Parallel Orchestration
npx machina-cli add skill a5c-ai/babysitter/parallel-orchestration --openclawParallel Orchestration
Coordinate multiple specialized agents across parallel work streams with quality gates and conflict resolution.
Agents
- Database Engineer -
database-engineer - API Developer -
api-developer - UI Developer -
ui-developer - Test Engineer -
test-engineer - Documentation Writer -
documentation-writer
Workflow
- Prepare execution plan with stream batches
- Dispatch specialized agents per stream (ctx.parallel.all)
- Execute tasks sequentially within each stream
- Quality gate after each task with convergence loop (max 3 iterations)
- Check for cross-stream conflicts
- Resolve conflicts if found
- Run integration verification
- Merge stream results
Inputs
tasks- Tasks from decomposition phasestreams- Work stream definitionsqualityThreshold- Minimum quality score (default: 80)maxParallel- Maximum parallel streams (default: 5)githubRepo- GitHub repo for progress sync (optional)
Outputs
- Per-stream execution results
- Integration verification report
- Conflict detection and resolution report
- Merge result
Agent Dispatch (5-12 parallel agents per issue)
Agents are dispatched based on stream type:
database->database-engineerapi->api-developerui->ui-developertesting->test-engineerdocs->documentation-writerinfrastructure->architect
Process Files
ccpm-parallel-execution.js- Standalone parallel executionccpm-orchestrator.js- Phase 5 of full lifecycle
Source
git clone https://github.com/a5c-ai/babysitter/blob/main/plugins/babysitter/skills/babysit/process/methodologies/ccpm/skills/parallel-orchestration/SKILL.mdView on GitHub Overview
Parallel Orchestration coordinates specialized agents across parallel work streams, enforcing quality with iterative gates and resolving cross-stream conflicts. It dispatches the right specialists per stream (database-engineer, api-developer, ui-developer, test-engineer, documentation-writer), runs tasks within streams, and merges results after integration verification.
How This Skill Works
Prepare an execution plan with stream batches, then dispatch agents per stream based on type (database -> database-engineer, api -> api-developer, ui -> ui-developer, testing -> test-engineer, docs -> documentation-writer, infra -> architect). Each stream executes tasks sequentially while a quality gate is applied after every task, looping up to three iterations to converge. After convergence, cross-stream conflicts are checked and resolved before running integration verification and merging the per-stream results.
When to Use It
- A feature spans database, API, UI, and tests requiring coordinated delivery
- You need strict quality gates with a convergence loop (up to 3 iterations)
- Cross-stream conflicts are likely and must be resolved before merging
- You want parallel throughput capped by maxParallel with optional GitHub progress sync
- An end-to-end integration verification is required before delivering the merged result
Quick Start
- Step 1: Define tasks and streams, and set qualityThreshold and maxParallel
- Step 2: Dispatch agents per stream using the stream type mappings
- Step 3: Run tasks, apply quality gates, resolve conflicts, run integration verification, and merge results
Best Practices
- Define clear per-stream scopes and success criteria for each agent
- Set sensible defaults for qualityThreshold and maxParallel
- Map streams to the correct agents to avoid role mismatches
- Enforce the 3-iteration convergence cap to prevent stalls
- Capture and review per-stream results, conflict/resolution reports, and the integration verification
Example Use Cases
- Rolling out a new checkout flow by coordinating DB schema, API endpoints, UI changes, tests, and documentation
- Data migration with synchronized API surface and UI updates
- End-to-end feature delivery requiring cross-team collaboration across streams
- Documentation updates that mirror code changes in features or APIs
- Infrastructure changes coordinated with tests and documentation
