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Test Fixing

Community
mhattingpete
test-fixing

Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.

Overview

Publishermhattingpete
Repositoryclaude-skills-marketplace
Skill nametest-fixing
Stars
675
Forks
96
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Test Fixing 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/mhattingpete/claude-skills-marketplace.git /tmp/claude-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/claude-skills-marketplace/engineering-workflow-plugin/skills/test-fixing .claude/skills/test-fixing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Test Fixing 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 Fixing 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 Fixing 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 Fixing

Systematically identify and fix all failing tests using smart grouping strategies.

When to Use

  • Explicitly asks to fix tests ("fix these tests", "make tests pass")
  • Reports test failures ("tests are failing", "test suite is broken")
  • Completes implementation and wants tests passing
  • Mentions CI/CD failures due to tests

Systematic Approach

1. Initial Test Run

Run make test to identify all failing tests.

Analyze output for:

  • Total number of failures
  • Error types and patterns
  • Affected modules/files

2. Smart Error Grouping

Group similar failures by:

  • Error type: ImportError, AttributeError, AssertionError, etc.
  • Module/file: Same file causing multiple test failures
  • Root cause: Missing dependencies, API changes, refactoring impacts

Prioritize groups by:

  • Number of affected tests (highest impact first)
  • Dependency order (fix infrastructure before functionality)

3. Systematic Fixing Process

For each group (starting with highest impact):

  1. Identify root cause

    • Read relevant code
    • Check recent changes with git diff
    • Understand the error pattern
  2. Implement fix

    • Use Edit tool for code changes
    • Follow project conventions (see CLAUDE.md)
    • Make minimal, focused changes
  3. Verify fix

    • Run subset of tests for this group
    • Use pytest markers or file patterns:
      bash
      uv run pytest tests/path/to/test_file.py -v
      uv run pytest -k "pattern" -v
    • Ensure group passes before moving on
  4. Move to next group

4. Fix Order Strategy

Infrastructure first:

  • Import errors
  • Missing dependencies
  • Configuration issues

Then API changes:

  • Function signature changes
  • Module reorganization
  • Renamed variables/functions

Finally, logic issues:

  • Assertion failures
  • Business logic bugs
  • Edge case handling

5. Final Verification

After all groups fixed:

  • Run complete test suite: make test
  • Verify no regressions
  • Check test coverage remains intact

Best Practices

  • Fix one group at a time
  • Run focused tests after each fix
  • Use git diff to understand recent changes
  • Look for patterns in failures
  • Don't move to next group until current passes
  • Keep changes minimal and focused

Example Workflow

User: "The tests are failing after my refactor"

  1. Run make test → 15 failures identified
  2. Group errors:
    • 8 ImportErrors (module renamed)
    • 5 AttributeErrors (function signature changed)
    • 2 AssertionErrors (logic bugs)
  3. Fix ImportErrors first → Run subset → Verify
  4. Fix AttributeErrors → Run subset → Verify
  5. Fix AssertionErrors → Run subset → Verify
  6. Run full suite → All pass ✓

Frequently asked questions

What does the Test Fixing AI skill do?

Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.

Why use Test Fixing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mhattingpete/claude-skills-marketplace/tree/main/engineering-workflow-plugin/skills/test-fixing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Test Fixing?

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 Fixing?

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

Is the Test Fixing AI skill free?

Yes. It is published on GitHub by mhattingpete 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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