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Bug Capture

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rohitg00
bug-capture

Capture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".

Overview

Publisherrohitg00
Repositorypro-workflow
Skill namebug-capture
Stars
2.9K
Forks
286
Bundled files
Instructions only
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Bug Capture 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.
  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/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/bug-capture .claude/skills/bug-capture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bug Capture 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 Bug Capture 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 Bug Capture 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.

bug-capture

Turn a conversation into an issue that still reads correctly after a major refactor.

Flow

1. Listen, then clarify minimally

Let the user describe the problem in their own words. Ask at most two short clarifying questions, drawn from:

  • Expected behavior vs. actual behavior.
  • Concrete reproduction steps if not already implied.
  • Frequency: deterministic, intermittent, or one-off.

If the description already answers these, skip straight to filing. Over- interviewing is a tax the reporter pays for your uncertainty.

2. Explore in parallel

While the user is answering, start a background exploration of the relevant area. The goal is not to propose a fix. The goal is to absorb the project's own vocabulary — the nouns and verbs the codebase uses for this feature — so the issue reads like it was written by a maintainer.

If the repo has a glossary file (common names: GLOSSARY.md, UBIQUITOUS_LANGUAGE.md, docs/domain.md), read it first.

3. Check for duplicates before filing

Run gh issue list --search "<key phrase>" --state all --limit 10. If a live or recently closed issue matches, surface it to the user and ask whether to add a comment instead of opening a new issue. Do not silently skip filing.

4. Decide: single issue or breakdown

Break down when the report contains two or more independent failure modes that a different contributor could fix in parallel. Keep as one when every symptom traces to a single wrong behavior.

For a breakdown, file in dependency order so each child issue can reference a real parent number, and mark honest Blocked by links. Avoid inventing dependencies to make the tree look tidier.

5. File with gh issue create

File without asking the user to review the draft. Send back the URLs.

Single-issue template
## What happened
<observed behavior, in domain terms>

## What I expected
<expected behavior>

## Reproduction
1. <step>
2. <step>
3. <step>

## Context
<anything that narrows where the bug lives, in domain terms — e.g.
"only affects the import path, not the export path">
Child-issue template
## Parent
#<parent-number>

## What is wrong
<one behavior, narrow slice>

## What I expected
<expected for this slice>

## Reproduction
1. <step>

## Blocked by
<#issue or "none — independent">

## Context
<notes that apply only to this slice>

6. Rules that apply to every issue body

  • No file paths, line numbers, function names, or PR numbers. These go stale. Describe behavior, not code.
  • Use the project's domain nouns, not generic tech terms. "The sync worker drops the patch" beats "applyPatch() throws".
  • Reproduction steps are mandatory. If you cannot derive them, go back to the user once before filing.
  • Thirty-second read target. Cut anything that does not help a maintainer decide whether to pick it up.

7. Keep going

After each issue, print the URL and ask whether there is a next one. Do not batch multiple reports into one filing pass — each bug deserves its own scoped issue.

Frequently asked questions

What does the Bug Capture AI skill do?

Capture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".

Why use Bug Capture on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/bug-capture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bug Capture?

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 Bug Capture?

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

Is the Bug Capture AI skill free?

It is published on GitHub by rohitg00. 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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