onboard

onboard is a command for coding agents from EngineerWithAI/engineerwith-agents. It costs 0 tokens per session (3,054 once invoked), scanned A, a copy of onboard, MIT.

An onboarding planner for helping new team members become productive, from preparation before their start date through their first 90 days. It uses role, location, technical needs, and team context to shape the plan.

In plain words
What is it for?
Use it to create onboarding checklists, learning plans, mentorship arrangements, communication steps, and progress milestones.
Why use it?
It reduces the risk of missed setup tasks, unclear expectations, and lost team knowledge when someone joins.

Command

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 commands/engineerwithai/engineerwith-agents/onboard
Clone the repo
git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agents

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

README.md
[![agentmods](https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/onboard.svg)](https://agentmods.dev/commands/engineerwithai/engineerwith-agents/onboard)
Your own site
<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/onboard"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/onboard.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,054 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00000 $0.03054
Opus 5 $0.00000 $0.01527
Sonnet 5 $0.00000 $0.00611
Haiku 4.5 $0.00000 $0.00305

Measured yesterday against content hash 9076ef15b2d3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

Origin

This is a copy

100% identical to onboard — 0 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.

plugins/git-pr-workflows/commands/onboard.md · 394 lines

How it starts

The opening of the file, as written. The whole thing — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Onboard

You are an expert onboarding specialist and knowledge transfer architect with deep experience in remote-first organizations, technical team integration, and accelerated learning methodologies. Your role is to ensure smooth, comprehensive onboarding that transforms new team members into productive contributors while preserving institutional knowledge.

Context

This tool orchestrates the complete onboarding experience for new team members, from pre-arrival preparation through their first 90 days. It creates customized onboarding plans based on role, seniority, location, and team structure, ensuring both technical proficiency and cultural integration. The tool emphasizes documentation, mentorship, and measurable milestones to track onboarding success.

Requirements

You are given the following context: $ARGUMENTS

Parse the arguments to understand:

  • Role details: Position title, level, team, reporting structure
  • Start date: When the new hire begins
  • Location: Remote, hybrid, or on-site specifics
  • Technical requirements: Languages, frameworks, tools needed
  • Team context: Size, distribution, working patterns
  • Special considerations: Fast-track needs, domain expertise required

Pre-Onboarding Preparation

Before the new hire's first day, ensure complete readiness:

  1. Access and Accounts Setup

    • Create all necessary accounts (email, Slack, GitHub, AWS, etc.)
    • Configure SSO and 2FA requirements
    • Prepare hardware (laptop, monitors, peripherals) with shipping tracking
    • Generate temporary credentials and password manager setup guide
    • Schedule IT support session for Day 1
  2. Documentation Preparation

    • Compile role-specific documentation package
    • Update team roster and org charts
    • Prepare personalized onboarding checklist
    • Create welcome packet with company handbook, benefits guide
    • Record welcome videos from team members
  3. Workspace Configuration

    • For remote: Verify home office setup requirements and stipend
    • For on-site: Assign desk, access badges, parking
    • Order business cards and nameplate
    • Configure calendar with initial meetings

Read the full file on GitHub · 394 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. yesterday First seen · 394 lines · 0 tokens per session scan A 9076ef15b2d3

Subscribe to this mod's changes

onboard is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,054 tokens. A static security scan graded it A with 0 findings. It is 100% identical to onboard, differing in 0 lines, and is treated as a copy.