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pipeline-guards

Safety constraints and quality gates for pipeline and workflow execution

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Pipeline Guards Skill

Defines mandatory safety constraints for all pipeline, workflow, and iterative execution within the oh-my-customcode system. Prevents infinite loops, enforces timeouts, and establishes quality gates.

System-wide — these guards apply to dag-orchestration, worker-reviewer-pipeline, and any iterative process.

Guard Limits

GuardDefaultHard CapApplies To
Max iterations35worker-reviewer-pipeline
Max DAG nodes2030dag-orchestration
Max parallel agents45R009 (all pipelines)
Max parallel steps44pipeline parallel blocks
Timeout per node300s600sdag-orchestration nodes
Timeout per pipeline900s1800sworker-reviewer-pipeline
Max retry count23Failure retry strategies
Max PR improvement items2050pr-auto-improve
Max auto-improve items2050omcustom-auto-improve
Max files per agent1015All agent spawns (advisory)

Enforcement

Guards are enforced at two levels:

Level 1: Skill-Level (Soft)

Each skill checks guard limits before execution:

Before starting pipeline:
  1. Check max_iterations ≤ hard cap
  2. Check timeout ≤ hard cap
  3. Check node count ≤ hard cap
  If any exceeded → warn user, use hard cap value

Level 2: Hook-Level (Hard)

The stuck-detector hook monitors for guard violations:

PostToolUse → check:
  - Iteration count > max_iterations?
  - Elapsed time > timeout?
  - Same error repeated > max_retry?
  If any → emit advisory to stderr

Quality Gates

Pipeline Quality Gate

[Quality Gate Check]
├── Critical issues: {count} (must be 0)
├── Major issues: {count} (must be ≤ threshold)
├── Minor issues: {count} (informational)
└── Gate: PASS | FAIL

DAG Completion Gate

[DAG Completion Gate]
├── Nodes completed: {n}/{total}
├── Nodes failed: {count}
├── Nodes skipped: {count}
└── Gate: PASS | PARTIAL | FAIL

Escalation Integration

When guards are triggered, they integrate with existing advisory systems:

EventAction
Max iterations reached→ stuck-recovery advisory
Repeated failures→ model-escalation advisory
Timeout approaching (80%)→ warn user, suggest early termination
Hard cap hit→ force stop, report to user

Task Granularity Guard

Advisory guard for agent task scope. When a single agent is assigned too many files, it becomes a bottleneck in parallel execution.

SignalDefaultAction
Files per agent > 10Advisory warningSuggest splitting by layer/domain
Files per agent > 15Hard warningRequire explicit user override

Display:

[Guard] ⚠ Agent assigned {n} files (> 10) — consider splitting by layer
[Guard] 🛑 Agent assigned {n} files (> 15) — requires explicit override

This integrates with R009 Adaptive Parallel Splitting: if a stalled agent is detected AND it was assigned > 10 files, the splitting recommendation is stronger.

Guard Configuration

Pipelines can override defaults (within hard caps):

# In pipeline/workflow spec
guards:
  max_iterations: 4          # Override default 3, cannot exceed 5
  timeout_per_node: 120      # Override default 300s
  timeout_pipeline: 600      # Override default 900s
  quality_gate: all_pass     # all_pass | majority_pass

Kill Switch

When a pipeline or workflow must be terminated:

[Kill Switch] Activated
├── Reason: {max_iterations | timeout | user_request | stuck_detected}
├── Pipeline: {name}
├── Progress: {completed}/{total} steps
├── Preserved state: /tmp/.claude-pipeline-$PPID.json
└── Action: Stopped gracefully, state saved for resume

The kill switch:

  1. Signals all running agents to complete current operation
  2. Does NOT terminate mid-write (waits for current tool call)
  3. Saves pipeline state for potential resume
  4. Reports final status to user

State Preservation

On guard-triggered termination:

{
  "pipeline": "feature-review",
  "terminated_at": "2026-03-07T10:15:00Z",
  "reason": "max_iterations_reached",
  "completed_iterations": 3,
  "last_verdict": "FAIL",
  "remaining_issues": [
    {"severity": "major", "file": "src/auth.ts", "line": 42, "description": "..."}
  ],
  "worker_last_output": "...",
  "resumable": true
}

Display Format

Guard warnings appear inline:

[Guard] ⚠ Iteration 3/3 — final attempt
[Guard] ⚠ Timeout 80% (240s/300s) — consider early termination
[Guard] 🛑 Max iterations reached — pipeline stopped
[Guard] 🛑 Hard timeout (600s) — force stop

Integration

Rule/SkillIntegration
R009Max parallel agents enforced (hard cap: 5, soft default: 4)
R010Guards run in orchestrator only
R015Guard warnings displayed transparently
dag-orchestrationNode count and timeout limits
worker-reviewer-pipelineIteration and pipeline timeout limits
pr-auto-improveImprovement item count limits
omcustom-auto-improveAuto-improve item count limits
stuck-recoveryGuard triggers feed into stuck detection
model-escalationRepeated failures trigger escalation advisory
task-decompositionSubtask file counts validated against granularity guard thresholds

Checkpoint Gate Integration

각 guard 통과/실패 시 tracker-checkpoint 에이전트로 gate state 기록.

Flow

  1. Guard 진입 → tracker-checkpoint에 gate state: running 기록
  2. Guard 통과 → tracker-checkpoint에 gate state: passed + metrics 기록
  3. Guard 실패 → tracker-checkpoint에 gate state: failed + failure reason freeze
  4. 다음 단계는 checkpoint state 참조하여 재개/중단 판단

Benefits

  • 긴 파이프라인에서 guard 지점마다 복원점 확보
  • 부분 실패 시 직전 guard 지점부터 재시도 가능 (비용 절감)
  • guard metrics 축적으로 품질 추이 관찰 가능

See .claude/agents/tracker-checkpoint.md for the tracker spec.

Override Policy

  • Defaults can be overridden in pipeline spec (within hard caps)
  • Hard caps can ONLY be changed by modifying this skill file
  • User cannot bypass hard caps at runtime
  • All overrides are logged and displayed (R015)

Limitations

  • Guards are advisory at skill level, hard at hook level
  • Cannot prevent infinite loops in agent reasoning (only tool call patterns)
  • State preservation is best-effort (process crash = state loss)
  • Resume from saved state requires user confirmation

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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