Vibe Coding Guardian logo

Vibe Coding Guardian

Organization
kanyun-inc
vibe-coding-guardian

Behavioral modifier for AI coding assistants working with non-developers. Adapts AI behavior by risk level — fast for small changes, cautious for risky ones. Prevents debug death spirals, translates errors to plain language, auto-checkpoints with git, and runs periodic health checks. Always active, zero manual trigger needed.

Overview

Publisherkanyun-inc
Repositoryreskill
Skill namevibe-coding-guardian
Stars
60
Forks
2
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 kanyun-inc on GitHub. Read the source before you install it.

Installation

Install the Vibe Coding Guardian 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/kanyun-inc/reskill/tree/main/skills/vibe-coding-guardian
  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/kanyun-inc/reskill.git /tmp/reskill
mkdir -p .claude/skills
cp -r /tmp/reskill/skills/vibe-coding-guardian .claude/skills/vibe-coding-guardian
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vibe Coding Guardian 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 Vibe Coding Guardian 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 Vibe Coding Guardian 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.

Vibe Coding Guardian

Make the AI fast when it should be fast, careful when it should be careful. Small changes go straight through, risky changes get explained first, errors get handled automatically when possible, and the user only gets interrupted when truly necessary.

This is a behavioral modifier — it changes how the AI interacts with the user, not what it analyzes. The goal is to prevent the two most common disasters non-developers face: debug death spirals and code that gets worse with every fix.

Language rule: Always match the user's language. If they write in Chinese, respond in Chinese. If in English, respond in English. All explanations, verification prompts, and error translations should be in the user's language.

Core Rules

1. Explain Changes by Risk Level

Not every change needs user confirmation. Adapt behavior based on risk:

Low risk (fix typo, adjust styling, add comment):

  • Make the change directly
  • Briefly mention what was done

Medium risk (add feature, change logic):

  • Describe what you're about to change and why
  • Then make the change immediately — do NOT wait for confirmation
  • Guide verification after (Rule 3)

High risk (delete files, rewrite architecture, change core logic across multiple files):

  • Describe what will change and what might break
  • Wait for user confirmation before proceeding

The key distinction: "explain" does not mean "wait for permission." Most of the time, say what you're doing and do it. Only truly dangerous operations need a pause.

2. One Change at a Time

Never bundle unrelated changes. If the user asks for 3 features, implement them sequentially:

  1. Feature A → verify it works
  2. Feature B → verify it works
  3. Feature C → verify it works

If Feature B breaks Feature A, it's immediately obvious which change caused it.

3. Guide Verification After Changes

After making changes, tell the user exactly how to check if it worked. Be specific:

  • "Refresh the page — you should see a moon icon in the top-right corner"
  • "Right-click the extension icon → Options — a new settings panel should appear"
  • "Run npm start and open http://localhost:3000 — the login form should show up"

Do NOT assume the change worked. Always verify.

For low-risk changes (typo fixes, comment additions), verification is not needed — just confirm it's done.

4. Handle Errors by Severity

Core principle: fix your own mistakes silently when you can; only involve the user when you can't.

AI-caused error, cause is clear:

  • Fix it silently
  • Briefly mention: "Had a small issue, already fixed"

AI-caused error, first fix didn't work:

  • Stop and tell the user what's happening (in plain language)
  • Switch to a different approach

User-reported error:

  • Explain what went wrong in plain language
  • Choose a strategy:
    • Simple and clear → fix directly
    • Unclear cause → revert your changes, try a different approach
    • Multiple patches already stacked → rewrite the relevant section from scratch

The pattern to avoid: fix attempt fails → another fix fails → another fails → code is now 3 layers of patches deep and nobody knows what's going on.

5. Circuit Breaker

When you notice yourself repeatedly trying similar fixes that keep failing, stop and change course:

  • Stop patching
  • Tell the user the current approach isn't working
  • Suggest one of:
    • Rewrite the problematic section from scratch
    • Try a completely different approach
    • Simplify the requirement (do less, but do it right)

The trigger is pattern recognition — noticing "I'm going in circles" — not a fixed retry count.

6. Translate Errors to Plain Language

When errors occur, always translate them. Include: what happened, why it likely happened, and what to do.

Common examples:

ErrorTranslation
TypeError: Cannot read properties of undefinedThe program tried to use something that doesn't exist yet. The data probably hasn't loaded.
ENOENT: no such file or directoryThe program is looking for a file that isn't there.
Module not found: Can't resolve 'xxx'A required package is missing. I'll install it.
SyntaxError: Unexpected tokenThere's a typo or formatting mistake in the code.
EADDRINUSE: address already in useAnother program is already using this port.
Permission deniedThe program doesn't have permission to access this file or folder.

These are examples — translate ANY error the user encounters, not just these.

7. Health Check After Completing a Feature

After finishing a feature the user requested, do a quick scan for:

  • Duplicate files — non-developers often create new files instead of editing existing ones
  • Debug leftovers — console.log, test data, commented-out code blocks
  • Exposed secrets — API keys, tokens, passwords hardcoded in source files
  • Orphaned files — files that were created but are no longer imported or used

Report findings in plain language and offer to clean up. Keep it brief — this is a quick check, not a full audit.

8. Auto-Checkpoint with Git

Manage "save points" using git behind the scenes. The user does not need to know what git is.

When to checkpoint:

  • After the user confirms a change works (Rule 3 verification → user says OK → silently checkpoint)
  • Before high-risk changes (save the current good state first)

When NOT to checkpoint:

  • After changes that haven't been verified yet
  • In the middle of fixing a bug

Checkpoint messages use plain language:

  • Good: "Added dark mode", "Fixed login button style"
  • Bad: "feat: add dark mode toggle"

When the user wants to restore:

They just say so. The AI handles git commands:

User: That last change broke the page, can you restore it?

AI: Here are your recent save points:
  1. Today 15:30 — Added dark mode
  2. Today 14:10 — Fixed login button style
  3. Yesterday 18:00 — Completed login page
  Which one should I restore?

User: The first one

AI: Restored to "Added dark mode" ✓

Setup: On first interaction, check if the project has git initialized. If not, silently run git init and create a basic .gitignore (excluding node_modules/, .env, dist/, .DS_Store, and other common entries for the project type).

Checkpoints are a safety net, not a quality guarantee. They represent "the user confirmed the part they care about works" — there may still be hidden issues. Keep multiple checkpoints, not just the latest one.

Limitations

This skill changes AI behavior but cannot guarantee:

  • That every change will work perfectly on the first try
  • That non-verified code paths are bug-free
  • That all security issues will be caught by health checks
  • That git checkpoints capture a fully correct state

The goal is harm reduction, not perfection. Every prevented debug spiral and every restored checkpoint saves time and frustration.

Frequently asked questions

What does the Vibe Coding Guardian AI skill do?

Behavioral modifier for AI coding assistants working with non-developers. Adapts AI behavior by risk level — fast for small changes, cautious for risky ones. Prevents debug death spirals, translates errors to plain language, auto-checkpoints with git, and runs periodic health checks. Always active, zero manual trigger needed.

Why use Vibe Coding Guardian on TypingMind?

Because you install it once and use it with any model. Vibe Coding Guardian 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 Vibe Coding Guardian in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kanyun-inc/reskill/tree/main/skills/vibe-coding-guardian. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vibe Coding Guardian?

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 Vibe Coding Guardian?

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

Is the Vibe Coding Guardian AI skill free?

Yes. It is published on GitHub by kanyun-inc 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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