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Test Blindspots

Community
Neeeophytee
test-blindspots

Find consequential behavior that a passing test suite does not establish, using focused exploratory checks. Use when the user asks what green tests miss or wants confidence in test coverage for a specific change. Not a general code review, routine test run, or debugging workflow for an already failing test.

Overview

PublisherNeeeophytee
Repositoryfinding-unknowns-skills
Skill nametest-blindspots
Stars
353
Forks
28
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 Neeeophytee on GitHub. Read the source before you install it.

Installation

Install the Test Blindspots 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/Neeeophytee/finding-unknowns-skills/tree/main/skills/test-blindspots
  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/Neeeophytee/finding-unknowns-skills.git /tmp/finding-unknowns-skills
mkdir -p .claude/skills
cp -r /tmp/finding-unknowns-skills/skills/test-blindspots .claude/skills/test-blindspots
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Test Blindspots 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 Test Blindspots 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 Test Blindspots 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.

Test blindspots

Passing tests establish their assertions under their setup. The gap to investigate is where the implementation and its tests share the same untested assumption.

Steps

  1. Establish the intended behavior and scope from the request, specification, changed code, callers, and relevant tests. Run the relevant baseline when possible. If it is already failing, report that limitation rather than describing it as green.
  2. Compare important behavior with what the tests actually assert. Look for a concrete gap: a mock replacing the boundary being claimed, an untested transition, a missing consumer expectation, or an invariant only exercised on the happy path. Choose by consequence and evidence, not by a generic checklist or coverage percentage.
  3. For each selected gap, state the question and design a small exploratory probe that can distinguish correct from incorrect behavior. Use existing tooling and isolated data. Prefer the actual component over a mock when the mock is the source of uncertainty.
  4. Execute the bounded probes within scope. Record observed behavior and the requirement it contradicts. If intended behavior is unclear, report a specification question; if execution is unavailable, report an untested risk. Neither is a confirmed defect.
  5. For a confirmed failure, preserve a minimal reproducer. Add a focused regression test when test edits are within the task's scope; verify that it fails for the intended reason. Report any deliberately failing reproducer separately from the baseline. Do not silently repair production code.
  6. Finish with confirmed defects first, then consequential untested risks or specification questions, the evidence for each, and what was not examined. Finding no consequential gap is a valid result.

Guardrails

  • Do not equate low coverage with a defect, or green tests with exhaustive correctness. A hypothetical scenario alone is not a finding.
  • Preserve existing tests and assertions. Do not weaken a test, expand the refactor, or install an unrelated testing framework to produce a result.
  • Keep probes away from production data and external side effects unless explicitly authorized. State when a local fixture cannot represent the real boundary.
  • Keep this proportional to the change. Stop when further exploration is unlikely to alter the user's decision.

Frequently asked questions

What does the Test Blindspots AI skill do?

Find consequential behavior that a passing test suite does not establish, using focused exploratory checks. Use when the user asks what green tests miss or wants confidence in test coverage for a specific change. Not a general code review, routine test run, or debugging workflow for an already failing test.

Why use Test Blindspots on TypingMind?

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

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

Which AI models can use Test Blindspots?

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 Test Blindspots?

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

Is the Test Blindspots AI skill free?

Yes. It is published on GitHub by Neeeophytee 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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