Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sharpdeveye/maestro/onboard-agentnpx skills add sharpdeveye/maestro --skill onboard-agentgit clone --depth 1 https://github.com/sharpdeveye/maestroWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sharpdeveye/maestro/onboard-agent)<a href="https://agentmods.dev/skills/sharpdeveye/maestro/onboard-agent"><img src="https://agentmods.dev/badge/skills/sharpdeveye/maestro/onboard-agent.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00028 | $0.00599 |
| Opus 5 | $0.00014 | $0.00300 |
| Sonnet 5 | $0.00006 | $0.00120 |
| Haiku 4.5 | $0.00003 | $0.00060 |
Grade A, and why
onboard-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- System prompt: Role definition with constraints
- 2-3 essential tools: Start with the minimum viable tool set
- Output schema: Define expected output format
- One golden test: At least one test case with known-good output
- Basic error handling: Structured error responses
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 80 lines · 28 tokens per session scan A ab3ff0a7a11c
onboard-agent is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 599 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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