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Agent Init

Community
szsip239
agent-init

Initialize and configure OpenClaw agent workspace MD files (AGENTS.md, SOUL.md, IDENTITY.md, USER.md, TOOLS.md, BOOTSTRAP.md, HEARTBEAT.md). Use when: setting up a new agent, customizing agent personality/behavior, configuring agent workspace for TeamClaw, or checking/fixing agent environment (Python/uv). Provides interactive interview workflow before generating files. Integrates with TeamClaw session file system (input/output folders) and supports both container and external instances.

Overview

Publisherszsip239
Repositoryteamclaw
Skill nameagent-init
Stars
112
Forks
17
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by szsip239 on GitHub. Read the source before you install it.

Installation

Install the Agent Init 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/szsip239/teamclaw/tree/main/data/skills/agent-init
  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/szsip239/teamclaw.git /tmp/teamclaw
mkdir -p .claude/skills
cp -r /tmp/teamclaw/data/skills/agent-init .claude/skills/agent-init
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Init 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 Agent Init 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 Agent Init 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.

Agent Init

Initialize OpenClaw agent workspace with tailored MD files through an interactive interview.

Workflow

Phase 1: Interview (MANDATORY — do not skip)

Before generating ANY files, gather context through conversation. Ask in batches of 2-3 questions:

Batch 1 — Identity & Purpose:

  • What is this agent's primary purpose? (e.g., coding assistant, research, DevOps, personal assistant)
  • What name and emoji? Any personality traits?

Batch 2 — User Profile:

  • Who will use this agent? (name, timezone, preferences)
  • Communication style preference? (formal/casual, verbose/terse, language)

Batch 3 — Environment:

  • Is this a container instance or external instance?
  • What tools/languages does the agent need? (Python, Node, etc.)
  • Any specific workflows or periodic tasks?

Batch 4 — Boundaries:

  • Any topics or actions the agent should avoid?
  • Privacy requirements beyond defaults?

Only proceed to Phase 2 after user confirms the interview is complete.

Phase 2: Environment Check

Run scripts/check-env.sh to detect Python/uv status:

bash
bash <skill-path>/scripts/check-env.sh

If uv is missing and user wants Python support:

bash
bash <skill-path>/scripts/check-env.sh --install

For container instances, run inside the container:

bash
docker exec <containerId> bash -c "which uv && uv --version || echo 'uv: NOT FOUND'"

Phase 3: Generate Files

Generate files in this order, showing each to user for confirmation before writing:

  1. IDENTITY.md — Fill in fields from interview
  2. USER.md — Fill in user profile
  3. SOUL.md — Rewrite content, keep 4-section structure (Core Truths / Boundaries / Vibe / Continuity)
  4. TOOLS.md — Add environment info, Python/uv config, documentation access notes
  5. AGENTS.md — Extend default template with domain-specific sections (see strategy below)
  6. HEARTBEAT.md — Add periodic tasks if any
  7. BOOTSTRAP.md — Skip unless user wants first-run ritual
AGENTS.md Strategy: Extend, Don't Replace

Read the current AGENTS.md first. The default template contains critical infrastructure:

  • Session startup sequence (file loading order)
  • Memory system (daily + MEMORY.md)
  • Safety rules
  • Heartbeat logic

Add new sections; never remove existing ones. Safe insertion points:

  • After "Every Session" → domain-specific startup tasks
  • After "Safety" → TeamClaw security rules + file exchange rules
  • After "Tools" → Python/uv preferences, documentation access
  • Before "Make It Yours" → project-specific workflows
Mandatory AGENTS.md Additions

Always add these to AGENTS.md regardless of agent type:

markdown
## File Exchange (TeamClaw)
- User uploads appear in `current-session/input/`
- Save output files to `current-session/output/`
- Check input folder at session start for uploaded files
- ALWAYS use `current-session/` symlink — NEVER navigate sessions/ by folder name
  (folders use TeamClaw DB IDs which differ from your OpenClaw session UUID)

## Security (TeamClaw)
- NEVER delete files you didn't create — use `trash` over `rm`
- NEVER display API keys, tokens, passwords, or credentials
- NEVER read other users' session directories
- NEVER use `web_fetch` tool (DNS issues with proxy) — use browser tools instead
- For OpenClaw docs: `qmd query "<topic>"` or `qmd get "openclaw-docs/<path>"`
Mandatory TOOLS.md Additions
markdown
## Python
- Package manager: `uv` (NEVER use pip directly)
- Create venv: `uv venv .venv`
- Install: `uv pip install <package>`
- Run: `uv run python script.py`
- If uv missing: `curl -LsSf https://astral.sh/uv/install.sh | sh`

## Documentation
- OpenClaw docs: `qmd query "<topic>"` or `qmd get "openclaw-docs/<path>"`
- Web browsing: use browser tools only (no web_fetch)

Phase 4: Write Files

Determine write method based on instance type:

External instance (workspacePath set):

bash
# Workspace files live at {workspacePath}/workspace/ (or workspace-{profile}/)
cat > {workspacePath}/workspace/SOUL.md << 'ENDOFFILE'
[content]
ENDOFFILE

Container instance (containerId set):

bash
docker exec -i <containerId> sh -c 'cat > /home/node/.openclaw/workspace/SOUL.md' << 'ENDOFFILE'
[content]
ENDOFFILE

For non-main agents (agentId ≠ "main"):

  • Check if agent has a dedicated workspace via config.get
  • Agent workspace might be at workspace-{agentId}/ or configured separately

Phase 5: Verify

After writing, confirm files are in place:

bash
# External
ls -la {workspacePath}/workspace/*.md

# Container
docker exec <containerId> ls -la /home/node/.openclaw/workspace/*.md

Reference Files

  • references/templates.md — Official templates, loading order, per-file strategy, section structure
  • references/teamclaw-integration.md — Instance types, session files, security rules, Python env, gateway notes

Read these when you need detailed guidance on template structure or TeamClaw-specific configuration.

Rules

  1. Interview first — never generate files without understanding the user's intent
  2. Extend, don't replace — AGENTS.md default template is infrastructure, not boilerplate
  3. Show before write — display each generated file for user confirmation
  4. No web_fetch — DNS resolution fails under proxy; use browser tools or qmd
  5. uv over pip — always configure uv as the Python package manager
  6. No secrets in files — workspace files are injected into every prompt turn
  7. Session isolation — agent must only access its own session's input/output
  8. Keep files concise — all workspace files consume tokens every turn (20KB/file limit, 150KB total)
  9. qmd for docs — use qmd query / qmd get for OpenClaw documentation, not web fetch
  10. Respect existing content — read before write, merge non-destructively

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 Agent Init AI skill do?

Initialize and configure OpenClaw agent workspace MD files (AGENTS.md, SOUL.md, IDENTITY.md, USER.md, TOOLS.md, BOOTSTRAP.md, HEARTBEAT.md). Use when: setting up a new agent, customizing agent personality/behavior, configuring agent workspace for TeamClaw, or checking/fixing agent environment (Python/uv). Provides interactive interview workflow before generating files. Integrates with TeamClaw session file system (input/output folders) and supports both container and external instances.

Why use Agent Init on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/szsip239/teamclaw/tree/main/data/skills/agent-init. 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 Agent Init?

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 Agent Init?

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

Is the Agent Init AI skill free?

Yes. It is published on GitHub by szsip239 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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