stream-chain

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   # or: refactor | test | optimize

Full QA sweep (analysis -> refactor -> test -> optimize) via helper script:

./scripts/qa-chain.sh 60   # arg = per-step timeout seconds

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.