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Qa

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AvdLee
qa

Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

Overview

PublisherAvdLee
RepositoryRocketSimApp
Skill nameqa
Stars
805
Forks
37
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 AvdLee on GitHub. Read the source before you install it.

Installation

Install the Qa 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/AvdLee/RocketSimApp/tree/master/docs/.agents/skills/qa
  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/AvdLee/RocketSimApp.git /tmp/RocketSimApp
mkdir -p .claude/skills
cp -r /tmp/RocketSimApp/docs/.agents/skills/qa .claude/skills/qa
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qa 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 Qa 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 Qa 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.

QA Session

Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and file GitHub issues that are durable, user-focused, and use the project's domain language.

For each issue the user raises

1. Listen and lightly clarify

Let the user describe the problem in their own words. Ask at most 2-3 short clarifying questions focused on:

  • What they expected vs what actually happened
  • Steps to reproduce (if not obvious)
  • Whether it's consistent or intermittent

Do NOT over-interview. If the description is clear enough to file, move on.

2. Explore the codebase in the background

While talking to the user, kick off an Agent (subagent_type=Explore) in the background to understand the relevant area. The goal is NOT to find a fix — it's to:

  • Learn the domain language used in that area (check UBIQUITOUS_LANGUAGE.md)
  • Understand what the feature is supposed to do
  • Identify the user-facing behavior boundary

This context helps you write a better issue — but the issue itself should NOT reference specific files, line numbers, or internal implementation details.

3. Assess scope: single issue or breakdown?

Before filing, decide whether this is a single issue or needs to be broken down into multiple issues.

Break down when:

  • The fix spans multiple independent areas (e.g. "the form validation is wrong AND the success message is missing AND the redirect is broken")
  • There are clearly separable concerns that different people could work on in parallel
  • The user describes something that has multiple distinct failure modes or symptoms

Keep as a single issue when:

  • It's one behavior that's wrong in one place
  • The symptoms are all caused by the same root behavior

4. File the GitHub issue(s)

Create issues with gh issue create. Do NOT ask the user to review first — just file and share URLs.

Issues must be durable — they should still make sense after major refactors. Write from the user's perspective.

For a single issue

Use this template:

## What happened

[Describe the actual behavior the user experienced, in plain language]

## What I expected

[Describe the expected behavior]

## Steps to reproduce

1. [Concrete, numbered steps a developer can follow]
2. [Use domain terms from the codebase, not internal module names]
3. [Include relevant inputs, flags, or configuration]

## Additional context

[Any extra observations from the user or from codebase exploration that help frame the issue — e.g. "this only happens when using the Docker layer, not the filesystem layer" — use domain language but don't cite files]
For a breakdown (multiple issues)

Create issues in dependency order (blockers first) so you can reference real issue numbers.

Use this template for each sub-issue:

## Parent issue

#<parent-issue-number> (if you created a tracking issue) or "Reported during QA session"

## What's wrong

[Describe this specific behavior problem — just this slice, not the whole report]

## What I expected

[Expected behavior for this specific slice]

## Steps to reproduce

1. [Steps specific to THIS issue]

## Blocked by

- #<issue-number> (if this issue can't be fixed until another is resolved)

Or "None — can start immediately" if no blockers.

## Additional context

[Any extra observations relevant to this slice]

When creating a breakdown:

  • Prefer many thin issues over few thick ones — each should be independently fixable and verifiable
  • Mark blocking relationships honestly — if issue B genuinely can't be tested until issue A is fixed, say so. If they're independent, mark both as "None — can start immediately"
  • Create issues in dependency order so you can reference real issue numbers in "Blocked by"
  • Maximize parallelism — the goal is that multiple people (or agents) can grab different issues simultaneously
Rules for all issue bodies
  • No file paths or line numbers — these go stale
  • Use the project's domain language (check UBIQUITOUS_LANGUAGE.md if it exists)
  • Describe behaviors, not code — "the sync service fails to apply the patch" not "applyPatch() throws on line 42"
  • Reproduction steps are mandatory — if you can't determine them, ask the user
  • Keep it concise — a developer should be able to read the issue in 30 seconds

After filing, print all issue URLs (with blocking relationships summarized) and ask: "Next issue, or are we done?"

5. Continue the session

Keep going until the user says they're done. Each issue is independent — don't batch them.

Frequently asked questions

What does the Qa AI skill do?

Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

Why use Qa on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AvdLee/RocketSimApp/tree/master/docs/.agents/skills/qa. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Qa?

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 Qa?

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

Is the Qa AI skill free?

It is published on GitHub by AvdLee. 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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