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    python bug hunter

    1

    Evidence-driven Python debugger focused on minimal patches and regression testing.

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    python bug hunter

    python bug hunter

    Example session with this skill installed

    I'm getting a KeyError in my data processor when the 'tags' field is missing from the JSON input, even though I have a default value set. Here is the traceback and the snippet.

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    • Compiled the python bug hunter
    • Generated the document

    I have analyzed the traceback and the provided snippet. The issue stems from an explicit dictionary access before the default check is reached.

    Python Bug Hunter Report

    Failure Summary

    A KeyError: 'tags' occurs in processor.py at line 42. The code attempts to access the key directly to check for length before the .get() default logic is applied.

    Root Cause

    StatusCauseEvidenceLocation
    CONFIRMEDEager key accessTraceback points to len(data['tags']) before safety check.processor.py:42
    LIKELYMissing validationInput schema allows empty or missing keys.schemas.py:12

    Minimal Fix

    
# processor.py
def process_data(data):
    # Change: data['tags'] -> data.get('tags', [])
    tags = data.get('tags', [])
    if not tags:
        return None
    # ... rest of function

    Regression Test

    import pytest
from processor import process_data

def test_missing_tags_key():
    """Ensure missing tags field does not raise KeyError."""
    input_data = {"id": 123, "content": "test"}
    assert process_data(input_data) is None

def test_null_tags_value():
    """Ensure null tags field is handled gracefully."""
    input_data = {"id": 124, "tags": None}
    # This identifies a secondary risk if .get(key, []) returns None
    with pytest.raises(TypeError):
        process_data(input_data)

    Verification

    NOT RUN (Static analysis only)

    Remaining Risk

    The test_null_tags_value reveals that if the key exists but is explicitly None, the len() check will still fail. A more robust fix would be data.get('tags') or [].

    Next steps

    1. Apply the suggested change to processor.py.
    2. Run the provided pytest cases to verify the fix.
    3. Update the input schema to enforce non-null values if None is not valid.

    python-bug-hunter.pdf

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    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Diagnose intermittent async/await race conditions.Fix KeyError or AttributeError crashes in data pipelines.Identify and patch logic errors in complex loops or branches.Generate minimal patches that don't break existing API contracts.

    About this skill

    Debugging Python applications often involves chasing ghosts or applying broad refactors that introduce new regressions. This skill enforces an evidence-driven workflow to isolate the root cause and apply the smallest possible patch.

    What it does

    • Root Cause Isolation identifies the specific logic, state, or dependency failure using a traceback-to-source mapping.
    • Evidence Labeling categorizes every finding as CONFIRMED, LIKELY, or NEEDS VERIFICATION to prevent debugging by guesswork.
    • Minimal Patching generates the smallest safe code change required to fix the issue without unrelated refactoring.
    • Regression Testing creates a specific test case for the failure and the nearest logical edge case.
    • Risk Assessment documents potential side effects or remaining edge cases based only on supported evidence.

    How it works

    1. Evidence Collection where the user provides logs, tracebacks, or failing test outputs.
    2. Trace Mapping to follow the execution path from the input to the point of failure.
    3. Hypothesis Verification where candidate causes are checked against actual code state and dependency behavior.
    4. Patch Generation producing a justified, minimal fix and a corresponding regression test.

    Frameworks & tools

    Works with any Python stack including FastAPI, Django, Flask, Pytest, and Unittest. Handles async/await concurrency, type-hinting issues, and third-party dependency conflicts.

    Why this beats prompting it yourself

    Generic prompts often hallucinate stack frames or suggest massive refactors for simple logic errors. This skill forces the agent to prove its findings with evidence and strictly limits changes to the minimal justified patch, preserving your existing architecture.

    Use cases

    • Debugging intermittent race conditions in async tasks.
    • Resolving NoneType errors or unexpected boundary conditions in data pipelines.
    • Fixing broken dependency assumptions after package updates.
    • Creating targeted regression tests for legacy code bugs.

    Known limitations

    Does not perform live execution unless your environment supports a Python interpreter tool. Cannot verify fixes for external infrastructure or closed-source API failures.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

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    Verified clean 4 days ago

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