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dag-mutation-strategist

Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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包含的文件(2)

  • SKILL.md5.3 KB
  • references/revisioned-mutation-and-invalidation.md1.1 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

DAG Mutation Strategist

Decides HOW to mutate a DAG when things go wrong. Given a failure diagnosis from dag-quality or dag-ops, selects a bounded recovery proposal. Use Revisioned Mutation and Invalidation: a proposed graph mutation is not execution, and it must preserve the old graph revision, affected descendants, and effect disposition.


When to Use

✅ Use for:

  • Choosing a mutation strategy after a node fails
  • Deciding between retry, replan, fork, or escalate
  • Adapting to mid-execution discoveries that change the plan
  • Cost-aware recovery using task-specific measured or declared estimates

❌ NOT for:

  • Detecting failures (use dag-quality)
  • Executing the mutation (use dag-planner to rewire, dag-runtime to execute)
  • General error handling in code

Strategy Selection

flowchart TD
  F[Failure detected] --> T{Failure type?}
  
  T -->|Transient: API timeout, rate limit| R{Effect status and idempotency known?}
  R -->|Yes| RB[Retry under bounded local policy]
  R -->|No| RC[Reconcile external effect before rerun]
  T -->|Model: wrong format, refusal| M{Evidence supports a changed capability or prompt?}
  M -->|Yes| U[Propose capability/profile change]
  M -->|No| P[Escalate missing diagnosis]
  
  T -->|Contract: output schema mismatch| C[Validate producer/consumer contract and affected descendants]
  T -->|Quality or acceptance failure| Q{Bounded changed-factor experiment exists?}
  Q -->|Yes| L[Propose revision with acceptance and regression checks]
  Q -->|No| HG[Escalate with evidence]
  
  T -->|Logic: wrong approach entirely| D{Cost of replan vs remaining budget?}
  D -->|Fits declared resources and authority| RP[Replan affected subgraph]
  D -->|Does not fit| HG
  
  T -->|Possible upstream contribution| FIX[Trace candidate causes and affected descendants]
  FIX --> RC
flowchart LR
    A[Immutable graph revision] --> B[Mutation proposal]
    B --> C[Contract, authority, cycle, and duplicate-effect checks]
    C --> D{Checks pass?}
    D -->|Yes| E[New revision plus affected-descendant set]
    D -->|No| F[Keep prior revision and escalate]
    E --> G[Runner may execute separately and emit receipts]

Strategy Catalog

StrategyPreconditionsRequired record
RetryEffect status, idempotency, and local retry policy are knownAttempt lineage and reconciliation result
Prompt or capability changeEvidence identifies an unmet contract or capability gapExact changed factor and acceptance check
Contract repairProducer/consumer mismatch and affected descendants are knownSchema/semantic contract revision
Fork or replanAuthority, resource, merge, and duplicate-effect policy existNew graph revision and join rule
Insert validatorA declared acceptance gap needs independent evidenceEvaluator identity and result provenance
EscalateNo bounded safe experiment remainsEvidence, unknowns, and decision requested

Decision Principles

  1. Evidence before ranking: choose a recovery only after classifying the failure and affected effect boundary.
  2. Cost is task-local: compare measured or declared resource estimates, acceptance value, and authority; do not use fixed model prices.
  3. Do not repeat an unchanged hypothesis: escalate when no discriminating experiment remains.
  4. Repair supported causes, not just symptoms: when evidence shows Node A produced invalid input consumed by Node C, invalidate and repair the affected path from Node A while checking for additional causes.

Anti-Patterns

Retry Everything

Wrong: Retrying every failure with the same prompt and model despite unchanged evidence and hypothesis. Right: Retry only after effect reconciliation and an idempotency-safe local policy; change strategy when the next attempt would not test a different condition.

Expensive Recovery for Cheap Failures

Wrong: Selecting a recovery from provider/model labels or remembered prices. Right: Compare measured task-specific cost, capability evidence, and acceptance risk before proposing a change.

Ignoring Cascade Failures

Wrong: Assuming Node A is the sole root cause because Node C consumed its output. Right: Trace dependency, shared-cause, and independent-failure hypotheses; invalidate and repair only the descendants supported by the evidence, while preserving unresolved alternatives.

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