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Skill Selector

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mizchi
skill-selector

Meta-skill for picking project skills via APM. Invoke ONLY when the user explicitly asks to set up apm.yml, choose which skills a project needs, or evaluate a catalog match — do NOT auto-invoke on routine project-init or apm-management tasks. Two-phase: pick from a curated catalog first; only escalate to broader search/evaluation when the catalog has no fit. Avoids the failure mode of impulse-installing skills you never use or hand-searching GitHub when a vetted answer already exists.

Overview

Publishermizchi
Repositoryskills
Skill nameskill-selector
Stars
333
Forks
4
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Skill Selector 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/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skill-selector .claude/skills/skill-selector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Selector 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 Selector 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 Selector 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.

Skill Selector

Adding a skill is cheap on disk and not free in context. Pick deliberately.

This skill separates two concerns:

  1. Selection from a curated catalog — fast, opinionated, mostly enough.
  2. Search-and-evaluate — broader, slower, only when phase 1 doesn't fit.

Run them as distinct passes. The catalog has been pre-vetted. Outside-catalog skills haven't, and need explicit evaluation before adoption.

When to use

  • Starting a new project / new repo and you want it stocked with the right skills
  • Mid-development, when a recurring task type is uncovered (suddenly need Playwright, suddenly need release automation)
  • A slash command or another skill hints at a downstream skill not yet installed

When NOT to use:

  • One-off task that won't recur — solve it inline; don't install a skill you'll never trigger again
  • The project already lists the relevant skill in apm.yml — re-check before searching

Phase 1 — Curated catalog

The catalog lives in references/catalog.md. It is grouped by project signal (language / tool / process), with apm install strings inline.

Workflow:

  1. Read references/catalog.md.
  2. Identify project signals from three sources:
    • Repo files: package.json (Node), moon.mod.json (MoonBit), gleam.toml (Gleam), flake.nix (Nix), .github/workflows/ (CI), Playwright config, dotenvx keystore.
    • Stated user intent: in-message cues like "we plan to deploy to X", "we publish a small npm utility eventually".
    • CLAUDE.md mandates: persistent rules in user-level ~/.claude/CLAUDE.md or project-level CLAUDE.md (e.g., "task runner: justfile", "lint: ast-grep") count as signals even when the scenario text is silent. When mandate and stated intent disagree, surface the conflict to the user before installing. In the proposal, label mandate-driven rows explicitly (e.g., # from ~/.claude/CLAUDE.md: task runner = justfile) so a reviewer doesn't mistake them for padding.
  3. Confirm with the user before installing — propose, let them subtract. Default to fewer skills, not more. Each skill consumes context every conversation.
    • Non-interactive contexts (subagent dispatch, scripted automation, batch runs): emit the proposal as the deliverable and stop. The caller subtracts. Do not stall waiting for a confirmation that will not come.
    • Active-in-language heuristic: if the user is actively writing code in a language with a <lang>-practice skill in the catalog (e.g. moonbit-practice, gleam-practice), include it. If they only consume a single binding or dep written in that language, hold off.
    • Platform-name caveat: when a catalog row's description names a specific CI provider / runtime / cloud:
      • Project platform matches: adopt as-is. Do not re-read the underlying SKILL.md; the catalog row already answered the question.
      • Project platform differs: read the underlying SKILL.md before deciding. The core capability may still apply — install for that core, note "integration glue N/A." Skipping for platform mismatch alone is wrong.
      • If you cannot read the underlying SKILL.md (no local install, no upstream clone, no network): do NOT silently skip the check. Surface the uncertainty in the proposal explicitly — e.g., note "adopting <skill> based on its catalog description; the SKILL.md was not consulted, so the core-vs-integration split is unverified." The user can then decide whether to verify before committing the install. Silently dropping the verification leaves a bug (the wrong skill adopted, or a usable skill rejected) that no later step recovers.
    • Out-of-band rows: rows tagged (out-of-band) in the catalog cannot be installed via public APM (chezmoi-local, gated, etc.). Mention them in prose if the project would benefit, but do NOT put them in apm.yml.
  4. Install via APM. Read apm-usage first to confirm the exact apm.yml syntax — the manifest format is non-trivial and field names should not be inferred from this skill alone.
    • Project scope: edit apm.yml, run apm install. Commit apm.lock.yaml.
    • Global scope: apm install -g <repo>/<path>, verify in ~/.apm/apm.yml.
    • targets: declaration is required. APM 0.12+ does not fall back to a default target when no marker directory (.claude/ / .github/ / etc.) exists at the repo root — apm install errors out asking for an explicit choice. Write targets: [claude] (or whatever the host harness is) in apm.yml unconditionally; do not rely on directory auto-detection.
    • targets: does not strictly gate deploy directories. Even with targets: [claude], APM 0.12+ may also write to .agents/skills/ alongside .claude/skills/. Inspect the apm install output (Skill integrated -> ...) and treat every listed path as a deploy target for the gitignore decision below.
    • Pinning: catalog entries do not carry tags. Resolve a concrete tag or SHA via apm view <repo> (or check the upstream repo's release page) before committing apm.yml. Floating refs are listed under Common mistakes.
  5. Decide the gitignore policy before the first commit. apm install auto-adds apm_modules/ to .gitignore but does NOT add the deploy targets. Pick one and stick to it:
    • Commit deploy targets (.claude/skills/, .agents/skills/, …): repo is self-contained, teammates / CI can clone without running apm install. Cost: file count balloons (7 skills ≈ 700 files in a recent utels install). Choose when teammates may not have APM, or when you want skill content reviewable in PRs.
    • Gitignore deploy targets: leaner repo; teammates run apm install --frozen-lockfile after clone. Add the exact paths emitted by apm install (e.g., .claude/skills/, .agents/skills/). Caveat: if the project also keeps local skills under the same directory (.claude/skills/<local-skill>/), gitignore only the APM-managed subpaths, not the whole directory.
    • Always commit apm.yml and apm.lock.yaml regardless. Without the lockfile the install is not reproducible — the deploy-target gitignore choice only changes whether the generated artifacts live in git.
    • Propose the choice to the user when the install lands the first APM skills in the repo. Don't silently pick — file-count bloat vs. clone-time install are both legitimate but the trade-off is repo-specific.
  6. If a need is unmet, escalate to Phase 2. Do not skip Phase 1 — even if a search query is already forming in your head, scanning the catalog is cheaper.

Phase 2 — Search and evaluate (delegated to skill-finder)

Phase 2 is owned by the skill-finder skill. Trigger it only when all of the following are true:

  • Phase 1 has no candidate (catalog scanned, no row matched within ~30 seconds).
  • The need is recurring, not a one-off setup task. One-off scaffolding (Vite/React init, single-shot config conversion, ad-hoc data migration) belongs inline.
  • The need is stated as an active pain or near-term task, not just an ambient signal. A Rust service that exists in the repo but isn't currently causing skill-shaped questions does not by itself justify Phase 2 — wait until the recurring need surfaces.

If any of the three is false, do nothing. Re-read the "When NOT to use" section above before escalating.

To run Phase 2: tell the user "Phase 1 catalog has no fit; want me to invoke skill-finder for a cross-source search?" and only proceed on explicit go-ahead. skill-finder performs the cross-source survey (Anthropic official → claude-skill-registry → VoltAgent → ComposioHQ → Superpowers → GitHub topic), applies the same rubric, and gates adoption through a mandatory waxa eval. Do not duplicate that workflow inline.

Phase boundary — do not blur

The two phases solve different problems:

  • Phase 1 trades breadth for confidence. The catalog is curated; trust it.
  • Phase 2 trades confidence for breadth. Compensate with explicit evaluation.

Common failure: starting in Phase 2 ("let me grep GitHub for a Playwright skill") when Phase 1 already has playwright-test. Always read the catalog first.

Reverse failure: forcing a Phase 1 fit when the catalog truly has nothing suitable. If no row matches within ~30 seconds of scanning, escalate — don't pad the install list with adjacent-but-not-quite skills.

Maintenance of the catalog

  • Catalog is part of this skill. Keep it in sync when mizchi/skills (and upstream skill repos referenced) gain or lose skills.
  • A skill discovered through Phase 2 (skill-finder) may be promoted into the catalog after it has been used in 2+ projects without issue and after passing its waxa eval.
  • If the catalog feels stale, cross-check against mizchi/skills README before falling back to skill-finder.

Common mistakes

MistakeFix
Installing skills "just in case"Don't. Each one costs context per conversation. Install only when there's a near-term task that needs it.
Skipping the catalog and going straight to GitHub searchRe-read the catalog first. It exists to avoid this.
Adopting a Phase 2 skill without testingskill-finder requires a waxa eval gate before adoption. Don't bypass it — descriptions can lie.
Floating refs in apm.yml for project scopePin to a tag or SHA. Drift mid-feature is its own debugging hell.
Re-evaluating the same rejected skill quarterlyRecord the rejection reason in-repo. Don't re-search ground already covered.
Silently committing (or silently gitignoring) the APM deploy targetsSurface the choice — see step 5. Default-commit can balloon the repo; default-ignore can break teammates without APM.
Assuming targets: [claude] keeps deploy paths under .claude/skills/ onlyAPM 0.12+ may also write to .agents/skills/. Read the apm install output and treat every path it lists as a deploy target.
Treating Phase 1 catalog as exhaustiveIf nothing matches in ~30 seconds, escalate to Phase 2. Don't shoehorn.

Related

  • skill-finder — Phase 2 owner; cross-source survey + waxa eval gate when the catalog has no fit
  • apm-usage — actual apm install syntax and manifest format
  • empirical-prompt-tuning — how to test a candidate skill before adopting it
  • superpowers:writing-skills — when no existing skill fits, write one instead of adopting a poor match
  • chezmoi-management — for skills that must stay private (the APM-vs-chezmoi boundary)

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 Selector AI skill do?

Meta-skill for picking project skills via APM. Invoke ONLY when the user explicitly asks to set up apm.yml, choose which skills a project needs, or evaluate a catalog match — do NOT auto-invoke on routine project-init or apm-management tasks. Two-phase: pick from a curated catalog first; only escalate to broader search/evaluation when the catalog has no fit. Avoids the failure mode of impulse-installing skills you never use or hand-searching GitHub when a vetted answer already exists.

Why use Skill Selector on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mizchi/skills/tree/main/skill-selector. 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 Selector?

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 Selector?

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

Is the Skill Selector AI skill free?

It is published on GitHub by mizchi. 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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