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Qc Checklist

Community
sgharlow
qc-checklist

Turn a process description into an operational quality-control checklist — binary pass/fail checks, acceptance criteria, critical-vs-routine tiers, and failure modes with catch-points. Use when the user says "build a QC checklist", "standardize how we review X", "acceptance criteria for this process", or describes quality varying by who does the work.

Overview

Publishersgharlow
Repositoryclaude-code-recipes
Skill nameqc-checklist
Stars
393
Forks
50
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Qc Checklist 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/sgharlow/claude-code-recipes/tree/main/skills/qc-checklist
  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/sgharlow/claude-code-recipes.git /tmp/claude-code-recipes
mkdir -p .claude/skills
cp -r /tmp/claude-code-recipes/skills/qc-checklist .claude/skills/qc-checklist
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qc Checklist 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 Qc Checklist 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 Qc Checklist 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.

Quality Control Checklist

Convert "experienced people just know" into checks anyone can run. A good check is binary (pass/fail), observable (no judgment calls hidden inside), and placed at the point where the failure it catches actually happens.

Steps

  1. Get the process: what's produced, the steps, who performs it, and — most important — what has actually gone wrong before. Real past failures anchor the checklist; ask for 2–3 if none are offered.
  2. Draft the failure-mode inventory first: for each step, what can go wrong, how it would be detected, and the cost of missing it. Every checklist item must trace to a failure mode — a check that catches nothing is process theater.
  3. Build the checklist in two tiers:
    • Critical (stop-the-line): failures that are costly or invisible downstream. These are never skippable and each states its acceptance criterion measurably.
    • Routine: consistency checks that tolerate an occasional miss. Phrase every item as a verifiable pass/fail question with the evidence to look at — "Invoice total matches PO total (compare field X to field Y)", not "check invoice".
  4. Add the operating shell: when the checklist runs (per unit / per batch / per release), who signs off, where results are recorded, and the escalation when a critical check fails. A checklist without a recorded result is a suggestion.
  5. Verify by dry-run: walk one recent real work product through the draft. Every past failure the user named must be caught by some item; items that catch nothing on the dry-run get justified or cut. Then hand over with a 90-day review note — checklists drift as processes change.

Constraints

  • No item without a named failure mode it exists to catch.
  • Acceptance criteria are measurable ("≤ 2 business days", "all 4 fields populated") — no "appropriate", "sufficient", or "high quality".
  • Keep it runnable: if the full list exceeds ~15 items per tier, split by role or stage rather than shipping a wall nobody completes under time pressure.

Full walkthrough, examples, and variations: recipes/Recipe-048-Quality-Control-Checklists-Standards.md. For enforcing checks on AI-assisted code specifically, the same philosophy is implemented in ai-control-framework.

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 Qc Checklist AI skill do?

Turn a process description into an operational quality-control checklist — binary pass/fail checks, acceptance criteria, critical-vs-routine tiers, and failure modes with catch-points. Use when the user says "build a QC checklist", "standardize how we review X", "acceptance criteria for this process", or describes quality varying by who does the work.

Why use Qc Checklist on TypingMind?

Because you install it once and use it with any model. Qc Checklist 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 Qc Checklist in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sgharlow/claude-code-recipes/tree/main/skills/qc-checklist. 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 Qc Checklist?

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 Qc Checklist?

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

Is the Qc Checklist AI skill free?

It is published on GitHub by sgharlow. 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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