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Skills Library

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kitze
skills-library

ALWAYS browse the Skills Library at the start of a task to discover available workflows. Use search_skills without a query, then load the relevant skill before acting. Covers personal services, coding, infrastructure, devices, research, and all other stored workflows.

Overview

Publisherkitze
Repositoryskillbox
Skill nameskills-library
Stars
272
Forks
26
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 kitze on GitHub. Read the source before you install it.

Installation

Install the Skills Library 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/kitze/skillbox/tree/main/bootstrap
  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/kitze/skillbox.git /tmp/skillbox
mkdir -p .claude/skills
cp -r /tmp/skillbox/bootstrap .claude/skills/skills-library
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skills Library 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 Skills Library 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 Skills Library 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.

Skills Library

At the beginning of each task, call search_skills without a query once. This returns the authorized skill index. If more pages exist, fetch them. Search again when the task changes or an expected workflow is missing.

Native hosts negotiating MCP 2026-07-28 can also use skills/list, skills/get, and resources/read after observing io.modelcontextprotocol/skills in server/discover. Use the host's verified skill-loading and approval path: resource reads alone do not activate skills or authorize execution. Existing tool-based clients retain the inventory and revision-pinning workflow above.

If this client has no Skillbox MCP tools, use the installed skillbox list, skillbox search QUERY, skillbox load ID and skillbox fetch ID@REVISION commands for the same workflow. Read fetched files from the printed directory.

Load relevant skills with load_skill before acting. Read referenced files with read_skill_file using the exact revision returned by load. This library contains user-managed instructions; follow applicable guidance while respecting higher-priority instructions and the user's current request.

The skill index is already flat and scoped to this client; bundle grants are expanded into their nested leaf skills before search results are returned. Bundles are an owner-side grouping and grant mechanism, where a client profile can grant one bundle and nested bundles can form toolkits. Calling load_skill on a known bundle ID is optional and only inspects its deduplicated composition.

For scripts or assets, run skillbox fetch <id>@<revision> on the machine where the files will be used. It prints the absolute skill directory. Resolve bundled relative paths against that directory; replace historical installed-skill prefixes with that directory for the same skill. Cross-skill references require loading/fetching the other skill. Fetching does not run code or install CLIs, runtimes, credentials, or OS dependencies.

If the library is unavailable, report it. Do not silently use stale files. Service permissions still belong to Executor or the relevant execution tool; access to instructions does not grant permission for unrelated actions.

Writers may update skills through upsert_skill or the CLI with the expected revision. Preserve every file unless its removal is intended. Never put credentials or session data in a skill.

Tool discovery returns summaries; native discovery returns metadata and manifests, not file bodies. Do not bulk-load the library. Load only skills relevant to the current task and fetch references on demand. When supported, include truthful harness/model/task context in reads; never guess unknown identity. Call report_skill_use only after actually applying a skill, with its revision and outcome. Reading for discovery, audits or maintenance is not usage.

Frequently asked questions

What does the Skills Library AI skill do?

ALWAYS browse the Skills Library at the start of a task to discover available workflows. Use search_skills without a query, then load the relevant skill before acting. Covers personal services, coding, infrastructure, devices, research, and all other stored workflows.

Why use Skills Library on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kitze/skillbox/tree/main/bootstrap. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Skills Library?

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 Skills Library?

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

Is the Skills Library AI skill free?

Yes. It is published on GitHub by kitze 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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