/chain
Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.
$ npx -y skills add sharpdeveye/maestro --skill chain --agent claude-codeHow 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.mdname: 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
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
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.
Repo: sharpdeveye/maestro
Other skills on maestro.
- /accelerate
Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
Open skill - /adapt-workflow
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Open skill - /agent-workflow
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Open skill - /amplify
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
Open skill - /calibrate
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
Open skill - /capture
Capture a session summary — what was done, what decisions were made, and what to do next.
Open skill

