| name | stream-chain |
| description | Stream-JSON chaining for sequential multi-agent pipelines and data transformation. Use when step N's output must feed step N+1's input (build->test->optimise, extract->transform->validate). Not for parallel swarms or GitHub CI. |
| version | 1.0.0 |
| category | workflow |
| tags | ["streaming","pipeline","chaining","multi-agent","workflow"] |
Stream-Chain Skill
Execute multi-step workflows where each agent's complete output flows into the next
step as context, enabling sequential data transformation and multi-agent coordination.
Two modes:
- Custom chains (
run) — your own prompt sequence, full control.
- Predefined pipelines (
pipeline) — battle-tested workflows for common tasks.
When to use
- Sequential pipelines where each step builds on the previous output.
- Multi-stage data transformation (extract -> transform -> validate -> report).
- Iterative refinement or verification chains (implement -> test -> verify).
When NOT to use
- Parallel multi-agent swarm orchestration -> use
swarm-advanced (this skill is sequential).
- GitHub-specific CI/CD pipelines -> use
github-workflow-automation.
- Cloud workflow automation with Flow Nexus -> use
flow-nexus-swarm.
- Simple single-step tasks that need no chaining -> run the task directly.
Quick start
Custom chain (minimum 2 prompts; output flows step-to-step):
claude-flow stream-chain run \
"Analyze codebase structure" \
"Identify improvement areas" \
"Generate action plan"
Predefined pipeline:
claude-flow stream-chain pipeline analysis
Full QA sweep (analysis -> refactor -> test -> optimize) via helper script:
./scripts/qa-chain.sh 60
Common options for both modes: --verbose, --timeout <seconds> (default 30),
--debug.
References
- references/pipelines.md — the four predefined pipelines
(analysis / refactor / test / optimize) with steps and use cases, pipeline options and
output, and custom pipeline definitions in
.claude-flow/config.json.
- references/cookbook.md — how context flows between steps,
custom-chain and advanced worked examples (security audit, migration, data transform),
best practices, swarm/memory/neural integration, troubleshooting, and performance
characteristics.
- scripts/qa-chain.sh — runnable full QA chain.
