/teach-maestro
Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.
$ npx -y skills add sharpdeveye/maestro --skill teach-maestro --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
/teach-maestro
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.
SKILL.md
teach-maestro.SKILL.mdname: teach-maestro
description: "Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project."
argument-hint: "[project name]"
category: utility
version: 2.0.0
user-invocable: true
BOOTSTRAP — First Command to Run
This is the entry point for Maestro. It creates the `.maestro.md` context file that all other Maestro commands depend on. No other preparation is needed — this IS the preparation.
---
You are conducting a structured interview to understand this project's AI workflow setup. Be conversational but thorough.
Interview Questions
Ask these questions one section at a time. Wait for answers before proceeding.
**Section 1 — Models & Providers**
- What AI model(s) are you using? (e.g., GPT-4, Claude, Gemini, local models)
- Are you using APIs directly or through a framework? (e.g., LangChain, LlamaIndex, custom)
- What are your context window sizes?
**Section 2 — Workflow Architecture**
- Describe your current workflow at a high level (what goes in, what comes out)
- Do you have multiple agents/steps, or is it a single-agent system?
- What tools/functions are available to your agent(s)?
**Section 3 — Quality & Evaluation**
- How do you currently evaluate output quality?
- Do you have test cases or golden examples?
- What are the most common failure modes?
**Section 4 — Constraints**
- What are your cost constraints? (budget per request, per day)
- What are your latency requirements? (real-time, batch, async)
- Are there compliance requirements? (HIPAA, GDPR, SOC2, etc.)
**Section 5 — Priorities**
- Rank these from most to least important: Quality, Speed, Cost, Safety
- What's the single biggest workflow problem you want to solve?
Output Format
After gathering all answers, generate a `.maestro.md` file:
# Maestro Workflow Context
Generated: [date]
## Models & Providers
[answers from section 1]
## Workflow Architecture
[answers from section 2]
## Quality & Evaluation
[answers from section 3]
## Constraints
[answers from section 4]
## Priorities
[answers from section 5, with ranked priorities]
Save this file to the project root as `.maestro.md`.
Context Completeness
| Section | Status | Impact if Missing | |---------|--------|-------------------| | Models & Providers | ? | Commands can't tailor advice to your stack | | Workflow Architecture | ? | Commands can't assess complexity | | Quality & Evaluation | ? | `/iterate` and `/evaluate` less effective | | Constraints | ? | `/guard` and `/accelerate` can't set limits | | Priorities | ? | All commands default to generic guidance |
Interview Checklist
- [ ] All 5 sections asked and answered
- [ ] Answers are specific (not vague)
- [ ] `.maestro.md` file generated and saved
- [ ] User confirmed accuracy of the generated context
Recommended Next Step
After creating `.maestro.md`, run `/diagnose` for a baseline health check of your workflow.
**NEVER**:
- Skip questions — every section matters for downstream commands
- Make assumptions — ask if unclear
- Overwrite an existing `.maestro.md` without asking
Read more
name: teach-maestro description: "Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project." argument-hint: "[project name]" category: utility version: 2.0.0 user-invocable: true
BOOTSTRAP — First Command to Run
This is the entry point for Maestro. It creates the `.maestro.md` context file that all other Maestro commands depend on. No other preparation is needed — this IS the preparation.
---
You are conducting a structured interview to understand this project's AI workflow setup. Be conversational but thorough.
Interview Questions
Ask these questions one section at a time. Wait for answers before proceeding.
**Section 1 — Models & Providers**
- What AI model(s) are you using? (e.g., GPT-4, Claude, Gemini, local models)
- Are you using APIs directly or through a framework? (e.g., LangChain, LlamaIndex, custom)
- What are your context window sizes?
**Section 2 — Workflow Architecture**
- Describe your current workflow at a high level (what goes in, what comes out)
- Do you have multiple agents/steps, or is it a single-agent system?
- What tools/functions are available to your agent(s)?
**Section 3 — Quality & Evaluation**
- How do you currently evaluate output quality?
- Do you have test cases or golden examples?
- What are the most common failure modes?
**Section 4 — Constraints**
- What are your cost constraints? (budget per request, per day)
- What are your latency requirements? (real-time, batch, async)
- Are there compliance requirements? (HIPAA, GDPR, SOC2, etc.)
**Section 5 — Priorities**
- Rank these from most to least important: Quality, Speed, Cost, Safety
- What's the single biggest workflow problem you want to solve?
Output Format
After gathering all answers, generate a `.maestro.md` file:
# Maestro Workflow Context Generated: [date] ## Models & Providers [answers from section 1] ## Workflow Architecture [answers from section 2] ## Quality & Evaluation [answers from section 3] ## Constraints [answers from section 4] ## Priorities [answers from section 5, with ranked priorities]
Save this file to the project root as `.maestro.md`.
Context Completeness
| Section | Status | Impact if Missing | |---------|--------|-------------------| | Models & Providers | ? | Commands can't tailor advice to your stack | | Workflow Architecture | ? | Commands can't assess complexity | | Quality & Evaluation | ? | `/iterate` and `/evaluate` less effective | | Constraints | ? | `/guard` and `/accelerate` can't set limits | | Priorities | ? | All commands default to generic guidance |
Interview Checklist
- [ ] All 5 sections asked and answered
- [ ] Answers are specific (not vague)
- [ ] `.maestro.md` file generated and saved
- [ ] User confirmed accuracy of the generated context
Recommended Next Step
After creating `.maestro.md`, run `/diagnose` for a baseline health check of your workflow.
**NEVER**:
- Skip questions — every section matters for downstream commands
- Make assumptions — ask if unclear
- Overwrite an existing `.maestro.md` without asking
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

