/onboard-agent
Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.
$ npx -y skills add sharpdeveye/maestro --skill onboard-agent --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
/onboard-agent
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.
SKILL.md
onboard-agent.SKILL.mdname: onboard-agent
description: "Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch."
argument-hint: "[project or agent name]"
category: utility
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.
---
Bootstrap a new agent workflow from scratch, or add a new agent to an existing system.
Step 1: Establish Conventions
## Workflow Conventions
### Prompt Format
- Delimiter style: [XML tags / markdown headers / triple-dash]
- Section order: [System → Context → Instructions → Input]
- Output format: [JSON with schema / markdown template]
### Tool Conventions
- Naming: [verb_noun / noun.verb / camelCase]
- Description template: [What → When → When Not → Returns]
- Error format: [{ code, message, details }]
### Logging
- Format: [JSON structured]
- Required fields: [workflow_id, step, timestamp, level]
### File Structure
- Prompts: [prompts/workflow-name/v1.md]
- Tools: [tools/tool-name.{ext}]
- Config: [config/environment.yaml]
- Tests: [tests/workflow-name/]Step 2: Create Initial Structure
project/
├── prompts/ # System prompts, versioned
├── tools/ # Tool definitions
├── config/ # Environment-specific configuration
├── tests/ # Golden test sets and evaluation suites
├── logs/ # Runtime logs (gitignored)
└── .maestro.md # Workflow context
Step 3: Create the First Agent
1. **System prompt**: Role definition with constraints 2. **2-3 essential tools**: Start with the minimum viable tool set 3. **Output schema**: Define expected output format 4. **One golden test**: At least one test case with known-good output 5. **Basic error handling**: Structured error responses 6. **Logging**: Structured log output for each run
Step 4: Verify
- Run the agent with the golden test case
- Verify error handling works (send bad input)
- Verify logging captures useful context
Recommended Next Step
After onboarding, run `/diagnose` for a baseline health check, then `/fortify` to add production-grade error handling.
**NEVER**:
- Start building without establishing conventions
- Create tools without descriptions
- Skip the golden test case
- Over-scope the initial agent (start minimal, amplify later)
Read more
name: onboard-agent description: "Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch." argument-hint: "[project or agent name]" category: utility 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.
---
Bootstrap a new agent workflow from scratch, or add a new agent to an existing system.
Step 1: Establish Conventions
## Workflow Conventions
### Prompt Format
- Delimiter style: [XML tags / markdown headers / triple-dash]
- Section order: [System → Context → Instructions → Input]
- Output format: [JSON with schema / markdown template]
### Tool Conventions
- Naming: [verb_noun / noun.verb / camelCase]
- Description template: [What → When → When Not → Returns]
- Error format: [{ code, message, details }]
### Logging
- Format: [JSON structured]
- Required fields: [workflow_id, step, timestamp, level]
### File Structure
- Prompts: [prompts/workflow-name/v1.md]
- Tools: [tools/tool-name.{ext}]
- Config: [config/environment.yaml]
- Tests: [tests/workflow-name/]Step 2: Create Initial Structure
project/ ├── prompts/ # System prompts, versioned ├── tools/ # Tool definitions ├── config/ # Environment-specific configuration ├── tests/ # Golden test sets and evaluation suites ├── logs/ # Runtime logs (gitignored) └── .maestro.md # Workflow context
Step 3: Create the First Agent
1. **System prompt**: Role definition with constraints 2. **2-3 essential tools**: Start with the minimum viable tool set 3. **Output schema**: Define expected output format 4. **One golden test**: At least one test case with known-good output 5. **Basic error handling**: Structured error responses 6. **Logging**: Structured log output for each run
Step 4: Verify
- Run the agent with the golden test case
- Verify error handling works (send bad input)
- Verify logging captures useful context
Recommended Next Step
After onboarding, run `/diagnose` for a baseline health check, then `/fortify` to add production-grade error handling.
**NEVER**:
- Start building without establishing conventions
- Create tools without descriptions
- Skip the golden test case
- Over-scope the initial agent (start minimal, amplify later)
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

