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YAML-based DAG workflow engine with topological execution and failure strategies
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Based on SOC occupation classification
Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline
Full Self Driving — autonomous release loop that processes all auto-dev-eligible GitHub issues until none remain, by repeatedly running /pipeline auto-dev then /homework.
On explicit /homework invocation, analyze the current and linked previous sessions, extract mistakes (찐빠), and report them via omcustom-feedback with a confirmation gate. Auto-activation on session cleanup/session-end signals is OPT-IN (default OFF) — requires an explicit project/user directive. Use when explicitly auditing recent work for harness gaps.
hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis
Pre-action boundary checking — validates agent tool calls against declared capabilities and task contracts
Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile
| name | dag-orchestration |
| description | YAML-based DAG workflow engine with topological execution and failure strategies |
| scope | core |
| context | fork |
| user-invocable | false |
Defines and executes directed acyclic graph (DAG) workflows. The orchestrator uses this skill to plan multi-step tasks with dependencies, execute them in topologically-sorted order, and handle failures.
Orchestrator-only — only the main conversation uses this skill (R010). Subagents execute individual nodes.
# .claude/workflows/<name>.yaml or inline in conversation
workflow:
name: feature-implementation
description: Implement a new feature with tests and docs
nodes:
- id: analyze
agent: Explore
model: haiku
prompt: "Analyze codebase for integration points"
- id: implement
agent: lang-typescript-expert
model: sonnet
prompt: "Implement the feature"
depends_on: [analyze]
- id: test
agent: qa-engineer
model: sonnet
prompt: "Write and run tests"
depends_on: [implement]
- id: review
agent: lang-typescript-expert
model: opus
prompt: "Code review"
depends_on: [implement]
- id: docs
agent: arch-documenter
model: sonnet
prompt: "Update documentation"
depends_on: [implement]
- id: commit
agent: mgr-gitnerd
model: sonnet
prompt: "Commit changes"
depends_on: [test, review, docs]
config:
max_parallel: 4 # R009 limit
failure_strategy: stop # stop | skip | retry
retry_count: 2 # Max retries per node (if strategy=retry)
timeout_per_node: 300 # Seconds per node (0 = no limit)
1. Parse workflow YAML
2. Build adjacency list and in-degree map
3. Validate: detect cycles (error if found)
4. Initialize queue with nodes where in-degree = 0
5. While queue is not empty:
a. Dequeue up to max_parallel nodes
b. Execute nodes in parallel via Task tool (R009)
c. On completion:
- Success → decrement in-degree of dependents
- Failure → apply failure_strategy
d. Enqueue newly-ready nodes (in-degree = 0)
6. Verify all nodes executed (detect unreachable nodes)
| Rule | Detail |
|---|---|
| Max parallel | 4 concurrent nodes (R009) |
| Agent Teams gate | 3+ parallel nodes → check R018 eligibility |
| Orchestrator only | DAG scheduling runs in main conversation (R010) |
| Node execution | Each node = one Task tool call to specified agent |
| State tracking | /tmp/.claude-dag-$PPID.json |
| Strategy | Behavior |
|---|---|
stop | Halt entire DAG on first failure (default) |
skip | Mark failed node as skipped, continue dependents with warning |
retry | Retry failed node up to retry_count times, then stop |
{
"workflow": "feature-implementation",
"started_at": "2026-03-07T10:00:00Z",
"status": "running",
"nodes": {
"analyze": {"status": "completed", "started": "...", "completed": "..."},
"implement": {"status": "running", "started": "..."},
"test": {"status": "pending"},
"review": {"status": "pending"},
"docs": {"status": "pending"},
"commit": {"status": "blocked", "blocked_by": ["test", "review", "docs"]}
},
"execution_order": [["analyze"], ["implement"], ["test", "review", "docs"], ["commit"]]
}
[DAG] feature-implementation — 6 nodes
[Layer 0] analyze ← running
[Layer 1] implement ← pending (depends: analyze)
[Layer 2] test, review, docs ← pending (parallel, depends: implement)
[Layer 3] commit ← blocked (depends: test, review, docs)
Progress:
[DAG Progress] 3/6 nodes completed
✓ analyze (12s)
✓ implement (45s)
→ test (running)
→ review (running)
→ docs (running)
○ commit (blocked)
nodes: [analyze → implement → [test, review, docs] → commit]
nodes: [review → fix → re-review → commit]
failure_strategy: retry
nodes: [
analyze → [impl-frontend, impl-backend, impl-db] → integration-test → commit
]
nodes: [
analyze → plan → [refactor-1, refactor-2, refactor-3] → test → review → commit
]
| Rule | Integration |
|---|---|
| R009 | Max 4 parallel nodes; independent nodes MUST parallelize |
| R010 | DAG scheduler runs only in orchestrator |
| R015 | Display DAG plan before execution |
| R018 | 3+ parallel nodes → check Agent Teams eligibility |
| model-escalation | Node failures feed into task-outcome-recorder |
| stuck-recovery | Repeated node failures trigger stuck detection |
For ad-hoc workflows without a YAML file:
[DAG Plan]
1. analyze (Explore:haiku)
2. implement (lang-typescript-expert:sonnet) ← depends: 1
3. test (qa-engineer:sonnet) ← depends: 2
4. review (lang-typescript-expert:opus) ← depends: 2
5. commit (mgr-gitnerd:sonnet) ← depends: 3, 4
Execute? [Y/n]
The orchestrator builds the DAG from this inline format and executes using the same algorithm.