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Inspect Quality

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danielvm-git
inspect-quality

Interactive QA session where user reports bugs or issues conversationally, and the agent logs them to specs/bugs/registry.yaml with a structured audit schema. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, or mentions "QA session".

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill nameinspect-quality
Stars
206
Forks
18
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 danielvm-git on GitHub. Read the source before you install it.

Installation

Install the Inspect Quality 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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/inspect-quality .claude/skills/inspect-quality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Inspect Quality 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 Inspect Quality 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 Inspect Quality 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.

Inspect Quality

HARD GATEHARD GATE — Quality metrics (coverage, lint, cyclomatic complexity, security scans) must be monitored. If a metric degrades, surface it as a blocker. Do NOT accept regressions.

Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and log each issue to specs/bugs/registry.yaml with a structured, durable format.

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 log, move on.

2. Explore the codebase in the background

Kick off an Agent (subagent_type=Explore) 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 specs/UBIQUITOUS_LANGUAGE_LATEST.md if present)
  • Understand what the feature is supposed to do
  • Identify the user-facing behavior boundary

3. Assess scope: single issue or breakdown?

Break down when:

  • The fix spans multiple independent areas
  • There are clearly separable concerns that could be worked on in parallel
  • The user describes something with multiple distinct failure modes

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. Log to specs/bugs/registry.yaml

Append the issue to specs/bugs/registry.yaml. Create the specs/bugs/ directory if it doesn't exist.

registry.yaml format

The file maintains a Markdown table with the following columns (derived from structured audit practice):

FieldDescription
bug_idBUG-YYYY-MM-DDTHHMMSS
dateYYYY-MM-DD
severitycritical / high / medium / low
priorityp0 / p1 / p2 / p3
scopekebab-case area (e.g. auth, checkout)
what_happenedactual behavior (user-facing terms)
what_expectedexpected behavior
steps_to_reproducenumbered steps
root_causeone-line hypothesis
files_changedfilled in after fix
approachfilled in after fix
risk_levellow / medium / high
new_testscount (filled in after fix)
type_checkpass / fail (filled in after fix)
lintpass / fail (filled in after fix)
commit_typefix / fix! / feat (filled in after fix)
release_typepatch / minor / major (filled in after fix)
commit_messageConventional Commits message (filled in after fix)
follow_upssemicolon-separated follow-up items
filepath to detailed specs/bugs/BUG-*.md (filled in by investigate-bug)
statusopen / in-progress / fixed / wont-fix

When a bug is fixed (via validate-fix), update the relevant row with the resolution fields.

Issue body (for context below the table)

For each bug, also append a detail section:

markdown
### BUG-YYYY-MM-DDTHHMMSS: [short title]

**What happened:** [actual behavior, plain language]
**What I expected:** [expected behavior]
**Steps to reproduce:**
1. [Step 1]
2. [Step 2]

**Additional context:** [domain-language observations, no file paths]
Rules for all entries
  • bug_id uses full timestamp: BUG-YYYY-MM-DDTHHMMSS — matches the individual bug file name in specs/bugs/
  • No file paths or line numbers — these go stale
  • Use the project's domain language (check specs/UBIQUITOUS_LANGUAGE_LATEST.md if it exists)
  • Describe behaviors, not code — "the sync service fails to apply the patch" not "applyPatch() throws"
  • Reproduction steps are mandatory — if you can't determine them, ask the user

5. Continue the session

After logging, ask: "Next issue, or are we done?" Keep going until the user says done. Each issue is independent — don't batch them.

Frequently asked questions

What does the Inspect Quality AI skill do?

Interactive QA session where user reports bugs or issues conversationally, and the agent logs them to specs/bugs/registry.yaml with a structured audit schema. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, or mentions "QA session".

Why use Inspect Quality on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielvm-git/bigpowers/tree/main/skills/inspect-quality. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Inspect Quality?

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 Inspect Quality?

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

Is the Inspect Quality AI skill free?

Yes. It is published on GitHub by danielvm-git 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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