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Onboard

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poshan0126
onboard

Get oriented in an unfamiliar codebase fast. An Explore-subagent-driven tour that goes from a one-paragraph overview down to architecture, data models, auth, conventions, and a glossary — without flooding your main context. Use on a repo you're new to.

Overview

Publisherposhan0126
Repositorydotclaude
Skill nameonboard
Stars
870
Forks
204
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 poshan0126 on GitHub. Read the source before you install it.

Installation

Install the 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.
    https://github.com/poshan0126/dotclaude/tree/main/skills/onboard
  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/poshan0126/dotclaude.git /tmp/dotclaude
mkdir -p .claude/skills
cp -r /tmp/dotclaude/skills/onboard .claude/skills/onboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Build a mental model of a codebase you're new to — broad first, then narrowing — the way the Claude Code onboarding recipe prescribes. Read only; never modify anything. Delegate the heavy reading to Explore subagents so their file dumps stay out of this conversation and only the summaries return.

Orientation (injected — already in front of you)

Top level:

ls -1 "${CLAUDE_PROJECT_DIR:-.}" 2>/dev/null | head -40

Manifests / entry points present at the root:

ls -1 "${CLAUDE_PROJECT_DIR:-.}" 2>/dev/null | grep -iE '^(package\.json|pyproject\.toml|go\.mod|cargo\.toml|gemfile|composer\.json|pom\.xml|build\.gradle|makefile|dockerfile|readme)' || echo "(no standard manifest at root — this may be a subdir or an unusual layout)"

Step 1: Dispatch the tour

Launch Explore subagents (in parallel where independent) to answer these, starting broad and narrowing. If $ARGUMENTS names a focus area, weight every prompt toward it.

  1. Overview — what this project is, who uses it, the top-level layout and what each major directory holds. Point the agent at the README and manifests first.
  2. Architecture — the main patterns, the entry points, how one request (or CLI invocation) flows end to end, and the module boundaries (what must not depend on what).
  3. Data models — the core domain types/entities/tables and their relationships, and where they're defined.
  4. Auth & security — how authentication and authorization work, where secrets/config load from, and the trust boundaries.
  5. Conventions — naming, file layout, test style and framework, error-handling and logging patterns, and the real build/test/lint commands from the manifests.

Each Explore prompt must ask for a tight summary with file:path pointers, NOT pasted file contents.

Step 2: Synthesize the brief

Merge the returns into one scannable onboarding brief — nothing pasted verbatim, everything a pointer:

# Onboarding: <project>

**What it is**: <2-3 sentences>
**Stack**: <languages, frameworks, key libraries>
**Architecture**: <the 3-5 load-bearing facts + entry points, as file:path>
**Data model**: <core entities and where they live>
**Auth & boundaries**: <how auth works, where secrets load>
**Conventions**: <naming, tests, error handling — the house style>
**Run it**: <build / test / lint commands>
**Glossary**: <project-specific terms an outsider wouldn't know>
**Start here**: <the 2-3 files to read first for the focus area>

If the repo is empty or trivial (no source beyond config), say so plainly instead of padding the brief.

Rules

  • Strictly read-only. This skill maps the code; it never changes it.
  • Summaries and file:line pointers only — never paste large files or diffs into the brief.
  • Prefer several focused Explore subagents over reading everything in the main context; keeping exploration out of this conversation is the whole point.
  • Don't invent structure you didn't verify. If an area (e.g. auth) doesn't exist, say "none found" — don't fabricate one.

Frequently asked questions

What does the Onboard AI skill do?

Get oriented in an unfamiliar codebase fast. An Explore-subagent-driven tour that goes from a one-paragraph overview down to architecture, data models, auth, conventions, and a glossary — without flooding your main context. Use on a repo you're new to.

Why use Onboard on TypingMind?

Because you install it once and use it with any model. 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 Onboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/poshan0126/dotclaude/tree/main/skills/onboard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Onboard?

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

Is the Onboard AI skill free?

Yes. It is published on GitHub by poshan0126 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.

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