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Debug Fix

Community
poshan0126
debug-fix

Find and fix a bug. Default is careful (reproduce, investigate, test). Add `--fast` for emergency production mode (hotfix branch, minimal change, ship a PR fast).

Overview

Publisherposhan0126
Repositorydotclaude
Skill namedebug-fix
Stars
870
Forks
204
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 poshan0126 on GitHub. Read the source before you install it.

Installation

Install the Debug Fix 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/poshan0126/dotclaude/tree/main/skills/debug-fix
  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/poshan0126/dotclaude.git /tmp/dotclaude
mkdir -p .claude/skills
cp -r /tmp/dotclaude/skills/debug-fix .claude/skills/debug-fix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debug Fix 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 Debug Fix 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 Debug Fix 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.

Find and fix the following issue:

Problem: $ARGUMENTS

Mode

Check $ARGUMENTS for --fast. Strip it before parsing the problem description.

  • Default: careful debug-fix. Use when you have time to do this right.
  • --fast: emergency hotfix. Use when production is broken and shipping speed matters more than thoroughness. Triggers a hotfix/* branch from production, minimal-change discipline, critical-tests-only verification, and ships a [HOTFIX] PR. Before committing to fast mode, briefly confirm with the user that this is genuinely emergency-grade. If not, suggest dropping --fast.

Step 1: Understand

  • Issue number: gh issue view $ARGUMENTS (or the project's issue tracker).
  • Error or stack trace: parse it for file, line, error type, and call chain.
  • Description: identify expected vs actual.
  • URL or screenshot: examine the referenced resource.

If unclear, ask clarifying questions before proceeding.

Step 2: Hotfix branch (--fast only)

In --fast mode only:

  • Detect the production branch: git symbolic-ref refs/remotes/origin/HEAD or git remote show origin.
  • Stash uncommitted work if needed.
  • Create and switch to hotfix/<short-description> branched from production.
  • ASK the user to confirm the branch name first.

In default mode, skip this. Branch creation happens in Step 7.

Step 3: Reproduce (default only)

In --fast mode, skip this step. Trust the report and move to Step 5.

  • Find the simplest way to trigger the issue (a test, curl, script).
  • Confirm you can reproduce reliably.
  • If you can't reproduce:
    • Environment-specific? Check env vars, OS, runtime version, database state.
    • Intermittent? Likely a race condition. Look for shared mutable state, timing dependencies, async ordering assumptions.
    • Already fixed? git log for recent commits mentioning the issue.

Step 4: Investigate (default only)

In --fast mode, skip this and go to Step 5. Trust the report and minimize investigation depth.

Don't skip ahead to guessing:

  1. Locate the symptom. Which file and line produces the wrong output?
  2. Read the code path backwards. What called this? What data was passed?
  3. Check git history: git log --oneline -20 -- <file>, git log --all --grep="<keyword>".
  4. Narrow scope with git bisect or targeted grep.
  5. Form a hypothesis: "X is wrong because Y."
  6. Verify the hypothesis with a targeted log or assertion.
  7. If wrong, trace a different path. Don't keep guessing the same hypothesis.

Step 5: Fix

  • Make the smallest correct change.
  • Don't patch symptoms. Trace back to where bad data originates and fix it there.
  • Don't refactor surrounding code while fixing.
  • Don't add defensive checks that mask the problem.

In --fast mode specifically:

  • If the fix needs more than ~50 lines changed, warn the user. This may not actually be a hotfix.
  • Do NOT add features, change formatting, clean up unrelated issues, or add non-essential comments.

Step 6: Verify

Default:

  • Write a test that reproduces the bug and now passes.
  • Run related tests for regressions.
  • Run lint and typecheck.
  • Temporarily revert your fix and confirm the new test fails (proves the test catches the bug).

--fast:

  • Run only tests directly relevant to the changed code, not the full suite.
  • Run the build.
  • If you can reproduce the original error, verify it's fixed.
  • ASK the user if they want extra verification before shipping.

Step 7: Wrap up or ship

Default:

  • Create a branch if not already on one.
  • Stage only the fix and test files.
  • Commit: fix: <what was wrong and why> (#number).

--fast:

  • Stage only the fix files (never secrets, locks, or build output).
  • Draft commit: hotfix: <short description>. ASK the user to confirm.
  • Push: git push -u origin hotfix/<description>.
  • Create a PR targeting production:
    • Title: [HOTFIX] <description>.
    • Body: what broke, what caused it, what this fixes.
    • Try to add the hotfix label: gh pr create ... --label hotfix. Fall back to no label on failure.
  • Show the PR URL.

Rules

  • NEVER skip confirmation steps in --fast mode (branch name, commit message, push).
  • NEVER force-push.
  • NEVER commit secrets or unrelated changes.
  • If the user says "skip" at any step, skip and move on.
  • In --fast mode specifically: if the fix turns out to be complex, tell the user and suggest dropping --fast to use the careful path.

Frequently asked questions

What does the Debug Fix AI skill do?

Find and fix a bug. Default is careful (reproduce, investigate, test). Add `--fast` for emergency production mode (hotfix branch, minimal change, ship a PR fast).

Why use Debug Fix on TypingMind?

Because you install it once and use it with any model. Debug Fix 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 Debug Fix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/poshan0126/dotclaude/tree/main/skills/debug-fix. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debug Fix?

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 Debug Fix?

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

Is the Debug Fix AI skill free?

Yes. It is published on GitHub by poshan0126 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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