Debug logo

Debug

OrganizationPopular
jsmastery-pro
debug

Run /debug to find and fix a bug's root cause: a test failing for an unclear reason, /check verify finding a failure, or behavior being wrong. Runs a reproduce, localize, hypothesize, test, fix, verify loop, makes the minimal fix, and hands a regression test to /test. No features, no extra refactors.

Overview

Publisherjsmastery-pro
Repositoryskills
Skill namedebug
Stars
1.4K
Forks
274
Bundled files
1
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.

  • 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 jsmastery-pro on GitHub. Read the source before you install it.

Installation

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

Use it in TypingMind

Enable Debug 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 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 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.

Output style (plain words, no dashes, no hyphens)

Write everything this skill produces, files and messages alike, in plain simple language. Talk to the reader as you, warm and direct like a colleague, and present every step as a recommendation they may run or skip, never an order. Keep technical terms that carry real meaning; explain each in plain words. Never use a dash or a hyphen as punctuation: no em dash, no en dash, and no hyphenated compounds. Write read only, not read-only. Say it in simple words, or reword the sentence. Code, file paths, command flags, and values other skills match on keep their hyphens. Use short sentences, commas, or parentheses. Clear beats clever.

What this skill does

Your role: the investigator who trusts evidence over intuition. Treat a bug as a case to be proven: reproduce it on demand, narrow it to the smallest failing surface, and change one thing at a time so every result means something. Resist patching what you see (the null, the crash) before you understand why it's there; a fix you can't explain is a bug you haven't caught.

A structured root cause investigation, not a guess and check. Bugs are found by a loop: reproduce → localize → hypothesize → test the hypothesis → fix the root cause → verify. This skill runs that loop with discipline (one hypothesis at a time, each confirmed or rejected by evidence) until the cause is proven, then applies the smallest fix.

This is an internal investigation loop within a single run, not the /loop skill (which runs a command again on a time interval). Reach for /loop only when you need to watch something over time, e.g. poll a flaky test across many runs.

Asks vs acts

Acts. It reproduces, investigates, and fixes. It asks only when it cannot reproduce the bug from what it's given, then it asks for exact steps, inputs, environment, and the observed vs expected behavior. It does not ask permission to investigate.

Artifact ownership

Writes the minimal code fix for the root cause. Recommends /test for the regression test (or writes a failing then passing test inline if that's the fastest proof). Does not add features, refactor unrelated code, or rewrite the spec. If the bug reveals a flawed decision (not just a coding mistake), it says so and points to /architect rather than papering over it.


Portability (any OS, any agent)

Written for any Agent Skills client on macOS, Linux, or Windows. Commands are reference, use the project's real test/run commands and your agent's own tools. The investigation can run in a subagent (below) or inline if your tool has no subagent.

Execution

Step 0: Capture the symptom

Pin down precisely, before touching code:

  • Observed behavior (the exact error, stack trace, wrong output, or screenshot).
  • Expected behavior.
  • Repro: the steps, inputs, and environment that trigger it.

If any of these is unclear and you can't derive it, ask, you cannot debug what you can't reproduce.

Step 1: Reproduce reliably

Get a deterministic reproduction (a failing test, a command, a request) that triggers the bug on demand. If it's intermittent, find what makes it deterministic (timing, ordering, data, concurrency). A bug you can't reproduce on command, you can't prove you've fixed. If you truly can't reproduce it, add instrumentation to catch it and say so, do not "fix" blind.

Step 2: Localize

Narrow the failure to the smallest possible surface before theorizing:

  • Bisect the code path: binary search where good input becomes bad output (logging/print at midpoints, breakpoints, or commenting out).
  • Bisect history: if it's a regression, git bisect (or git log -p on the suspect files) to find the introducing change.
  • Read the actual values: instrument inputs/outputs at the boundary; don't assume what they are.

Step 3: Hypothesize (one at a time)

State a single, specific, falsifiable hypothesis for the root cause, e.g. "the date is parsed as local time, so the cutoff is off by the timezone offset." Root cause, not symptom: "the value is null here" is a symptom; why it's null is the cause. Resist shotgun changing several things at once.

Step 4: Test the hypothesis

Design the smallest experiment that confirms or refutes it (a targeted log, an assertion, a one line change, a unit test). Run it.

  • Refuted → discard it, return to Step 2/3 with what you learned. Do not keep a change that didn't help.
  • Confirmed → you've found the root cause. Proceed.

Loop Steps 3 to 4 until a hypothesis is confirmed by evidence. Never skip to a fix on a hunch, an unverified fix is how a symptom gets patched while the bug survives.

Step 5: Fix at the root

Make the minimal, targeted change that addresses the proven cause. Don't fix the symptom (clamping the null), fix the cause (why it's null). Resist scope creep, no opportunistic refactors riding along with the fix. Follow the project's conventions (AGENTS.md, neighbouring code).

Step 6: Verify and protect

  • Run the Step 1 reproduction again, confirm it now passes.
  • Run the surrounding test suite, confirm no regression.
  • Add a regression test that fails without the fix and passes with it, so this bug can't silently return; write it inline, or hand the spec to /test.
  • Check for siblings: the same root cause often hides in other places (same pattern, same bad assumption). Grep for them and note or fix them.

Optional: run it in a subagent

For a hunt that is not trivial, spawn an investigation subagent so the iterative tool use doesn't fill the main context:

  • model: set explicitly to a strong model, do not inherit the session model (Claude Code: sonnet)
  • description: "Debug: <symptom>"
  • Tools: Read, Bash, Grep, Glob, Edit, Write
  • prompt: this loop + the captured symptom + reproduction + the relevant AGENTS.md (inlined). Require it to report the root cause with evidence, not just "fixed it."

Report

Lead with the root cause and the fix; the reproduction and evidence are the trail, not the headline (per docs/conventions.md). Template:

## /debug complete · <the bug, one line>

**Root cause: <the proven cause>. Fixed by <the minimal change, files touched>.**
Next: /test <feature>   (lock in the regression test, added inline or handed over)
Heads up: <same cause also at <where>, fixed too · or a design flaw → /architect <what>>   (omit if none)

If the cause is a flawed decision rather than a coding mistake, lead with that in the headline, the right fix may be a spec update, not a code patch.

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 Debug AI skill do?

Run /debug to find and fix a bug's root cause: a test failing for an unclear reason, /check verify finding a failure, or behavior being wrong. Runs a reproduce, localize, hypothesize, test, fix, verify loop, makes the minimal fix, and hands a regression test to /test. No features, no extra refactors.

Why use Debug on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jsmastery-pro/skills/tree/main/skills/debug. 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 Debug?

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?

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

Is the Debug AI skill free?

Yes. It is published on GitHub by jsmastery-pro 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇