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Maplibre Skill Authoring

Organization
maplibre
maplibre-skill-authoring

How to turn a working session into a MapLibre agent skill — you researched something MapLibre's docs did not cover, got it wrong before you got it right, shipped it, and the session is about to end. Covers what to capture while the context is still live, how to separate the durable claim from the project it came from, and where to hand it off. Use at the end of a MapLibre task that took real research, or when asked to capture one as a skill.

Overview

Publishermaplibre
Repositorymaplibre-agent-skills
Skill namemaplibre-skill-authoring
Stars
148
Forks
10
Bundled files
Instructions only
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 maplibre on GitHub. Read the source before you install it.

Installation

Install the Maplibre Skill Authoring 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/maplibre/maplibre-agent-skills.git /tmp/maplibre-agent-skills
mkdir -p .claude/skills
cp -r /tmp/maplibre-agent-skills/skills/maplibre-skill-authoring .claude/skills/maplibre-skill-authoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maplibre Skill Authoring 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 Maplibre Skill Authoring 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 Maplibre Skill Authoring 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.

Authoring a Skill From a Session

The most valuable input to maplibre-agent-skills is a session where an agent got a MapLibre answer wrong, went and found the right one, and shipped working code. That session holds something no later reconstruction can recover: the wrong answer that came first. It is the evidence that a skill is warranted at all, and it exists only until the context ends.

This skill is about that harvest. It does not restate how to contribute — AGENTS.md has the agent-specific rules and CONTRIBUTING.md has the lanes and the four-step order for writing a new skill. Read those for the handoff; read this before the session ends, because after that most of what follows is unrecoverable.

When to Use This Skill

  • A MapLibre task just took real research: docs, source, a CHANGELOG, an issue thread
  • The first approach was wrong, or right for Mapbox GL JS and wrong for MapLibre
  • A version boundary, gotcha, or failure mode turned up that the docs do not state plainly
  • Someone asks to capture the session as a skill

Capture first, decide later

Do this while the session is still open. Four things, in a scratch file:

  1. The wrong first answer, verbatim. What you asserted, wrote, or would have written before you checked. This is the baseline hypothesis, and it is the only artifact here that cannot be reconstructed afterward — once the correct answer is in context, the model will not produce the wrong one again on request. A skill earns its place only where a model fails without it, so this is what makes the eval writable.
  2. The primary sources you actually opened. URLs, not recollections: the style spec page, the GL JS docs page, the PR or issue, the CHANGELOG entry. A citation reconstructed later from memory is a guess wearing a link.
  3. The correct claim, in one or two sentences. Written as a rule, not as a narrative of what you did.
  4. Versions and the minimal reproduction. Which MapLibre version, which SDK (GL JS and Native diverge), and the smallest style or code that shows the behavior.

Separate the claim from the project

Everything above is entangled with the codebase it came from. The test for each line: would this be true in someone else's project?

  • Cut project names, file paths, internal architecture, and business logic. If a code sample only makes sense with your app around it, rewrite it against a bare map.
  • Keep the version boundary. "Broken before X, works after" is judgment; "it works" is not.
  • Keep the reason the wrong answer was plausible. That is what makes the skill catch the next agent, and it is usually a Mapbox GL JS habit or a stale training-data assumption.
  • Nothing private ships. Anonymize the report; the failure travels, your employer's code does not.

Size it before you write it

One session's failure buys one test and one section — usually an addition to a skill that already exists. Check Available Skills and the open issues before assuming you have a new one.

A new skill is warranted only when the claim has no home: not a subtopic of a shipped skill, and not one an existing skill should own. Prefer adding a section, then a pointer between skills, then a new skill last.

Write the eval from the session, not from the draft

Turn the captured artifacts into an eval before writing content, and write it from what happened rather than from what you intend to say:

  • The prompt is the question the session actually started with, stripped of the project.
  • The rubric encodes the correct claim (artifact 3), phrased so it would grade a stranger's answer — not so it matches your draft's wording.
  • The icontains tripwire is the name the wrong answer invented (artifact 1), asserted as not-icontains, plus the real name asserted as icontains. A session that hallucinated an API name hands you the sharpest tripwire you will ever write.

Mechanics, provider setup, and the baseline run are in evals/README.md.

Hand it off honestly

You are drafting a proposal, not a verified skill, and saying so is part of the handoff. State which claims you verified against a primary source and which you could not. If you cannot run the eval — no API keys, or you are working outside a clone of the repo — say that plainly rather than omitting it, and leave the pull request and its description to the human who will answer for them in review.

A failure report carrying artifacts 1, 2, and 4 is a complete contribution on its own. It is worth more than a draft skill with no evidence behind it.

Do not

  • Don't paste the transcript. A session is the source material, not the deliverable.
  • Don't cite a source you did not open. Reconstructed references are the most common way a confidently wrong claim gets through review.
  • Don't report an eval result you did not run, and don't invent the API keys to run one.
  • Don't promote a project quirk to a skill. If the fix depended on your build setup, it is not MapLibre judgment.
  • Don't skip the baseline because the answer feels obviously missing. The model may already get it right, and that finding is a result worth reporting, not a dead end.

Related Skills

  • maplibre-mapbox-migration — the frequent source of a plausible wrong first answer, where a Mapbox GL JS habit carries over.

References


This skill is a snapshot. Where a primary source contradicts it — the References above, MapLibre's current documentation, or what MapLibre does when you run it — that source wins. Follow it, then report the disagreement, citing the source and your MapLibre version: editing your installed copy helps no one else and is overwritten on the next update.

Frequently asked questions

What does the Maplibre Skill Authoring AI skill do?

How to turn a working session into a MapLibre agent skill — you researched something MapLibre's docs did not cover, got it wrong before you got it right, shipped it, and the session is about to end. Covers what to capture while the context is still live, how to separate the durable claim from the project it came from, and where to hand it off. Use at the end of a MapLibre task that took real research, or when asked to capture one as a skill.

Why use Maplibre Skill Authoring on TypingMind?

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

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

Which AI models can use Maplibre Skill Authoring?

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 Maplibre Skill Authoring?

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

Is the Maplibre Skill Authoring AI skill free?

It is published on GitHub by maplibre. Check the repository for licensing terms. 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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