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Broken Window Check

Community
Archive228
broken-window-check

Before picking new work, smoke-test the last "completed" feature. If it's broken, revert and re-open it before touching anything else. Kills the "looks shipped, isn't shipped" bug across sessions.

Overview

PublisherArchive228
Repositoryloopkit
Skill namebroken-window-check
Stars
755
Forks
124
Bundled files
Instructions only
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.

  • 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 Broken Window Check 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/Archive228/loopkit/tree/main/skills/broken-window-check
  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/Archive228/loopkit.git /tmp/loopkit
mkdir -p .claude/skills
cp -r /tmp/loopkit/skills/broken-window-check .claude/skills/broken-window-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Broken Window Check 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 Broken Window Check 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 Broken Window Check 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.

Broken-Window Check

Across shift-notes-driven sessions (see shift-notes), agents will sometimes mark a feature complete after unit tests pass — even when the feature is end-to-end broken. The next session opens the repo, sees a green git log, and builds on top of a broken foundation. By the time anyone notices, three features are stacked on the crack.

The check: before picking new work, exercise the most recently "completed" feature end-to-end. If it fails, treat it as your only job this session.

The sequence — run in order, no skipping

  1. Read the last "done" entry in the shift notes / feature list (whichever the project uses).
  2. Drive the feature end-to-end using the actual runtime path — browser automation, HTTP request, CLI invocation. Not the unit test.
  3. Compare observed behavior to the spec — the steps field on the feature, or the acceptance criteria in the spec.
  4. If it works — proceed to normal work selection. Note the check in the shift notes ("verified feature N still green").
  5. If it fails —
    • git revert the commit that claimed completion (do not force-push).
    • Set that feature's status back to not-done in the feature list.
    • Note in shift notes: "reverted feature N, cause: ".
    • Fix it. That is your entire session.

Do not pick new work on top of a broken previous feature. Ever.

What "end-to-end" means

The check must exercise the path the user actually takes. Anything less is theater.

Feature shapeValid checkInvalid check
Web UI buttonPuppeteer/Playwright click → observe DOMexpect(handler).toHaveBeenCalled()
HTTP endpointcurl the route → check status + bodyUnit test on the handler function
CLI flagInvoke the binary with the flag → observe outputImport the parser, assert on the AST
Background jobTrigger it → wait → assert side effectAssert the job function returns

Red flags — the check is not doing its job

  • You're reading tests instead of running the feature. Tests can pass while the feature is broken (wrong route, missing CORS, config mismatch). Drive the runtime.
  • You add a mock to make the check pass. The mock is the bug. Remove it, watch the failure, that's the real state.
  • You "fix" by editing the feature list to match reality ("oh it never supported X"). No. Revert. Re-open. Fix the code or negotiate scope in the spec, not the ledger.
  • You skip the check because "it worked yesterday". Yesterday's session doesn't run today's dev server.

Cost

The check is 30-90 seconds per session in a healthy project. In a project that's about to go sideways, it saves hours. The dropout in premature-completion rate is roughly 4x when the check is enforced vs. not (measured in shift-work-style agent runs).

Pairs with

  • shift-notes — the ledger the check reads and writes.
  • adversarial-verify — run this on the current session's diff before claiming done, so the next session doesn't have to broken-window you.
  • verification-before-completion — the general form of "don't claim without evidence".

If every session enforces the check, the compounding-error mode of shift-work agents stops compounding.

Frequently asked questions

What does the Broken Window Check AI skill do?

Before picking new work, smoke-test the last "completed" feature. If it's broken, revert and re-open it before touching anything else. Kills the "looks shipped, isn't shipped" bug across sessions.

Why use Broken Window Check on TypingMind?

Because you install it once and use it with any model. Broken Window Check 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 Broken Window Check in TypingMind?

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

Which AI models can use Broken Window Check?

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 Broken Window Check?

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

Is the Broken Window Check 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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