Agent skill
analyze-test-failures
Use when analyzing failing test cases to determine whether failures indicate genuine bugs or test implementation issues. Activates on "analyze failing tests", "debug test failures", "investigate test errors", or when provided with specific failing test names or output. Applies balanced investigative reasoning — does not auto-fix tests without establishing root cause.
Install this agent skill to your Project
npx add-skill https://github.com/Jamie-BitFlight/claude_skills/tree/main/plugins/python3-development/skills/analyze-test-failures
SKILL.md
Analyze Test Failures
Analyze failing test cases with a balanced, investigative approach.
Context
Consult ../python3-development/references/python3-standards.md when shared testing or quality rules from this plugin apply; full standards, graphs, and amendment process are documented there.
When tests fail, there are two primary possibilities:
- False positive: The test itself is incorrect
- True positive: The test discovered a genuine bug
Assuming tests are wrong by default is a dangerous anti-pattern that defeats the purpose of testing.
Analysis Process
1. Initial Analysis
- Read the failing test carefully, understanding its intent
- Examine the test's assertions and expected behavior
- Review the error message and stack trace
2. Investigate the Implementation
- Check the actual implementation being tested
- Trace through the code path that leads to the failure
- Verify that implementation matches documented behavior
3. Apply Critical Thinking
For each failing test, ask:
- What behavior is the test trying to verify?
- Is this behavior clearly documented or implied by the API design?
- Does the current implementation actually provide this behavior?
- Could this be an edge case the implementation missed?
4. Make a Determination
Classify the failure as one of:
| Classification | Meaning |
|---|---|
| Test Bug | Test's expectations are incorrect |
| Implementation Bug | Code doesn't behave as it should |
| Ambiguous | Intended behavior is unclear |
5. Document Reasoning
Provide clear explanation including:
- Evidence supporting the conclusion
- Specific mismatch between expectation and reality
- Recommended fix (to test or implementation)
Example Analyses
Example 1: Ambiguous Behavior
Scenario: Test expects calculateDiscount(100, 0.2) to return 20, but it returns 80
Analysis:
- Test assumes function returns discount amount
- Implementation returns price after discount
- Function name is ambiguous
Determination: Ambiguous Recommendation: Check documentation or clarify intended behavior
Example 2: Implementation Bug
Scenario: Test expects validateEmail("user@example.com") to return true, but it returns false
Analysis:
- Test provides a valid email format
- Implementation regex is missing support for dots in domain
- Other valid emails also fail
Determination: Implementation Bug Recommendation: Fix the regex to properly validate email addresses per RFC standards
Example 3: Test Bug
Scenario: Test expects divide(10, 0) to return 0, but it throws an error
Analysis:
- Test assumes division by zero returns 0
- Implementation throws DivisionByZeroError
- Standard mathematical behavior is to treat as undefined/error
Determination: Test Bug Recommendation: Update test to expect an error, not 0
Output Format
For each failing test, provide:
Test: [test name/description]
Failure: [what failed and how]
Investigation:
- Test expects: [expected behavior]
- Implementation does: [actual behavior]
- Root cause: [why they differ]
Determination: [Test Bug | Implementation Bug | Ambiguous]
Recommendation:
[Specific fix to either test or implementation]
Key Principles
- NEVER automatically assume the test is wrong
- ALWAYS consider that the test might have found a real bug
- When uncertain, lean toward investigating the implementation
- Tests are often your specification - they define expected behavior
- A failing test is a gift - it's either catching a bug or clarifying requirements
Related Skills
- test-failure-mindset: Set investigative approach for session
- comprehensive-test-review: Full test suite review
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
ccc
This skill should be used when code search is needed (whether explicitly requested or as part of completing a task), when indexing the codebase after changes, or when the user asks about ccc, cocoindex-code, or the codebase index. Trigger phrases include 'search the codebase', 'find code related to', 'update the index', 'ccc', 'cocoindex-code'.
agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
delegate
Quick delegation template for sub-agent prompts. Use when assigning work to a sub-agent, before invoking the Agent tool, or when preparing prompts for specialized agents. Provides the WHERE-WHAT-WHY framework. For comprehensive delegation guidance, activate the agent-orchestration how-to-delegate skill.
swarm-spawning
Spawn agents and teammates in Claude Code swarms. Use when choosing between subagents vs teammates, selecting agent types (Explore, Plan, general-purpose, plugin agents), configuring spawn backends (in-process, tmux, iterm2), or setting environment variables for spawned agents.
knowledge-explorer
Manage the research/ knowledge base (KB) of tool and library research entries. Use when browsing KB topics, adding new research entries, updating existing entries with dated revisions, fetching GitHub repo metadata into a draft KB entry, or migrating old-format entries to skill-spec frontmatter. Triggers on tasks like "what do we have on X", "add this to the KB", "update the KB entry for Y", "fetch github info for owner/repo", or "migrate old entries".
design-anti-patterns
Enforce anti-AI UI design rules based on the Uncodixfy methodology. Use when generating HTML, CSS, React, Vue, Svelte, or any frontend UI code. Prevents "Codex UI" — the generic AI aesthetic of soft gradients, floating panels, oversized rounded corners, glassmorphism, hero sections in dashboards, and decorative copy. Applies constraints from Linear/Raycast/Stripe/GitHub design philosophy: functional, honest, human-designed interfaces. Triggers on: UI generation, dashboard building, frontend component creation, CSS styling, landing page design, or any task producing visual interface code.
Didn't find tool you were looking for?