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Create Tool

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lploc94
create-tool

Use to create or customize a Raw tool plugin or executable variable provider through files or dashboard. Covers contracts, registration and verification; use create-package to distribute components.

Overview

Publisherlploc94
Repositoryraw-cli
Skill namecreate-tool
Stars
107
Forks
14
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by lploc94 on GitHub. Read the source before you install it.

Installation

Install the Create Tool 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/lploc94/raw-cli/tree/main/examples/skills/create_tool
  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/lploc94/raw-cli.git /tmp/raw-cli
mkdir -p .claude/skills
cp -r /tmp/raw-cli/examples/skills/create_tool .claude/skills/create-tool
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Tool 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 Create Tool 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 Create Tool 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.

Create a Raw tool plugin

Create callable tools or executable variable providers; reusable instructions belong in create_skill. Tools have manifests and ESM handlers; providers are scripts returning configured data.

Read references/packages.md for sharing and references/dashboard.md for browser authoring with read_file.

Choose the task and contract

Establish agent/inputs/output/side effects; diagnose failures from the exact error.

Define inputs/results/limits/failures in English. Preflight does not make I/O transactional: document partial execution and cwd/path semantics. Reuse suitable existing tools.

Layout and schema

  • agent/<id>: tools/<id>/ beside the selected config; portable with that directory.
  • local/<id>: $XDG_CONFIG_HOME/raw/tools/<id>/, otherwise ~/.config/raw/tools/<id>/.
  • builtin/<id>: installed package-owned tools. Fork examples/tools into a user root; rename folder/manifest/name together.

--config changes the agent root, not the global root. Folder/manifest id matches [a-z][a-z0-9_-]*; model-visible name matches [A-Za-z_][A-Za-z0-9_-]{0,63} and must be unique among selected tools. version is a three-component numeric string.

tool.json requires api_version:2, id, version, name, nonempty description, object input_schema, and entry:"./index.mjs". Optional condition_sources, effects_schema and panels declare inspection and UI capabilities. Schema defaults to draft 2020-12; $schema can select draft-07. Remote $ref and $async are unsupported. Use explicit properties, required fields and additionalProperties:false. Manifests/schemas are validated before handler imports; unselected code stays inert. Symlinks cannot escape their root/folder.

index.mjs exports async handler(args, context) and may export synchronous validateArgs(args), returning an error string or undefined. Semantic preflight runs before approval and execution. context.cwd resolves paths; context.signal communicates abort. Return {content:[...], isError?:boolean, code?:string}; content may be {type:"text",text}, {type:"json",value} or a supported typed image. A JSON value must be serializable. Keep imports free of side effects. The handler runs with Raw's OS permissions; cwd and approval rules are not filesystem isolation.

Working example

Prefer one action per call with a flat schema, and put a complete example call in the description: models repeat a simple call more reliably than they fill a batch array. For local/append_notes, create tool.json in the global tools/append_notes/ folder:

json
{
  "api_version": 2,
  "id": "append_notes",
  "version": "1.0.0",
  "name": "append_notes",
  "description": "Append one newline-terminated note to a file, creating the file and its parent directories if needed. Example: {\"path\":\"notes.txt\",\"text\":\"Ship the docs\\n\"}. Relative paths use the session cwd. The result names the file and the bytes appended. To add notes to several files, make several calls.",
  "input_schema": {
    "type": "object",
    "properties": {
      "path": { "type": "string", "minLength": 1, "description": "File path, absolute or relative to the session cwd." },
      "text": { "type": "string", "minLength": 1, "description": "The note, ending with a newline." }
    },
    "required": ["path", "text"],
    "additionalProperties": false
  },
  "entry": "./index.mjs"
}

Create its index.mjs. The semantic validator rejects a note without a final newline before anything is written, and its error shows a valid call. The handler checks abort before writing and returns one text line:

js
import { appendFile, mkdir } from "node:fs/promises";
import { dirname, resolve } from "node:path";

export function validateArgs(args) {
  if (typeof args.path !== "string" || !args.path.trim()) return "path must be a nonempty string";
  if (typeof args.text !== "string" || !args.text.endsWith("\n"))
    return 'text must end with a newline, for example {"path":"notes.txt","text":"Ship the docs\\n"}';
  return undefined;
}

export async function handler(args, context) {
  if (context.signal?.aborted) return { isError: true, code: "aborted", content: [{ type: "text", text: "aborted" }] };
  const path = resolve(context.cwd, args.path);
  await mkdir(dirname(path), { recursive: true });
  await appendFile(path, args.text, "utf8");
  return { isError: false, content: [{ type: "text", text: `Appended ${Buffer.byteLength(args.text)} bytes to ${args.path}` }] };
}

Append the exact ID to agents.<name>.tools.use, preserving existing entries and order. This is an agent fragment, not a complete config:

json
{ "tools": { "use": ["builtin/read_file", "local/append_notes", "builtin/bash"] } }

Use agent/append_notes for a config-adjacent folder.

Implement and verify

  1. Inspect the target agent's selected names and policy. Create the manifest and handler in the chosen root. Validate the whole input before side effects; handle aborts and genuine runtime failures without claiming rollback.
  2. Back up existing config, add the exact selection, and keep config mode 0600. Run raw config list or raw --config PATH config list for static config validation; this alone does not import or execute the plugin.
  3. With the installed library, use loadConfig({configPath,requireModel:false}), then createRuntimeTools({runtime,cwd}). Dispatch the model-visible name through tools.registry.dispatch(name,args,{cwd,maxOutputBytes:8192}); close tools.mcp in finally. This verifies registration/schema/preflight without inference.
  4. For this example, first send a note without a final newline; confirm the validation error and that no file was created. Then send {"path":"notes.txt","text":"first\n"} and verify the file contents and the result Appended 6 bytes to notes.txt. Repeat to check append behavior, reject unknown properties, and test abort when relevant. Host output caps still apply.
  5. Add tools.rules only for requested policy. Match the canonical ID, e.g. {"match":"local/append_notes","effect":"ask"}. Last matching rule wins; unmatched calls run, and -y does not bypass explicit ask. Only ask accepts a when predicate on a schema-bound string path. Verify a requested rule with harmless inputs.

Check failed loads for ID/entry/exports/schema/containment. Report partial runtime effects. Selected source/schema edits rotate generated cache keys on resume; unselected edits do not. Report paths/registration/checks.

Variable consumers and providers

For a configured data value, write an executable provider instead of a tool manifest. Save host.mjs beside the config:

js
import { hostname } from "node:os";
let input = "";
for await (const part of process.stdin) input += part;
const request = JSON.parse(input);
if (request.protocol_version !== 1) throw new Error("unsupported protocol");
process.stdout.write(JSON.stringify({value:hostname()}) + "\n");

Root var_providers.host={command:"node",args:["host.mjs"]} and vars.host={description:"Host name",access:"read",type:"string",source:{kind:"provider",name:"host"}}; append host to agent.vars. Select builtin/list_vars and builtin/read_var for discovery/reads. Test raw --config PATH vars get host. No model call is needed. Provider receives {protocol_version:1,name,params}; params come from config. Output exactly {value,observed_at?} JSON, exit 0; logs stderr. Raw bounds time/bytes. Paths/cwd default to config directory; configure_raw covers limits/cache/source schema.

Selected handlers receive context.vars: list(), read(name,{signal}), validateEnvRefs(refs), resolveEnv(refs,{signal}). Pass selected scalar values to a child through env, not shell interpolation; code can still print them. Respect context.signal. Unselected names and use-only reads fail. Bash and process start support env_refs; MCP has no implicit vars rewriting.

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 Create Tool AI skill do?

Use to create or customize a Raw tool plugin or executable variable provider through files or dashboard. Covers contracts, registration and verification; use create-package to distribute components.

Why use Create Tool on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lploc94/raw-cli/tree/main/examples/skills/create_tool. 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 Create Tool?

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 Create Tool?

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

Is the Create Tool AI skill free?

Yes. It is published on GitHub by lploc94 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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