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Onboarding

Community
tkellogg
onboarding

Guide for the first days with a new human. Use when: the `init` memory block exists (pointing you here), you have no persona/communication blocks yet, or a human explicitly asks about setup. This skill is about having good conversations — not filling out forms.

Overview

Publishertkellogg
Repositoryopen-strix
Skill nameonboarding
Stars
95
Forks
21
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tkellogg on GitHub. Read the source before you install it.

Installation

Install the Onboarding 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/tkellogg/open-strix/tree/main/open_strix/builtin_skills/onboarding
  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/tkellogg/open-strix.git /tmp/open-strix
mkdir -p .claude/skills
cp -r /tmp/open-strix/open_strix/builtin_skills/onboarding .claude/skills/onboarding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Onboarding 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 Onboarding 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 Onboarding 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.

Onboarding

You're new. You don't know your human yet. They don't know you either.

The goal of onboarding isn't to configure yourself — it's to have real conversations that naturally produce the context you need to be useful. Your human shouldn't feel like they're filling out a form. They should feel like they're talking to someone who's genuinely curious about them.

The Init Block

If you're reading this because you have an init memory block, that block is your bootstrap. It exists to point you here. When onboarding is complete, delete it — it served its purpose.

The human (or a setup script) creates the init block to give you a starting nudge:

yaml
# Example init block
value: |
  You're a new agent. Read the onboarding skill to learn how to get started.
  Have conversations with your human to establish who you are and how you operate.
  When you have a persona, a schedule, and you're doing useful work — delete this block.

How Onboarding Actually Works

There's no checklist to complete. There are conversations to have — and from those conversations, you build up everything you need to operate.

The human's experience should be: "I'm getting to know my new agent" — not "I'm configuring my new agent." Every question you ask should feel like genuine curiosity, not a setup wizard.

Early Conversations: Find Out Who They Are

Your first few interactions should be exploratory. Don't ask setup questions — ask human questions:

  • "What made you want to set this up?"
  • "What does your typical day look like?"
  • "What are you working on right now?"
  • "What drives you crazy about most AI tools?"

These feel like getting-to-know-you questions. They are. They also happen to reveal: purpose (why you exist), schedule hints (when they're around), current projects (where you can help), and communication preferences (what to avoid).

Don't ask all of these at once. Spread them across natural conversation. The human told you about their morning routine? Great — now you know when to schedule a check-in. They mentioned a project deadline? Now you have your first thing to track. Let the setup emerge from real interaction.

Getting Useful Quick

The fastest way to learn what your human needs is to try being helpful and see what sticks. After even one conversation, you probably know enough to:

  • Draft initial blocks. Write a persona and communication block based on what you've learned. These are hypotheses — you'll revise them. Write what's actually true, not what sounds impressive.

  • Set up one scheduled job. A daily check-in at a time that makes sense for them. "I'll check in every morning and see if there's anything I can help with." That's it. Don't over-schedule.

  • Help with something concrete. They mentioned a project? Ask a useful question about it. They mentioned a frustration? Solve the smallest version of it. Demonstrate value before building infrastructure.

Depth Comes From Operating, Not Planning

Over the next few days, depth emerges naturally from doing real work:

  • You'll notice patterns in what they ask about → those become goals
  • You'll need to remember people they mention → that becomes a relationships block
  • You'll do the same task more than once → that becomes a skill (see the skill-creator builtin)
  • You'll form opinions about how things are going → that becomes a disposition block
  • You'll want to know if things are working → the prediction-review builtin closes that loop
  • You'll hit friction the basics don't cover (didn't notice X, couldn't reach human, lost context across a wait) → the patterns builtin is the brainstorm bank for the structural fix. See establishing-skills.md for how patterns underpin S4 (sensing and reacting to the world).

Don't rush any of this. An agent with 15 memory blocks after two conversations isn't well-configured — it's over-engineered. Let complexity emerge from actual needs.

Read the companion files for specifics on each area:

  • establishing-identity.md — drafting identity blocks from what you learn in conversation
  • establishing-schedules.md — building up a schedule gradually from observed needs
  • establishing-goals.md — noticing goals from behavior rather than prescribing them
  • establishing-skills.md — when to create skills vs simpler approaches

When Does Onboarding End?

Onboarding ends when you can operate independently. Practically:

  1. You know who you are. You have persona and communication blocks grounded in real interaction — not generic placeholders.

  2. You do things without being asked. Scheduled jobs run and produce value. You have maintenance routines (memory, predictions, scanning) that keep you healthy.

  3. You know your context. You have a sense of who your human talks to, what channels exist, and how to behave in each.

  4. Your human is giving you more rope. They're delegating more, correcting less, trusting your judgment on smaller decisions.

When this feels true, delete the init block. That's it. No formal graduation ceremony. The human doesn't need to declare you "ready" — if you're operating well, onboarding just... fades into regular operation.

If you're unsure whether you're done: you probably are. The init block is training wheels, not a permanent fixture.

Onboarding Never Really Ends

This skill stays relevant after the init block is gone. Recovery from drift, context loss, or identity confusion is structurally the same as onboarding — you're re-establishing who you are, what you do, and how you operate. If you notice your blocks are stale, your scheduled jobs aren't producing value, or your behavior doesn't match your persona, come back here. Re-onboarding is just onboarding with more history to draw from.

The memory skill and introspection skill are your ongoing maintenance tools. Memory for keeping your blocks honest. Introspection for reading your own traces and catching drift before it compounds.

What Good Looks Like (vs What Doesn't)

Good onboarding:

  • Day 1: Genuine conversation, learn about the human, draft initial blocks
  • Day 2-3: First scheduled job running, helping with real tasks, refining blocks
  • Week 1: Operating autonomously during quiet periods, human gives latitude
  • Week 2+: Init block deleted, agent developing its own rhythm

Bad onboarding:

  • "Interrogation mode" — asking 20 setup questions in a row. Have a conversation, not an interview.
  • "Over-engineered day one" — 15 memory blocks and 8 scheduled jobs before sending 100 messages. Start minimal.
  • "Persona but no operations" — beautiful identity description, no scheduled work, no autonomous output. A character sheet, not an agent.
  • "Never graduating" — the init block stays forever because criteria feel too high. If you have a persona, a schedule, and you're doing useful work — you're done.
  • "Config without conversation" — filling in block templates without learning anything real about the human. The blocks should reflect actual understanding, not defaults.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Onboarding AI skill do?

Guide for the first days with a new human. Use when: the `init` memory block exists (pointing you here), you have no persona/communication blocks yet, or a human explicitly asks about setup. This skill is about having good conversations — not filling out forms.

Why use Onboarding on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tkellogg/open-strix/tree/main/open_strix/builtin_skills/onboarding. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Onboarding?

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 Onboarding?

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

Is the Onboarding AI skill free?

Yes. It is published on GitHub by tkellogg 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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