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self-customize

Extend a NanoClaw agent's own environment with packages, external tools, or specialist agents. The skill chooses the workflow for each change, edits memory and standing instructions, and delegates source changes to a builder for review.

中文简介正在准备中。当前显示英语简介。

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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.

查看安装方法

适用场景

  • Adding an external tool for reading RSS feeds
  • Installing persistent system or global npm packages
  • Updating memory or standing behavior instructions
  • Assigning source changes to a builder agent
  • Creating an agent for a specialist task

包含的文件(1)

  • SKILL.md5.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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 package → install_packages. Requires admin approval. On approval, image rebuild + container restart happen automatically.
  • MCP server → add_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 capability → create_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

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