Borrowing it
Nothing to install: this file belongs to lxyer/multi-agent-collaboration-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lxyer/multi-agent-collaboration-system/main/.claude/commands/trellis/onboard.mdgit clone --depth 1 https://github.com/lxyer/multi-agent-collaboration-systemWrote 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/commands/lxyer/multi-agent-collaboration-system/onboard)<a href="https://agentmods.dev/commands/lxyer/multi-agent-collaboration-system/onboard"><img src="https://agentmods.dev/badge/commands/lxyer/multi-agent-collaboration-system/onboard.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 5.1 | $0.00000 | $0.03369 |
| Opus 5 | $0.00000 | $0.01684 |
| Sonnet 5 | $0.00000 | $0.00674 |
| Haiku 4.5 | $0.00000 | $0.00337 |
Grade A, and why
onboard 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 6d 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.
This is a copy
86% identical to onboard — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 359 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior developer onboarding a new team member to this project's AI-assisted workflow system.
YOUR ROLE: Be a mentor and teacher. Don't just list steps - EXPLAIN the underlying principles, why each command exists, what problem it solves at a fundamental level.
CRITICAL INSTRUCTION - YOU MUST COMPLETE ALL SECTIONS
This onboarding has THREE equally important parts:
PART 1: Core Concepts (Sections: CORE PHILOSOPHY, SYSTEM STRUCTURE, COMMAND DEEP DIVE)
- Explain WHY this workflow exists
- Explain WHAT each command does and WHY
PART 2: Real-World Examples (Section: REAL-WORLD WORKFLOW EXAMPLES)
- Walk through ALL 5 examples in detail
- For EACH step in EACH example, explain:
- PRINCIPLE: Why this step exists
- WHAT HAPPENS: What the command actually does
- IF SKIPPED: What goes wrong without it
PART 3: Customize Your Development Guidelines (Section: CUSTOMIZE YOUR DEVELOPMENT GUIDELINES)
- Check if project guidelines are still empty templates
- If empty, guide the developer to fill them with project-specific content
- Explain the customization workflow
DO NOT skip any part. All three parts are essential:
- Part 1 teaches the concepts
- Part 2 shows how concepts work in practice
- Part 3 ensures the project has proper guidelines for AI to follow
After completing ALL THREE parts, ask the developer about their first task.
CORE PHILOSOPHY: Why This Workflow Exists
AI-assisted development has three fundamental challenges:
Challenge 1: AI Has No Memory
Every AI session starts with a blank slate. Unlike human engineers who accumulate project knowledge over weeks/months, AI forgets everything when a session ends.
The Problem: Without memory, AI asks the same questions repeatedly, makes the same mistakes, and can't build on previous work.
The Solution: The .trellis/workspace/ system captures what happened in each session - what was done, what was learned, what problems were solved. The /trellis:start command reads this history at session start, giving AI "artificial memory."
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.
- 6d ago First seen · 359 lines · 0 tokens per session scan A cf9591fcddc4
onboard is a command published in the GitHub repository lxyer/multi-agent-collaboration-system (1 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,369 tokens. A static security scan graded it A with 0 findings. It is 86% identical to onboard, differing in 26 lines, and is treated as a copy.
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