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Testing Verification

Community
thienanblog
testing-verification

Design or assess tests, acceptance checks, CI coverage, and browser verification. Use when verification is the main deliverable or requires specialist judgment; ordinary implementation can keep its focused checks inline.

Overview

Publisherthienanblog
Repositoryawesome-ai-agent-skills
Skill nametesting-verification
Stars
66
Forks
21
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by thienanblog on GitHub. Read the source before you install it.

Installation

Install the Testing Verification 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/thienanblog/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/plugins/project-development-skills/skills/testing-verification .claude/skills/testing-verification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testing Verification 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 Testing Verification 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 Testing Verification 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.

Testing Verification

Verify observable behavior at the narrowest reliable level, using project conventions and the failure cost to choose coverage.

Working agreement

Follow the user's request and applicable repository instructions over these defaults. Use existing authorization; ask only about missing decisions that materially affect scope, cost, safety, or the result. Continue independent authorized work while awaiting an answer.

Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.

Select evidence

Inspect relevant contracts, nearby tests, fixtures, commands, and CI definitions. Choose checks that can fail for the behavior in question, including important negative paths. Use test-strategy.md when the test level or coverage tradeoff is unclear.

Prefer existing test infrastructure and stable fixtures. Test public behavior rather than incidental implementation details; avoid hidden network dependencies, production data, and timing-based assertions. Add automation when it protects meaningful behavior, without writing tests that simply mirror trivial edits.

Browser work

Follow the host's browser policy. Use its built-in Browser for interactive, exploratory, screenshot, and visual comparison work when available. Use Playwright MCP only when that surface is unavailable and record why; troubleshoot a failed Browser setup before treating it as unavailable. If the user explicitly chose Browser, obtain direction before substituting another surface.

Source-controlled Playwright E2E provides repeatable regression coverage. Keep it distinct from a manual Browser pass. Read ui-visual-verification.md when comparison conditions or visual ambiguity matter.

Run and finish

Run focused checks and required repository gates. Investigate failures before broadening, and rerun only affected checks after a fix. Reuse passing results for unchanged responsibilities and equivalent conditions. A commit, PR, merge, or handoff alone does not justify repeating a suite.

Follow explicit testing budgets. Propose a broader suite only when it could resolve a material gap; ask when project policy or unapproved cost requires it. Once sufficient evidence exists, finish without a routine full-suite question.

Report commands, results, relevant coverage, any browser surface used, and remaining gaps. Do not claim behavioral or visual verification from static checks alone.

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 Testing Verification AI skill do?

Design or assess tests, acceptance checks, CI coverage, and browser verification. Use when verification is the main deliverable or requires specialist judgment; ordinary implementation can keep its focused checks inline.

Why use Testing Verification on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thienanblog/awesome-ai-agent-skills/tree/main/plugins/project-development-skills/skills/testing-verification. 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 Testing Verification?

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 Testing Verification?

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

Is the Testing Verification AI skill free?

Yes. It is published on GitHub by thienanblog under the Apache-2.0 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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