Skill Management logo

Skill Management

OrganizationPopular
vellum-ai
skill-management

Create, edit, and delete custom managed skills in the user's workspace. Use whenever the user wants to author a new skill from a description, scaffold a SKILL.md, or remove a skill they no longer need.

Overview

Publishervellum-ai
Repositoryvellum-assistant
Skill nameskill-management
Stars
1.3K
Forks
186
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by vellum-ai on GitHub. Read the source before you install it.

Installation

Install the Skill Management 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/vellum-ai/vellum-assistant.git /tmp/vellum-assistant
mkdir -p .claude/skills
cp -r /tmp/vellum-assistant/assistant/src/config/bundled-skills/skill-management .claude/skills/skill-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Management 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 Skill Management 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 Skill Management 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.

Manage the lifecycle of custom managed skills in {workspaceDir}/skills.

When to Use

USE THIS SKILL WHEN:

  • The user says "build me a skill" or "create a skill for X"
  • The user wants to scaffold, edit, or delete a SKILL.md in their workspace
  • The user wants a repeatable workflow captured as an invocable skill

Do NOT use this skill when the user just wants to run an existing skill. That is normal activation, not management.

Capabilities

  • Scaffold a new managed skill with YAML frontmatter and markdown body
  • Edit an existing skill by scaffolding over it (replaces the body and needs activation_hints restated; every other frontmatter field you leave out keeps its current value, and an empty value clears one)
  • Delete an existing managed skill directory

Skills created via scaffold_managed_skill become available for skill_load when a valid top-level SKILL.md is written under the skill directory.

Step 1 - Align with the user before building

Ask before doing anything. Do not scaffold a skill until you have confirmed with the user:

  • What the skill should do
  • When it should activate (the trigger phrases, in their words)
  • The major steps it performs
  • Any destructive steps and the done condition

✓ Checkpoint: Have you confirmed scope with the user? If you are guessing at any of the four points above, ask first. Do not scaffold on assumption.

Step 2 - Write a description AND activation-hints, always both

The description is what makes the skill discoverable. It must cover both what the skill does and when to reach for it, phrased the way the user would say it.

yaml
description: Build anything visual — apps, landing pages, dashboards, trackers,
  calculators, games, tools, slide decks, or data visualizations. Use whenever
  the user wants something built that they can see and interact with.

Every skill must also ship activation-hints in its frontmatter. This is not optional. Keep activation-hints separate from the description: the description sells the skill, the hints list the concrete trigger phrases the user confirmed in Step 1.

yaml
metadata:
  vellum:
    activation-hints:
      - "build me an app"
      - "make a dashboard"
      - "create a landing page"

✓ Checkpoint: Does the frontmatter have both a description and an activation-hints list? If hints are missing, go back and add them before writing the body.

Step 3 - Structure the body so it survives weak models

Strong models tolerate loose structure. Weaker models drift. Build every body with these patterns.

Open with a ## When to Use block. User language, not jargon. This is what makes the model recognize when the skill applies.

Put critical warnings at the point of action. A warning at the top of a file is forgotten by the time the model is 200 lines deep. Do not trust the top-of-file warning. Repeat the danger where the dangerous action happens.

markdown
## Step 5 - Apply the JSON blob

⚠️ CRITICAL: Use the complete blob below. Setting even one key
wipes the entire block. Copy the whole thing or fail.

Add explicit checkpoints between major steps, sparingly. Long executions blur together. The model finishes step 3 and slides into step 4 without re-anchoring. A checkpoint forces a re-read. Use them between major sections, not on every step.

Make branching explicit with If / →, and always name the default. Prose hides decisions. The model reads linearly and walks past a branch without registering it. Every If must cover the default case. Implicit fall-through ("otherwise figure it out") creates drift.

markdown
If the user already has a draft → restructure it into the template.
If not → build the steps from their description (default).

Step 4 - Define done by binding tool calls to artifacts

Without an explicit done condition, the model invents one. It stops too early ("the file was created, done") or overshoots ("let me add one more feature"). Both are drift.

Each completion criterion must bind a tool call to the user-visible artifact it produces. Do not write criteria the model can satisfy by narration alone.

markdown
## SKILL COMPLETE WHEN

- [ ] `scaffold_managed_skill` wrote the SKILL.md and returned its path
- [ ] User confirmed the skill loads via `skill_load`
- [ ] User saw the trigger phrases that will activate it

✓ Checkpoint: Before scaffolding, confirm the body has a ## When to Use block, point-of-action warnings on any dangerous step, explicit If / → branches with named defaults, and artifact-bound completion criteria.

Step 5 - Keep SKILL.md under 500 lines

Past 500 lines the model loses things in the middle. Warnings get buried, branching loses visibility, and the file fights the task for the same context budget. If a skill is growing past 500 lines, split reference material into separate files the skill points to.

Companion files ship through scaffold_managed_skill's files input and live inside the skill folder:

  • references/*.md for failure modes, gotchas, and cached values the body should point to.
  • scripts/* for reusable code the procedure runs. Store the exact version that already ran successfully: pass copy_from with the tested file's absolute path instead of pasting its contents into content, so the bytes that shipped are the bytes that ran. Have the new skill's body invoke it through the baseDir placeholder (the word baseDir in curly braces), which resolves to that skill's folder when it loads. The terminal does not run from the skill folder, so a bare scripts/... path would fail. (The placeholder is spelled out here rather than written literally because this very body undergoes the same substitution.)

Step 6 - Test the skill before calling it done

After scaffolding, load the skill and confirm it activates on the intended trigger and follows its own steps. If it does not activate or drifts, fix the body and test again.

How you exercise it depends on what the skill does:

  • If the skill performs side effects (sends messages, deletes data, makes purchases, mutates external state) → do not trigger a live run during creation. Confirm it loads and activates on the intended trigger (static check), then ask the user before exercising it for real.
  • Otherwise (read-only or local-only skills) → run it against a realistic prompt directly. This is the default.

⚠️ CRITICAL: Do not tell the user a skill is ready until you have confirmed it loads and activates on the intended trigger. A skill that was never loaded is a skill that was never tested. Never perform user-visible side effects just to test a skill without the user's consent.

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 Skill Management AI skill do?

Create, edit, and delete custom managed skills in the user's workspace. Use whenever the user wants to author a new skill from a description, scaffold a SKILL.md, or remove a skill they no longer need.

Why use Skill Management on TypingMind?

Because you install it once and use it with any model. Skill Management 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 Skill Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vellum-ai/vellum-assistant/tree/main/assistant/src/config/bundled-skills/skill-management. 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 Skill Management?

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 Skill Management?

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

Is the Skill Management AI skill free?

Yes. It is published on GitHub by vellum-ai 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 👇