onboard-agent

onboard-agent is a skill for Claude Code, Codex from sharpdeveye/maestro. It costs 28 tokens per session (599 once invoked), scanned A, original, MIT.

A setup guide for creating a new coding-agent workflow or adding an agent to an existing system.

In plain words
What is it for?
Use it when starting a project, onboarding an agent, or creating workflow infrastructure from scratch.
Why use it?
It helps establish shared conventions before building prompts, tools, configuration, logging, and tests.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/sharpdeveye/maestro/onboard-agent
Any agent
npx skills add sharpdeveye/maestro --skill onboard-agent
Clone the repo
git clone --depth 1 https://github.com/sharpdeveye/maestro

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for onboard-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/sharpdeveye/maestro/onboard-agent.svg)](https://agentmods.dev/skills/sharpdeveye/maestro/onboard-agent)
Your own site
<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>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 599 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash ab3ff0a7a11c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

source/skills/onboard-agent/SKILL.md · 80 lines

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

  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.

Read the full file on GitHub · 80 lines

Changes

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.

  1. 3d ago First seen · 80 lines · 28 tokens per session scan A ab3ff0a7a11c

Subscribe to this mod's changes

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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