| name | pipeline-creator |
| description | Use when the user wants to create a custom agent pipeline, add a new pipeline to an existing agent, or design a multi-stage workflow that chains skills together - guides through pipeline JSON creation with validation |
Pipeline Creator
I'm using the pipeline-creator skill to create a custom agent pipeline.
Process
1. Understand the Workflow
Ask the user:
- What is the end goal? (e.g., "build a SaaS app", "create training materials")
- Which agent should own this? (
coding-agent, docs-agent, startup-agent, or a new one)
- What stages are needed? List the steps from start to finish
2. Map Stages to Skills
For each stage, identify the right skill from the library. Read QUICK_REFERENCE.md to find matching skills.
Common skill paths:
obra-superpowers/skills/brainstorming
obra-superpowers/skills/writing-plans
obra-superpowers/skills/requesting-code-review
obra-superpowers/skills/verification-before-completion
obra-superpowers/skills/finishing-a-development-branch
backend-api/api-designer
backend-api/database-schema
frameworks/fastapi-builder
frameworks/react-component
devops-infrastructure/docker-composer
code-quality/code-reviewer
code-quality/test-generator
code-quality/security-auditor
business-communication/pitch-coach
business-communication/email-polisher
anthropics-official/document-skills/pptx
data-engineering/analytics-builder
community/d3js-visualization
3. Determine Stage Types
For each stage, choose:
skill — single skill invocation
skill-chain — multiple skills in sequence (same context)
parallel — independent branches dispatched as subagents
Parallel rule: Branches MUST be independent. No shared files. No shared state. If work depends on each other, use sequential stages or a skill-chain instead.
4. Design Data Flow
For each stage:
- Outputs: What files does this stage produce? Use template vars:
{date}, {name}, {project}
- Inputs: What does this stage need from earlier stages? Use interpolation:
${stage_id.outputs.key}
5. Add Gates
Decide where to add checkpoints:
approval — user reviews before continuing (skipped in autonomous mode)
quality — code review subagent runs (always enforced)
verification — evidence-based check (always enforced)
Best practices:
- Add approval gates after design/planning stages
- Add quality gates after build/parallel stages
- Add verification gates before the final stage
6. Generate the JSON
Create the pipeline file at:
.claude/skills/agents/<agent>/references/pipelines/<name>.json
Validate the JSON:
python3 -c "import json; json.load(open('path/to/pipeline.json')); print('valid')"
7. Update the Agent
Add the new pipeline to the agent's SKILL.md routing table:
- Add a row to the "Available Pipelines" table
- Add a routing case in the "Route" section
8. Validate
Check against these rules:
Pipeline Template
{
"name": "my-pipeline",
"description": "What this pipeline produces",
"mode": "interactive",
"stages": [
{
"id": "stage-1",
"type": "skill",
"skill": "category/skill-name",
"description": "What this stage does",
"outputs": {
"output_key": "docs/{date}-{name}-output.md"
},
"gate": {
"type": "approval",
"prompt": "Stage 1 complete. Continue?"
}
},
{
"id": "stage-2",
"type": "skill",
"skill": "category/another-skill",
"description": "Next stage",
"inputs": {
"input_key": "${stage-1.outputs.output_key}"
},
"outputs": {
"final_output": "output/{name}-result.md"
}
}
]
}
Integration
- Invoked by: User who wants to create a custom pipeline
- References:
pipeline-orchestrator/references/pipeline-schema.md for full schema
- References:
pipeline-orchestrator/references/data-contracts.md for data flow rules