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Skill

/chain

Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.

From plugin
maestro
41125 skills
Install
$ npx -y skills add sharpdeveye/maestro --skill chain --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/chain

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.

SKILL.md

chain.SKILL.md
name: chain
description: "Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow."
argument-hint: "[pipeline description]"
category: enhancement
version: 2.0.0
user-invocable: true

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the tool-orchestration reference in the agent-workflow skill for composition patterns and error handling.

---

Design tool chains that do complex work reliably. A chain is only as strong as its weakest link.

Chain Patterns

**Sequential**: A → B → C (each step depends on the previous) **Parallel**: [A, B, C] → Merge (independent steps run simultaneously) **Conditional**: A → (if X then B, else C) → D (branching based on results) **Iterative**: A → Check → (if not done) → A again (loop until convergence)

Chain Design Process

For each chain, define:

## Chain: [Name]

### Steps
1. [Tool A] — [what it does] — Input: [schema] — Output: [schema]
2. [Tool B] — [what it does] — Input: [output of step 1] — Output: [schema]
3. [Tool C] — [what it does] — Input: [output of step 2] — Output: [schema]

### Data Flow
Step 1 output.field_a → Step 2 input.source_data
Step 2 output.results → Step 3 input.items

### Error Handling
Step 1 failure → [retry 3x, then return error]
Step 2 failure → [return partial results from step 1]
Step 3 failure → [retry with simplified input]

### Constraints
Max total execution time: 60s
Max retries per step: 3

Chain Validation

  • [ ] Data schemas are compatible between connected steps
  • [ ] Every step has error handling
  • [ ] Total chain timeout is set
  • [ ] Maximum iteration count is set for loops
  • [ ] Partial results are handled (what if step 2 of 4 fails?)

Recommended Next Step

After building the chain, run `/fortify` to add error handling at each step, then `/evaluate` to test the full pipeline.

**NEVER**:

  • Build chains without defining data contracts between steps
  • Create loops without maximum iteration counts
  • Skip error handling at any step (the chain breaks at the weakest link)
  • Assume output of step N is always valid input for step N+1
  • Build long chains when a single prompt could handle the task
Read more
Ships withmaestro

Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and 6 more.

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TypeScript
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MIT
License
3mo ago
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4mo ago
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Repo: sharpdeveye/maestro