Qa logo

Qa

Organization
AgentiveStack
qa

Interactive QA session where you describe bugs conversationally and the agent files durable GitHub issues using the project's domain language. Explores the codebase in the background for context. Use when reporting bugs, doing QA testing, or filing issues from observations.

Overview

PublisherAgentiveStack
Repositoryskills
Skill nameqa
Stars
72
Forks
17
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 AgentiveStack 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/AgentiveStack/skills/tree/main/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/AgentiveStack/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/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. Describe problems you're encountering. I'll 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 raised

1. Listen and lightly clarify

Let you describe the problem in your own words. I'll ask at most 2-3 short clarifying questions:

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

I won't over-interview. If the description is clear enough, I'll move on.

2. Explore the codebase in background

While talking, I'll explore the relevant area to:

  • Learn the domain language (check docs/UBIQUITOUS_LANGUAGE.md)
  • Understand what the feature is supposed to do
  • Identify the user-facing behavior boundary
  • Check which bounded context this belongs to (docs/CONTEXT_MAP.md)

This helps me write a better issue — but the issue itself will NOT reference files, line numbers, or implementation details.

3. Assess scope

Before filing, decide: single issue or breakdown?

Break down when:

  • Fix spans multiple independent areas
  • Clearly separable concerns exist
  • Multiple distinct failure modes or symptoms

Keep as single when:

  • One behavior wrong in one place
  • Symptoms share the same root behavior

4. File the GitHub issue(s)

Create with gh issue create. I'll file and share URLs without asking for review first.

All issues must be durable — they should still make sense after major refactors.

Single issue template
markdown
## What happened

[Actual behavior in plain language, using domain terms]

## What I expected

[Expected behavior]

## Steps to reproduce

1. [Concrete, numbered steps]
2. [Use domain terms, not module names]
3. [Include relevant inputs, flags, config]

## Additional context

[Extra observations — use domain language, don't cite files]
Breakdown template (per sub-issue)
markdown
## Parent issue

#<parent-number> or "Reported during QA session"

## What's wrong

[This specific behavior problem, just this slice]

## What I expected

[Expected behavior for this slice]

## Steps to reproduce

1. [Steps specific to THIS issue]

## Blocked by

- #<issue-number> or "None — can start immediately"

## Additional context

[Relevant to this slice]

Rules for all issues

  • No file paths or line numbers — they go stale
  • Use the project's domain language (from UBIQUITOUS_LANGUAGE.md)
  • Describe behaviors, not code — "the experiment stats show 0 visitors" not "getExperimentStats() returns empty"
  • Reproduction steps are mandatory
  • Keep it concise — readable in 30 seconds

After filing, I'll share all issue URLs and ask: "Next issue, or are we done?"

5. Continue the session

Keep going until you're done. Each issue is independent.

Related skills

  • /holistic — understand the broader context of a bug before filing
  • /domain — if a bug reveals a terminology or domain model issue
  • /tdd — implement the fix using tracer-bullet TDD

Frequently asked questions

What does the Qa AI skill do?

Interactive QA session where you describe bugs conversationally and the agent files durable GitHub issues using the project's domain language. Explores the codebase in the background for context. Use when reporting bugs, doing QA testing, or filing issues from observations.

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/AgentiveStack/skills/tree/main/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?

Yes. It is published on GitHub by AgentiveStack 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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