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dag-failure-analyzer

Investigates DAG incidents with a precise top event, timeline, competing hypotheses, barriers, propagation, and bounded recovery. NOT for automated root-cause classification, trace collection, or performance profiling.

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  • SKILL.md4.5 KB
  • references/source-and-hand-checks.md10.9 KB

SKILL.md(원문)

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DAG Failure Analyzer

Error text and dependency reachability generate hypotheses; neither establishes cause. Preserve uncertainty when evidence cannot discriminate mechanisms. The incident method reference contains three worked incidents, discriminating checks, barrier analysis, and recovery gates.

Method 1 — establish an investigation record

Freeze the execution revision and incident window. State a precise top event, then build an evidence timeline with timestamp source and uncertainty. Collect attempt records, dependency contracts, external status, resource measurements, recent changes, and barrier/control records. Separate initiating event, latent condition, contributing factor, barrier failure, downstream propagation, and symptom.

Method 2 — test competing causes

For each candidate cause, create a causal-factor edge with evidence pointer, assumption, and a discriminating counterfactual test. DAG reachability can show which consumers could propagate a prerequisite failure; it cannot show that the prerequisite caused a temporal neighbor. Regexes may label a candidate signal but must not choose the cause.

flowchart LR
  T[Precise top event] --> L[Evidence timeline and clock provenance]
  D[Dependency and attempt graph] --> H[Competing hypotheses]
  L --> H
  H --> X[Discriminating test or missing evidence]
  X -->|inconclusive| U[Unknown; contain and investigate]
  X -->|supports mechanism| A{Authorized action assumptions hold?}
  A -->|no| E[Escalate or preserve hold]
  A -->|yes| R[Retry contain compensate or roll forward]

Method 3 — choose recovery only under its preconditions

Classify a proposed action as retry, contain, compensate, roll forward/back, or escalate. Retry requires an explicit reason another attempt is useful, a safe budget, and current retry authority. For a possibly effectful operation it also needs either an authoritative operation-bound terminal-absence receipt with a fence against late commit, or a validated same-operation/context idempotency contract. Compensation requires a confirmed effect, applicable postconditions, and its own authority; separately reauthorize a retry afterward under a fence or same-operation deduplication rule. Permission or policy denial is not transient by default. Finish with a falsifiable corrective action, owner, and re-observation predicate.

flowchart TB
  C[Latent condition candidate] -. candidate contribution .-> I[Initiating event candidate]
  I -. candidate contribution .-> B[Barrier failure candidate]
  B -. candidate contribution .-> F[Observable top event]
  E1[Timeline and attempt receipts] -. test or weaken .-> I
  E2[Barrier record] -. test or weaken .-> B
  E3[Counterfactual comparison] -. test or weaken .-> C
  F --> P[Query declared downstream symptoms]
  P --> O[Corrective owner and re-observation]

Hand check

C times out after 80 seconds queued; B completed in 3 seconds; the service reports throttling; a configuration rollout preceded the incident. Rate limit, queue saturation, and rollout regression are competing hypotheses. Queue metrics and unaffected-worker comparisons discriminate. Do not call it resource exhaustion from a timeout string. A permission denial gets no blind retry even if few descendants failed.

Sources and limits

NASA's Fault Tree Handbook v1.1, NASA RCAT, and the NASA mishap investigation overview support timelines, causal-factor trees, barriers, and disciplined alternatives. The source access is aerospace-method scope; it does not validate a regex table, automatic root-cause finding, or portable probability estimate.

Related skills

  • dag-execution-tracer supplies attempt and coverage evidence.
  • dag-performance-profiler supplies performance measurements; timing alone does not establish cause.
  • dag-pattern-learner can surface prior cases as hypotheses, and dag-dynamic-replanner owns runtime graph changes.

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