Self Customize logo

Self Customize

OrganizationPopular
nanocoai
self-customize

Customize your own agent — add capabilities, install packages, add MCP servers, edit code or CLAUDE.md. Use when the user asks you to add a feature, install a tool, or modify how you work. For non-trivial code changes, delegate to a builder agent via create_agent.

Overview

Publishernanocoai
Repositorynanoclaw
Skill nameself-customize
Stars
30.8K
Forks
12.8K
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 nanocoai on GitHub. Read the source before you install it.

Installation

Install the Self Customize 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/nanocoai/nanoclaw.git /tmp/nanoclaw
mkdir -p .claude/skills
cp -r /tmp/nanoclaw/container/skills/self-customize .claude/skills/self-customize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Self Customize 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 Self Customize 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 Self Customize 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.

Self-Customization

You can modify your own environment. Different kinds of changes have different workflows.

Decision Tree

What needs to change?

  • Memory or standing instructions → Edit memory/ or instructions.prepend.md directly, no approval needed. The workspace is persisted on the host. The composed provider document (CLAUDE.md or AGENTS.md) is regenerated every spawn and must not be edited.
  • System package (apt) or global npm packageinstall_packages. Requires admin approval. On approval, image rebuild + container restart happen automatically.
  • MCP serveradd_mcp_server. Requires admin approval. On approval, container restarts with the new server wired up (no rebuild — bun runs TS directly).
  • Your source code or Dockerfile → Delegate to a builder agent via create_agent (see below).
  • A new specialist capabilitycreate_agent to spin up a dedicated agent for it.

Workflow: Code Changes via Builder Agent

For anything that requires editing source files (your own code, Dockerfile, etc.), do not edit directly — delegate to a builder agent. This gives the user a reviewable boundary and keeps your main session focused.

  1. Describe what you need changed in concrete terms (files, behavior, acceptance criteria)
  2. Call create_agent({ name: "Builder", instructions: "<builder prompt>" }) — the returned agent group ID is your builder
  3. Call send_to_agent({ agentGroupId, text: "<task description with specific files and changes>" })
  4. The builder works in its own container, makes the changes, and reports back
  5. You review the builder's summary and confirm with the user. Source-code edits inside /app/src are picked up automatically on the next container start — no rebuild step needed (bun runs TS directly). If the builder also installed packages, its own install_packages approval will have rebuilt the image.

Builder Agent Instructions (use as CLAUDE.md when creating)

You are a builder agent. Your job is to make precise, minimal code changes to NanoClaw source files when the main agent requests it.

## Rules

- **Minimal scope.** Only change what was requested. Do not refactor surrounding code, "improve" unrelated files, or add features not asked for.
- **Diff size limits.** Reject any change that exceeds 200 new lines or 150 modified lines in a single task. If the change is larger, push back and ask for it to be split into smaller tasks.
- **Read before writing.** Always read the target file fully before editing. Understand the existing patterns.
- **Test if possible.** If there are relevant tests, run them after your change.
- **Report back.** When done, use send_to_agent to tell the requesting agent: (a) what files you changed, (b) a summary of the changes, (c) any follow-up needed (rebuild, tests, migrations).
- **No silent failures.** If you can't complete the task, explain why — don't produce partial work without flagging it.

## Safety

- Never edit files outside the requested scope
- Never commit or push anything
- Never modify secrets, credentials, or .env files
- If a change would break existing tests, stop and report

Diff Size Limits — Why

A 50-line focused change is reviewable. A 500-line sweep is not. Hard limits force the agent to decompose work into reviewable chunks, which:

  • Makes human approval meaningful (you can actually read 150 lines)
  • Catches runaway edits early (if the first task hits the limit, the scope was wrong)
  • Forces clear acceptance criteria per task

The limits are per builder task, not per session. A 500-line feature is fine as 4 sequential builder tasks of ~125 lines each, each with its own scope.

Example: Adding a New MCP Tool to Yourself

User: "Can you add a tool for reading RSS feeds?"

  1. Check mcp.so for an existing RSS MCP server
  2. If one exists → add_mcp_server({ name: "rss", command: "npx", args: ["some-rss-mcp"] }) → admin approves → container restarts with the new server → done
  3. If nothing suitable exists → delegate to a builder agent:
    • create_agent({ name: "RSS Tool Builder", instructions: "<builder prompt from above>" })
    • send_to_agent({ agentGroupId, text: "Add an MCP tool 'read_rss' to container/agent-runner/src/mcp-tools/. It should fetch an RSS URL and return the latest N items. Register it in mcp-tools/index.ts. Target: <200 new lines." })
    • Wait for builder's report — new tool code is picked up on the next container start (bun runs TS directly)

Example: Installing a System Tool

User: "Can you transcribe audio?"

  1. Check what's available — which ffmpeg (likely not installed in base image)
  2. Decide approach: @xenova/transformers (npm, workspace-local) or whisper.cpp (apt + compile)
  3. For persistent system tool: install_packages({ apt: ["ffmpeg"], npm: ["@xenova/transformers"], reason: "Audio transcription for voice messages" })
  4. Wait for admin approval — on approve, the image is rebuilt and your container is restarted automatically
  5. Test the new capability once the container restarts

When NOT to Self-Customize

  • The change is for a one-off task — just do it in your workspace, don't modify the container
  • The request is ambiguous — ask the user what they actually need before spinning up builders or requesting installs
  • You don't know if it will work — prototype in your workspace first (pnpm install in /workspace/agent/), then promote to container-level install if it proves useful

Frequently asked questions

What does the Self Customize AI skill do?

Customize your own agent — add capabilities, install packages, add MCP servers, edit code or CLAUDE.md. Use when the user asks you to add a feature, install a tool, or modify how you work. For non-trivial code changes, delegate to a builder agent via create_agent.

Why use Self Customize on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nanocoai/nanoclaw/tree/main/container/skills/self-customize. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Self Customize?

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 Self Customize?

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

Is the Self Customize AI skill free?

Yes. It is published on GitHub by nanocoai 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 👇