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Exploratory Test

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tobihagemann
exploratory-test

Execute multi-level exploratory testing of the app covering basic functionality, complex operations, adversarial testing, and cross-cutting scenarios, plus usability observations through a UX lens reported separately from defects. Deeper than $smoke-test. Use when the user asks to "exploratory test", "test thoroughly", "test all scenarios", "deep test", "test edge cases", "test everything", "break it", "find bugs by testing", "test usability", or "check the UX while testing".

Overview

Publishertobihagemann
Repositoryturbo
Skill nameexploratory-test
Stars
417
Forks
32
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 tobihagemann on GitHub. Read the source before you install it.

Installation

Install the Exploratory Test 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/tobihagemann/turbo/tree/main/codex/skills/exploratory-test
  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/tobihagemann/turbo.git /tmp/turbo
mkdir -p .claude/skills
cp -r /tmp/turbo/codex/skills/exploratory-test .claude/skills/exploratory-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Exploratory Test 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 Exploratory Test 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 Exploratory Test 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.

Exploratory Test

Execute multi-level exploratory testing that goes beyond smoke testing to actively find bugs through escalating test scenarios.

Task Tracking

At the start, use update_plan to track each step, restating any remaining steps of a parent workflow alongside them:

  1. Load or create test plan
  2. Determine testing approach
  3. Run $user-experience skill (when user-facing)
  4. Run $test-run-rules skill
  5. Execute tests by level
  6. Report

Step 1: Load or Create Test Plan

Resolve the test plan using these rules in order:

  1. Explicit path — If a file path was passed, use it
  2. Explicit slug — resolve to .turbo/test-plans/<slug>.md
  3. Anchoring artifact — If the work under test is anchored to a plan, resolve to .turbo/test-plans/<that-slug>.md when that file exists
  4. Single file — Glob .turbo/test-plans/*.md. If exactly one file exists, use it
  5. Most recent — If multiple files exist, use the most recently modified
  6. Legacy fallback — .turbo/test-plan.md if .turbo/test-plans/ does not exist
  7. Nothing found — run the $create-test-plan skill first, then use the plan it writes

If multiple test plans exist and the most-recent choice is non-obvious, use request_user_input to let the user pick from the candidates.

Read the resolved test plan and state its path.

Unless an explicit path or slug was passed, confirm the resolved plan still describes the work under test:

  • Unavailable branch state — a scenario's steps require a branch that no longer resolves in the repository
  • Completed prior run — every checkbox is already ticked and no recorded result is FAIL or PARTIAL
  • Superseded context — the plan's Context section names work that changes merged since the plan was written have reversed or removed

When a signal fires, output the signal and the scenarios it affects as text. For a superseded Context, name the scenarios that exercise the reversed or removed work. Then use request_user_input to offer:

  • Regenerate — run the $create-test-plan skill with the resolved path, and use the plan it writes
  • Execute anyway — the signal is a false positive
  • Pick another plan — resolve to a different test plan file, then confirm that plan against these same signals

If the user specifies a narrower scope, filter the plan to relevant scenarios rather than executing all of them. Reserve filtering for that case: a superseded plan keeps scenarios that each look plausible alone, so trimming it preserves the wrong ones.

Step 2: Determine Testing Approach

Use the approach specified in the test plan. If the plan does not specify one, determine it using the same logic as $create-test-plan Step 2.

Step 3: Run $user-experience Skill (When User-Facing)

If the app has a user-facing surface (UI, screens, commands, messages, or any behavior a user sees or does), run the $user-experience skill to load the UX lens before executing tests, so usability concerns surface while interacting with the app. When it is unclear whether the surface is user-facing, use request_user_input to ask rather than skipping silently. Skip this step for test targets with no user-facing behavior (internal library or infrastructure).

Step 4: Run $test-run-rules Skill

Run the $test-run-rules skill to load the rules for launching and driving the app.

Step 5: Execute Tests by Level

Work through each level sequentially. Complete all tests in a level before moving to the next.

Execution Loop (Per Test)

  1. Set up the preconditions described in the test scenario
  2. Perform the exact steps
  3. Capture the result (screenshot, output, or state observation)
  4. Compare against the expected outcome
  5. Record PASS, FAIL, or PARTIAL with details
  6. When the UX lens is loaded, note any usability observation it surfaces, kept separate from the verdict

Record a scenario the test run rules leave blocked or inconclusive as PARTIAL, naming what is unproven and why.

When the scenario's output is consumed by another system, withhold PASS until that system accepts it. Decoding a token, reading a response body, or confirming a row exists shows only that the artifact was produced. Stand up the consumer under the same isolation and cleanup rules as any other service this run starts, and exercise its own flow. When standing it up is not possible, record PARTIAL and name which half is unproven. PARTIAL counts as not passed everywhere a verdict is tallied or gated.

Level Progression

  1. Level 1: Basic Functionality — If any Level 1 test does not pass, report early and use request_user_input to ask whether to continue. Basic failures may indicate the feature is too broken for deeper testing.
  2. Level 2: Complex Operations — Execute all tests regardless of individual failures.
  3. Level 3: Adversarial Testing — Execute all tests. Failures here are expected and valuable.
  4. Level 4: Cross-Cutting Scenarios — Execute all tests.

If a project-specific testing skill or MCP tool was identified in Step 2, use that. The paths below are fallbacks.

Web App Path

Start or reuse a dev server under the test run rules. Use the browser-use@openai-bundled plugin to interact with the app.

UI/Native App Path

Launch the app. Use the computer-use@openai-bundled plugin to interact with the UI.

CLI Path

Run commands directly.

Step 6: Report

Present results organized by level:

Exploratory Test Results:

## Level 1: Basic Functionality (X/Y passed)
- [PASS] Test name: description — [substitution, when one was driven]
- [FAIL] Test name: description — [what went wrong]
- [PARTIAL] Test name: description — [what is unproven, and why]

## Level 2: Complex Operations (X/Y passed)
- [PASS] Test name: description — [substitution, when one was driven]
- [FAIL] Test name: description — [what went wrong]
- [PARTIAL] Test name: description — [what is unproven, and why]

## Level 3: Adversarial Testing (X/Y passed)
- [PASS] Test name: description — [substitution, when one was driven]
- [FAIL] Test name: description — [what went wrong]
- [PARTIAL] Test name: description — [what is unproven, and why]

## Level 4: Cross-Cutting Scenarios (X/Y passed)
- [PASS] Test name: description — [substitution, when one was driven]
- [FAIL] Test name: description — [what went wrong]
- [PARTIAL] Test name: description — [what is unproven, and why]

Overall: X/Y passed across all levels

Report usability observations from the UX lens below the level results, separately from the defects. A scenario can pass every functional check and still surface a usability concern.

## Usability Observations
- [UX] <observation> — names the UX context it touches (Understanding, Bridging, or Flowing) and the goal mismatch or friction it creates

For each failure, include the relevant screenshot, output, or state observation.

When the change under test spans several repositories, add a per-repo view of the findings below the usability observations, naming a suggested fix site for each.

Update the resolved test plan file by checking off completed tests and annotating results.

Then call update_plan to mark this step completed and continue with the next step of the active workflow.

Rules

  • To diagnose failures, run the $investigate skill on the test report.

Frequently asked questions

What does the Exploratory Test AI skill do?

Execute multi-level exploratory testing of the app covering basic functionality, complex operations, adversarial testing, and cross-cutting scenarios, plus usability observations through a UX lens reported separately from defects. Deeper than $smoke-test. Use when the user asks to "exploratory test", "test thoroughly", "test all scenarios", "deep test", "test edge cases", "test everything", "break it", "find bugs by testing", "test usability", or "check the UX while testing".

Why use Exploratory Test on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tobihagemann/turbo/tree/main/codex/skills/exploratory-test. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Exploratory Test?

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 Exploratory Test?

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

Is the Exploratory Test AI skill free?

Yes. It is published on GitHub by tobihagemann 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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