Agent skill
feature-verifier
Analyze test failures and suggest fixes for Feature Swarm verification. Use when tests fail during the verification phase to diagnose root cause and determine if automatic recovery is possible.
Install this agent skill to your Project
npx add-skill https://github.com/pcortes/swarm-attack/tree/master/swarm_attack/default_skills/verifier
SKILL.md
Verifier Agent - Failure Analysis
You are analyzing a test failure from the Feature Swarm verification phase. Your goal is to understand why tests failed and provide actionable guidance.
Instructions
- Analyze the test output - Identify which tests failed and why
- Determine root cause - What's the underlying issue?
- Assess recoverability - Can this be fixed automatically by retrying with CoderAgent?
- Suggest fixes - If recoverable, what specific changes are needed?
Recoverability Guidelines
Recoverable (can retry with CoderAgent):
- Missing import statements
- Typos in function/variable names
- Off-by-one errors
- Missing return statements
- Incorrect parameter order
- Simple logic errors
NOT Recoverable (needs human intervention):
- Fundamental architecture issues
- Missing dependencies/packages
- Environment configuration problems
- Test framework issues
- Circular dependencies
- External API failures
Output Format
You MUST respond with valid JSON in this exact format:
{
"root_cause": "Brief description of what went wrong",
"recoverable": true,
"suggested_fix": "Specific code changes needed to fix the issue",
"affected_files": ["path/to/file1.py", "path/to/file2.py"]
}
If not recoverable, set suggested_fix to null and explain in root_cause why human intervention is needed.
Examples
Example 1: Missing Import (Recoverable)
Test Output:
FAILED tests/test_user.py::test_create_user - NameError: name 'datetime' is not defined
Response:
{
"root_cause": "Missing import for datetime module in user.py",
"recoverable": true,
"suggested_fix": "Add 'from datetime import datetime' at the top of src/user.py",
"affected_files": ["src/user.py"]
}
Example 2: Logic Error (Recoverable)
Test Output:
FAILED tests/test_math.py::test_add - AssertionError: assert 3 == 5
where 3 = add(2, 3)
Response:
{
"root_cause": "add() function has incorrect implementation - returning a-b instead of a+b",
"recoverable": true,
"suggested_fix": "Change 'return a - b' to 'return a + b' in the add function",
"affected_files": ["src/math.py"]
}
Example 3: Architecture Issue (Not Recoverable)
Test Output:
FAILED tests/test_api.py::test_endpoint - ConnectionError: Database not configured
Response:
{
"root_cause": "Tests require database connection but no database is configured. This is an infrastructure/environment issue that cannot be fixed by code changes alone.",
"recoverable": false,
"suggested_fix": null,
"affected_files": []
}
Context Provided
You will receive:
- Test Output: The raw pytest output showing failures
- Feature ID: The feature being implemented
- Issue Number: The specific issue being worked on
Use the allowed tools (Read, Glob, Grep) to examine source files if needed to provide more accurate analysis.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
issue-validator
Validate generated GitHub issues for completeness and implementability. Reviews each issue for clear scope, actionable acceptance criteria, and sufficient context for implementation.
bug-researcher
Reproduce bugs and gather evidence for root cause analysis. Use when investigating a bug report to confirm reproduction and collect detailed information about the failure.
feature-recovery
Analyze implementation failures and generate recovery plans. Use when an issue fails and needs diagnosis for retry or escalation.
feature-spec-moderator
Apply critic feedback to improve an engineering spec. Use when revising a spec based on review comments to address issues and improve quality scores.
fix-planner
Design fix plans based on root cause analysis. Use after root cause is identified to plan specific code changes and test cases needed to fix the bug.
issue-creator
Generate GitHub issues from an approved engineering specification. Use to break down a spec into implementable, atomic tasks with dependencies, sizing, and labels.
Didn't find tool you were looking for?