stream-chain
FeaturedStream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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Quality Score: 94/100
Skill Content
Details
- Author
- spencermarx
- Repository
- spencermarx/open-code-review
- Created
- 6 months ago
- Last Updated
- 3 weeks ago
- Language
- TypeScript
- License
- Apache-2.0
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stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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Multi-step prompt chaining skill where each step's output becomes the next step's input, with explicit rules, constraints, and reference tokens between steps. Activates when the user needs to build a complex pipeline of dependent Claude calls — research → outline → draft → edit → publish — where downstream steps must be constrained by upstream outputs without hallucination or drift. Designs chains with named output variables, explicit pass rules, validation gates between steps, and failure recovery conditions. Use when user says: build a chain, multi-step prompt, pipeline of prompts, chain these steps, step by step with outputs, connect prompts, feed output into next prompt, automate a workflow, prompt pipeline, chained calls, sequential reasoning, dependent steps, chain of thought at scale, compound prompt. Do NOT activate for: single-turn questions, simple back-and-forth conversation, cases where one prompt is sufficient for the task. First response: "Prompt Chain Builder active. Describe the full workflow
chain
Execute a YAML-defined chain of skill invocations as a single reproducible, audited workflow — with template-driven input piping, schema validation, retry-on-malformed, conditional steps, gates (filesystem / semantic / tool), per-step worktree isolation, and JSONL audit logging. Use when the user says "run the X chain," "execute the X chain on Y," "chain these skills," "run this skill sequence," "run chain," "execute validate-<topic>," or names a chain defined in the workspace `chains/` or `drafts/sample-chains/` directories. Also offer this proactively when the active primary is about to manually run several skills in sequence on the same artifact, where the same sequence is likely to repeat — turning the ad-hoc flow into a chain YAML up-front buys reproducibility, audit trail, and consistent gate enforcement across runs.