? Does it include confidence?
  2. Detect duplicates — Jaccard similarity > 0.7 between branch evidence → merge
  3. Monitor budgets — kill branches exceeding limits
  4. Check confidence — kill branches with < 0.3 confidence after first evidence
  5. Update incident packet in working context (in-memory only; not persisted across sessions)

Phase 5: Adjudication

When all investigators complete (or budget exhausted), dispatch evidence-adjudicator:

  1. Pass all branch evidence packets to evidence-adjudicator
  2. Adjudicator builds reasoning tree, weighs evidence strength, detects contradictions
  3. Adjudicator produces RootCauseVerdict with confidence score and alternative causes
  4. Conductor receives verdict and proceeds to Phase 6

Ownership: The conductor orchestrates; evidence-adjudicator synthesizes. The conductor MUST NOT perform adjudication itself.

Phase 6: Resolution

  1. Record adjudicator verdict in incidentPacket.adjudication (in working context)
  2. Record final budget usage in incidentPacket.budget
  3. If confidence ≥ 0.8: Present root cause + recommended next steps
  4. If confidence 0.5–0.8: Present ranked hypotheses, recommend targeted experiments
  5. If confidence < 0.5: Escalate to user with what we know and what we don't
  6. Record resolution actions in incidentPacket.nextSteps
  7. Always: Invoke failure-autopsy for post-resolution learning
  8. Always: Update investigation-state with final verdict
  9. Always: Log to TODO ledger any deferred follow-ups

Bounded Forking Constraints

ConstraintValueAction at Limit
Max concurrent investigators4Queue additional; explain to user
Max total branches6Stop branching; dispatch evidence-adjudicator with current branches
Per-branch token budget25% of totalKill branch
Per-branch wall-clock5 minutesKill branch
Min confidence to continue0.3Kill branch
Duplicate thresholdJaccard > 0.7Merge branches

Failure Modes

FailureDetectionRecovery
All investigators find nothingAll branches < 0.3 confidenceEscalate to user; suggest new evidence sources
Investigators agree on wrong causeHigh confidence but contradicted by reproductionRequire reproduction before accepting verdict
Cost explosionBudget > 80% with no verdictStop forking; dispatch evidence-adjudicator with partial findings
Conductor bottleneckQueued evidence > 3 items unprocessedProcess evidence in batch; simplify validation
Circular investigationSame hypothesis re-investigatedTrack hypothesis IDs; reject duplicates
Adjudicator failureevidence-adjudicator times out or returns malformed verdictRetry once; if still failed, escalate with partial ranked evidence and mark adjudication as degraded

Companion Skills

  • systematic-debugging — serial investigation (always try first)
  • investigation-state — evidence persistence across sessions
  • think-twice — escalation signal (fork trigger)
  • adversarial-search — embedded in adjudicator Step 5b (disconfirmation pass)
  • failure-autopsy — post-resolution learning
  • thinking-orchestrator — hub router (routes here when fork-ready)

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