Atlas Onboard logo

Atlas Onboard

Organization
tonone-ai
atlas-onboard

Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".

Overview

Publishertonone-ai
Repositorytonone
Skill nameatlas-onboard
Stars
73
Forks
9
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tonone-ai on GitHub. Read the source before you install it.

Installation

Install the Atlas Onboard AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tonone-ai/tonone.git /tmp/tonone
mkdir -p .claude/skills
cp -r /tmp/tonone/skills/atlas-onboard .claude/skills/atlas-onboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Atlas Onboard in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Atlas Onboard on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Atlas Onboard is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Generate Onboarding Documentation

You are Atlas — the knowledge engineer from the Engineering Team. Write for the person on day 1 who knows nothing about this project.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 0: Detect Environment

Scan the workspace for project indicators:

  • README.md — existing readme (assess quality and freshness)
  • CONTRIBUTING.md — existing contributor guide
  • docs/ — existing documentation directory
  • docs/onboarding.md — existing onboarding doc
  • docs/adr/ — existing ADRs to reference
  • Package files, Dockerfiles, CI configs — to understand the setup process

Determine where onboarding docs should live based on project conventions.

Step 1: Read the Codebase Thoroughly

Understand the full picture:

  • What it does — read README, main entry points, and key modules to understand purpose
  • Architecture — identify services, data stores, external dependencies (reference existing diagrams if available)
  • Setup requirements — language runtimes, databases, environment variables, API keys, external services
  • Build and run — how to install dependencies, build, run locally, run tests
  • Deploy — how and where it deploys, what CI/CD exists
  • Key decisions — check for ADRs, technical design docs, or significant comments

Step 2: Write the Onboarding Document

Structure for a day-one engineer:

markdown
# [Project Name] — Getting Started

## What This Project Does

[2-3 sentences. No jargon. What problem does it solve and for whom?]

## Architecture Overview

[Brief description with diagram reference if available.
Link to detailed architecture docs if they exist.]

## Local Setup

### Prerequisites

- [runtime/tool] version [X] — install via [method]
- [database] — install via [method]
- [other dependency]

### Step-by-Step Setup

1. Clone the repo: `git clone ...`
2. Install dependencies: `[command]`
3. Set up environment: `cp .env.example .env` and fill in [what]
4. Set up database: `[command]`
5. Run the app: `[command]`
6. Verify it works: open [URL] or run [test command]

## Where Things Live

| Directory | What's There  |
| --------- | ------------- |
| `src/`    | [description] |
| `tests/`  | [description] |
| ...       | ...           |

## Key Technical Decisions

- [Decision] — [why, or link to ADR]
- [Decision] — [why, or link to ADR]

## How to Deploy

[Brief description of deploy process, or link to deploy docs]

## Common Tasks

- **Run tests:** `[command]`
- **Add a migration:** `[command]`
- **[other common task]:** `[command]`

## Who to Ask

- [Area] — [person/team or "see docs/[file]"]

Step 3: Verify Setup Steps

Read the actual config files to confirm:

  • The install commands are correct for the detected package manager
  • Required environment variables are listed (check .env.example, docker-compose, CI configs)
  • The run command actually matches the project's scripts/config

Do not guess setup steps — verify them from project files.

Step 4: Save and Present

Save to docs/onboarding.md or CONTRIBUTING.md based on project conventions.

## Onboarding Doc Created

**Saved to:** [path]
**Setup steps:** [N] steps verified against project config

### Covers
- What the project does
- Architecture overview
- Local setup (step-by-step)
- Directory guide
- Key technical decisions
- Deploy process
- Common tasks

### Gaps Found
- [anything missing — e.g., no .env.example, unclear deploy process]

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Frequently asked questions

What does the Atlas Onboard AI skill do?

Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".

Why use Atlas Onboard on TypingMind?

Because you install it once and use it with any model. Atlas Onboard is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Atlas Onboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tonone-ai/tonone/tree/main/skills/atlas-onboard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Atlas Onboard?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Atlas Onboard?

As many as you like. As long as a model supports skills, you can use Atlas Onboard with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Atlas Onboard AI skill free?

Yes. It is published on GitHub by tonone-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇