Using Loopkit

Community

Archive228

using-loopkit

Use when starting any conversation in a loopkit-enabled project - establishes how to find and use loopkit's 49 skills, requiring skill invocation before ANY response including clarifying questions.

Overview

Publisher	Archive228
Repository	`loopkit`
Skill name	`using-loopkit`
Stars	755
Forks	124
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Archive228 on GitHub. Read the source before you install it.

Installation

Install the Using Loopkit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Archive228/loopkit/tree/main/skills/using-loopkit
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.

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/Archive228/loopkit.git /tmp/loopkit
mkdir -p .claude/skills
cp -r /tmp/loopkit/skills/using-loopkit .claude/skills/using-loopkit

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Loopkit 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 Using Loopkit 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 Using Loopkit 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.

Using Loopkit

IF A SKILL APPLIES, YOU DO NOT HAVE A CHOICE. YOU MUST USE IT.

This overrides "just answer quickly" instincts. Not negotiable.

The Rule

Invoke relevant skills BEFORE any response or action — including clarifying questions, exploring the codebase, or reading files. If it turns out wrong for the situation, drop it.

Then announce "Using [skill] to [purpose]" and follow the skill exactly. If it has a checklist, create a todo per item.

Where the skills live

Skills are files at .claude/skills/<name>/SKILL.md. Each has YAML frontmatter with name and description (the description is a trigger phrase, not a summary). Load a skill by reading its SKILL.md when its trigger matches your task.

Skill routing (49 skills, 10 tracks)

Task shape	First skill
"Fix this bug" / test failing / crash	`systematic-debugging`, then `read-the-trace`
"It broke between two commits"	`bisect-regression`
"Flaky test"	`flaky-hunter`
"Add a feature" / write anything new	`spec-first`, then `write-failing-test-first`
"Refactor" / dead code / deep nesting	`kill-dead-code`, `simplify`, `reduce-nesting`
About to claim done / commit / open PR	`adversarial-verify` + `verification-before-completion` + `self-eval-bias`
Review a diff	`adversarial-verify`, `pr-from-diff`
Frontend / UI work	`design-system`, `a11y-pass`, `loading-empty-error-states`
Security touch	`owasp-review`, `authz-check`, `input-validation`, `secret-scan`, `dependency-audit`
Data / SQL / migrations	`sql-review`, `migration-writer`, `schema-diff`
Docs / changelog / README	`changelog-from-diff`, `decision-record`, `readme-audit`
Git ops	`clean-commits`, `pr-from-diff`, `rebase-safely`, `revert-surgical`
Test suite gaps	`coverage-gaps`, `contract-test`
Running out of context	`context-budget`, `tool-restraint`
Parallel work	`subagent-fanout`
Starting a fresh project / major feature	`planner-spec-expand`, then `feature-list-json`, then `init-script-contract`
Bootstrapping into an existing multi-session project	`progress-reading-protocol`
Entering an implementation sprint	`sprint-contract`
Calibrating a reviewer / evaluator	`evaluator-calibration`
New Claude/Sonnet/Opus model landed	`harness-stripping`

Full list: ls .claude/skills/.

Red Flags — STOP and check for a skill

Thought	Reality
"This is just a simple question"	Questions are tasks. Check first.
"Let me explore the codebase first"	Skills tell you HOW to explore. Check first.
"I remember this skill"	Skills evolve. Read the current SKILL.md.
"The skill is overkill"	Simple things become complex. Use it.
"I'll just do this one thing first"	Check BEFORE doing anything.
"Tests pass, we're good"	`verification-before-completion` says: run the exact command, read the output, then claim.
"I'll do both features while I'm in here"	`single-feature-discipline` says: one per session. Never two.
"The reviewer will let this slide"	`self-eval-bias` says: assume it will confidently praise. Calibrate first.

Priority when multiple skills apply

Process skills first (spec-first, systematic-debugging, planner-spec-expand, sprint-contract), then implementation skills (design-system, sql-review, etc.), then finishers (adversarial-verify, verification-before-completion, self-eval-bias, clean-commits).

"Let's build X" → planner-spec-expand → feature-list-json → sprint-contract → domain skills → adversarial-verify.
"Fix bug Y" → systematic-debugging → read-the-trace → fix → verification-before-completion.
"Session open in existing project" → progress-reading-protocol → sprint-contract → work.

Skill release convention

Every new skill in loopkit ships as a folder with four required files. No exceptions — a skill without these is a draft, not a release.


skills/<skill-name>/
  SKILL.md            # the skill itself (frontmatter + procedure)
  POST.md             # ~200-word X-thread-shaped explainer
  evidence/
    before.md         # verbatim transcript WITHOUT the skill loaded
    after.md          # same prompt WITH the skill loaded

SKILL.md — routed on its frontmatter description. Body under ~150 lines.
POST.md — the announcement thread. Copy from template/POST.md, fill in every placeholder before publishing.
evidence/before.md + evidence/after.md — one real task, both transcripts. The before/after pair is the receipt that the skill actually changes behaviour. If you cannot produce it, the skill is not ready; land it as a draft and merge once the evidence is real.

Bootstrap a new skill by copying template/ verbatim:


cp -r template skills/<skill-name>

Then edit SKILL.md, write POST.md, and capture the before/after pair.

User instructions win

CLAUDE.md, AGENTS.md, and direct user requests override loopkit skills. Only skip a skill workflow when the user has explicitly said to.

Frequently asked questions

What does the Using Loopkit AI skill do?

Use when starting any conversation in a loopkit-enabled project - establishes how to find and use loopkit's 49 skills, requiring skill invocation before ANY response including clarifying questions.

Why use Using Loopkit on TypingMind?

Because you install it once and use it with any model. Using Loopkit 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 Using Loopkit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Archive228/loopkit/tree/main/skills/using-loopkit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Using Loopkit?

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 Using Loopkit?

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

Is the Using Loopkit AI skill free?

Yes. It is published on GitHub by Archive228 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Using Loopkit

The Rule

Where the skills live

Skill routing (49 skills, 10 tracks)

Red Flags — STOP and check for a skill

Priority when multiple skills apply

Skill release convention

User instructions win

Frequently asked questions

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