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test-fixing

Detect failing tests and propose patches or fixes.

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What test-fixing Does

Test-fixing is an AI-powered skill that automatically detects failing tests in your codebase and generates targeted patches or fixes. Rather than spending hours debugging, developers can leverage Claude’s code understanding to identify root causes and propose solutions—whether that’s fixing logic errors, updating assertions, or adjusting test expectations. This skill is designed for development teams working with continuous integration pipelines, test-driven development practices, or legacy codebases where test failures are frequent and time-consuming to resolve.

The skill integrates into your development workflow by analyzing test output, examining the relevant source code, and understanding the mismatch between expected and actual behavior. It’s particularly valuable for teams that prioritize test coverage but struggle with maintenance overhead, as it reduces the manual cognitive load of interpreting test failures and tracing them back to source.

How to Install

  1. Prerequisites: Ensure you have access to the Claude Code skills marketplace and a working development environment with Git installed.

  2. Clone the repository:

    git clone https://github.com/mhattingpete/claude-skills-marketplace.git
cd claude-skills-marketplace/engineering-workflow-plugin/skills/test-fixing

  3. Review the skill configuration: Check the skill.json or manifest.json file to understand dependencies and configuration options.

  4. Install the skill in your IDE or Claude agent setup:

    • For Claude Code integration: Upload the skill directory to your agent configuration
    • For CLI usage: Add the skill path to your environment variables or configuration file

  5. Verify installation: Run a simple test command to confirm the skill is accessible:

    claude skill list | grep test-fixing

  6. Configure for your project: Update any project-specific settings (test framework, language, output format) in the skill configuration.

Use Cases

  • CI/CD pipeline failures: Automatically generate fixes for test failures detected in continuous integration, reducing deployment delays and enabling faster iteration cycles.
  • Test maintenance in large codebases: When refactoring code, tests often fail; this skill helps identify which assertions or dependencies need updates without manual investigation.
  • TDD debugging: Developers using test-driven development can quickly understand why a newly written test fails and get guidance on implementation changes needed.
  • Legacy system upgrades: When upgrading dependencies or frameworks (e.g., pytest to unittest, React version bumps), this skill identifies and proposes test updates for compatibility.
  • Onboarding and knowledge transfer: New team members can run failing tests and receive explanations of what’s expected, accelerating their understanding of the codebase.

How It Works

Test-fixing leverages Claude’s code comprehension to bridge the gap between test failures and source code fixes. When you provide a failing test, the skill parses the test runner output (capturing assertion errors, stack traces, or timeout messages) and cross-references it with the relevant source files. It builds a mental model of what the test expects versus what the code actually does, then generates patches ranked by likelihood of correctness.

The skill operates in several stages: (1) Parse test output to extract error messages, line numbers, and test names; (2) Retrieve context by loading the failing test file and related source code into the analysis window; (3) Analyze mismatch by comparing expected behavior (from test assertions) with actual behavior (from code logic); (4) Generate candidates by proposing multiple fix strategies (e.g., logic change, assertion adjustment, dependency mock, type fix); (5) Rank by confidence using code pattern matching and heuristics.

The skill handles common test failure patterns: assertion failures (where expectations don’t match reality), mock/stub issues (where external dependencies aren’t configured correctly), timing issues (race conditions or timeouts), and type/API mismatches (from refactoring or upgrades). It can propose fixes ranging from simple one-line changes to multi-file refactors, and it explains the reasoning behind each suggestion so developers can make informed decisions about which patch to apply.

Pros and Cons

Pros:

  • Dramatically reduces time spent interpreting test failures and debugging
  • Provides multiple fix candidates ranked by confidence, allowing informed selection
  • Explains root causes and reasoning, supporting team learning and code understanding
  • Works across multiple test frameworks and languages via output parsing
  • Integrates into CI/CD pipelines to accelerate failure resolution
  • Especially valuable for large codebases and legacy systems with high test maintenance burden

Cons:

  • May propose incorrect fixes in complex scenarios requiring domain expertise the AI lacks
  • Effectiveness depends on clear error messages and well-structured test code
  • Doesn’t replace understanding the codebase—fixes should be reviewed and validated
  • May struggle with flaky or non-deterministic failures
  • Requires sufficient test output context; some test frameworks may need custom output parsing
  • Can be overkill for simple, easily-debugged failures
  • Code debugger: Step through code execution to understand program state and behavior alongside test-fixing’s analysis
  • Type checker: Catch type mismatches that cause test failures before running tests
  • Refactoring assistant: Intelligently update code across multiple files when test-fixing suggests broader changes
  • Documentation generator: Create test documentation based on test intent that test-fixing analyzes
  • CI/CD integration: Automatically trigger test-fixing within your deployment pipeline to propose fixes on failures

Alternatives

  • Traditional debugging: Manually stepping through code with a debugger and reading test output—slower but gives complete control and understanding
  • Test generation tools: Rather than fixing existing tests, generate new ones automatically, though this doesn’t address failing tests you already have
  • Static analysis tools: Linters and type checkers catch some issues before tests run, complementing test-fixing but not replacing it for runtime failures
Glossary

Key terms

Test assertion
A statement in a test that verifies a condition is true (e.g., `assert result == expected`). When an assertion fails, the test fails.
Stack trace
A report of the active stack frames at a moment in time, showing the sequence of function calls that led to an error. Helps pinpoint where a test failure occurred.
Mock/Stub
Simplified replacement objects used in tests to simulate external services, databases, or APIs without requiring actual integration. Allows isolated unit testing.
Flaky test
A test that fails intermittently without code changes, usually due to timing issues, external dependencies, or race conditions. Difficult to debug because failures are non-deterministic.
Root cause
The underlying reason a test fails, distinct from the symptom. For example, the root cause might be a logic error while the symptom is an assertion mismatch.
FAQ

Frequently Asked Questions

How does test-fixing determine which fix is best?

The skill analyzes multiple factors: whether the fix aligns with the test's original intent, the likelihood based on common patterns in the codebase, the minimal scope of the change (simpler fixes rank higher), and whether the fix cascades to other tests. It presents ranked suggestions with confidence scores and explanations so you can choose the most appropriate fix for your context.

What test frameworks does test-fixing support?

Test-fixing is designed to work with standard test output formats. It best supports popular frameworks like pytest, Jest, JUnit, unittest, and Mocha by parsing their output formats. For other frameworks, you may need to ensure the error message and stack trace are clearly formatted in standard ways.

Can test-fixing handle flaky or intermittent test failures?

Test-fixing can identify patterns in flaky tests (race conditions, timing issues, external dependencies) but works best with deterministic failures. For intermittent failures, it may propose more defensive fixes like adding retries, improving synchronization, or mocking external services more reliably.

Does the skill modify my code automatically, or just suggest fixes?

Test-fixing operates in suggestion mode by default—it proposes patches and explains them, leaving the decision and approval to you. You can manually apply suggestions or use your IDE's integration to accept patches. This gives you full control and ensures fixes align with your team's standards.

How does test-fixing handle tests that depend on external services or databases?

The skill can identify external dependencies from error messages and test code, then suggest mock/stub strategies or configuration changes. It works best when your tests are designed with dependency injection or clear service boundaries, allowing it to propose targeted fixes without requiring actual service calls.

Can test-fixing explain what went wrong, not just fix it?

Yes. Each fix proposal includes an explanation of the root cause and why the patch addresses it. This is valuable for learning and understanding the codebase, especially useful for onboarding or knowledge transfer within teams.

What if test-fixing proposes a fix I disagree with?

You receive multiple fix candidates ranked by confidence. If you disagree with the top suggestion, review lower-ranked alternatives or use the explanations to guide your own fix. The skill is a suggestion tool, not a requirement—your domain knowledge and code standards always take precedence.

How does test-fixing handle performance tests or benchmarks?

For performance tests, the skill identifies timing thresholds and resource constraints from test assertions. It can suggest fixes like optimizing algorithms, caching improvements, or adjusting test timeouts, though true performance optimization often requires deeper analysis beyond the skill's scope.

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