parallel-implement-feature
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Implement approved OpenSpec proposal using DAG-scheduled multi-agent parallel execution
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NPX Install
npx skill4agent add jankneumann/agentic-coding-tools parallel-implement-featureTags
Translated version includes tags in frontmatterSKILL.md Content
View Translation Comparison →Parallel Implement Feature
Implement an approved OpenSpec proposal using DAG-scheduled multi-agent parallel execution. Each work package runs in its own worktree with explicit scope and lock claims. Degrades to linear-implement-feature behavior when the coordinator is unavailable.
Arguments
$ARGUMENTSPrerequisites
- Approved OpenSpec proposal with at
work-packages.yamlopenspec/changes/<change-id>/ - Contracts generated at
openspec/changes/<change-id>/contracts/ - Coordinator available with required capabilities
Coordinator Capability Check
At skill start, detect coordinator capabilities:
REQUIRED (hard failure without coordinator):
CAN_DISCOVER — discover_agents() for agent health monitoring
CAN_QUEUE_WORK — submit_work()/get_work()/complete_work()/get_task() for DAG dispatch
CAN_LOCK — acquire_lock()/release_lock()/check_locks() for resource claims
REQUIRED (safety):
CAN_GUARDRAILS — check_guardrails() for destructive operation detection
ENRICHING (degrades gracefully):
CAN_HANDOFF — write_handoff() for orchestrator resumability
CAN_MEMORY — remember()/recall() for procedural memories
CAN_POLICY — check_policy() for authorization decisions
CAN_AUDIT — query_audit() for execution summary generationIf required capabilities are unavailable, degrade to behavior and emit a warning.
/linear-implement-featureSteps
Phase A: Feature-Level Preflight (Orchestrator)
A1. Parse and validate work-packages.yaml
- YAML parse + validate against work-packages.schema.json
- Lock key canonicalization check
- File scope non-overlap for parallel packages
- Logical lock non-overlap for parallel packages
A2. Validate contracts exist
- Every file in contracts.openapi.files exists on disk
- Primary OpenAPI file parses without errors
A3. Compute DAG order
- Build directed graph from packages[].depends_on
- Detect cycles (validation error if found)
- Topological sort for execution order
A4. Submit work queue tasks
- For each package in topological order:
- Resolve depends_on package_ids to task_ids
- Build input_data envelope with context slice
- submit_work() with task_type, description, priority, depends_on
A5. Begin monitoring loop
- Poll discover_agents() for agent health
- Poll get_task(task_id) for each in-flight package
- On each completion: dispatch newly unblocked packagesPhase B: Package Execution Protocol (Every Worker Agent)
Each worker agent claiming a package via MUST execute steps B1-B11 from the execution protocol (see design.md section 2.4).
get_workKey steps: session registration, pause-lock check, deadlock-safe lock acquisition, code generation within scope, deterministic scope check via git diff, verification steps, structured result publication.
Phase C: Review + Integration Sequencing
C1. Result validation (on each package completion)
- Validate result against work-queue-result.schema.json
- Verify contracts_revision and plan_revision match
C2. Escalation processing
- Execute escalation protocol for any escalations
C3. Per-package review
- Dispatch /parallel-review-implementation on each package diff
C4. Integration gate
- Wait for all packages COMPLETED and reviewed
C5. Integration merge (wp-integration package)
- Merge all worktrees into feature branch
- Run full test suite and cross-package contract verification
C6. Execution summary generation
- DAG timeline, contract compliance, review findingsFinal Steps
After integration:
- Run quality checks (pytest, mypy, ruff, openspec validate)
- Create PR with execution summary attached
- Write handoff if CAN_HANDOFF=true
Output
- Feature branch:
openspec/<change-id> - All tests passing
- PR created with execution summary
Next Step
After PR is approved:
/parallel-cleanup-feature <change-id>