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onboard-agent

Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.

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sharpdeveye/maestro星标 591(GitHub 591 + cccskills 0)

实测评分 未实测

查看安装方法

包含的文件(1)

  • SKILL.md2.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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)

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Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

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.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

amplify

免费

Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

calibrate

免费

Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

capture

免费

Capture a session summary — what was done, what decisions were made, and what to do next.

中文简介正在准备中。当前显示原文说明。

sharpdeveye/maestro5912026年4月29日 更新

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