AgentSkillsCN

debugging

在提出修复方案之前,若遇到任何 Bug、测试失败或意外行为,应先行进行根本原因调查。在尝试修复之前,必须先查明问题的根源。随意的修复不仅浪费时间,还可能引发新的 Bug。

SKILL.md
--- frontmatter
name: debugging
description: Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Requires root cause investigation before any fix attempts. Random fixes waste time and create new bugs.

Systematic Debugging

Random fixes waste time and create new bugs. Quick patches mask underlying issues.

Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.


The Iron Law

code
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes.


When to Use

Any technical issue:

  • Test failures
  • Bugs in production
  • Unexpected behavior
  • Performance problems
  • Build failures
  • Integration issues

Especially when:

  • Under time pressure (emergencies make guessing tempting)
  • "Just one quick fix" seems obvious
  • You've already tried multiple fixes
  • Previous fix didn't work

The Four Phases

Complete each phase before proceeding to the next.

Phase 1: Root Cause Investigation

BEFORE attempting ANY fix:

  1. Read Error Messages Carefully

    • Don't skip past errors or warnings
    • Read stack traces completely
    • Note line numbers, file paths, error codes
  2. Reproduce Consistently

    • Can you trigger it reliably?
    • What are the exact steps?
    • If not reproducible -> gather more data, don't guess
  3. Check Recent Changes

    • Git diff, recent commits
    • New dependencies, config changes
    • Environmental differences
  4. Gather Evidence in Multi-Component Systems

    code
    For EACH component boundary:
      - Log what data enters
      - Log what data exits
      - Verify environment/config propagation
    
    Run once to gather evidence showing WHERE it breaks
    THEN investigate that specific component
    
  5. Trace Data Flow

    • Where does bad value originate?
    • What called this with bad value?
    • Keep tracing up until you find the source
    • Fix at source, not at symptom

Phase 2: Pattern Analysis

  1. Find Working Examples

    • Locate similar working code in same codebase
    • What works that's similar to what's broken?
  2. Compare Against References

    • Read reference implementation COMPLETELY
    • Don't skim - read every line
  3. Identify Differences

    • What's different between working and broken?
    • List every difference, however small

Phase 3: Hypothesis and Testing

  1. Form Single Hypothesis

    • "I think X is the root cause because Y"
    • Be specific, not vague
  2. Test Minimally

    • Make the SMALLEST possible change
    • One variable at a time
    • Don't fix multiple things at once
  3. Verify Before Continuing

    • Did it work? Yes -> Phase 4
    • Didn't work? Form NEW hypothesis
    • DON'T add more fixes on top

Phase 4: Implementation

  1. Create Failing Test Case

    • Simplest possible reproduction
    • MUST have before fixing
  2. Implement Single Fix

    • Address the root cause
    • ONE change at a time
    • No "while I'm here" improvements
  3. Verify Fix

    • Test passes now?
    • No other tests broken?
  4. If 3+ Fixes Failed: Question Architecture

    Pattern indicating architectural problem:

    • Each fix reveals new problem in different place
    • Fixes require massive refactoring
    • Each fix creates new symptoms elsewhere

    STOP and question fundamentals before attempting more fixes


Python-Specific Debugging

One-Shot Evaluation for Inspection

bash
# Evaluate expressions
python -c "print(1 + 2)"
python -c "import json; print(json.dumps({'key': 'value'}))"

# Check types in project context
python -c "from starter.core import greet; print(type(greet('x')))"

Print Debugging

python
# Quick debug prints
print(f"DEBUG: {variable=}")  # Python 3.8+ self-documenting expression
print(f"DEBUG: type={type(value)}, value={value!r}")

# For complex objects
import pprint
pprint.pprint(complex_dict)

Remove debug prints before committing!

Using pdb

bash
# One-shot commands (non-interactive is preferred)
python -c "import module; breakpoint(); module.function()"

# In tests
pytest --pdb  # Drop into debugger on failure
pytest --pdb -x  # Stop at first failure and debug

Common Python Errors

ErrorLikely Cause
AttributeError: 'NoneType'Function returned None unexpectedly
TypeError: unsupported operandWrong types in operation
KeyErrorMissing dict key - use .get() or check membership
ImportError / ModuleNotFoundErrorMissing dependency or wrong import path
IndentationErrorMixed tabs/spaces or wrong indent level
RecursionErrorInfinite recursion - check base case
ValueErrorWrong value passed - check input validation

Type Checking Issues

bash
# Run mypy for type errors
uv run mypy src/ --show-error-codes
uv run mypy src/ --show-column-numbers

Red Flags - STOP and Return to Phase 1

If you catch yourself thinking:

  • "Quick fix for now, investigate later"
  • "Just try changing X and see if it works"
  • "It's probably X, let me fix that"
  • "I don't fully understand but this might work"
  • "Here are the main problems: [lists fixes without investigation]"
  • Proposing solutions before tracing data flow
  • "One more fix attempt" (when already tried 2+)

Common Rationalizations

ExcuseReality
"Issue is simple"Simple issues have root causes too
"Emergency, no time"Systematic is FASTER than thrashing
"Just try this first"First fix sets the pattern
"Multiple fixes saves time"Can't isolate what worked
"I see the problem"Seeing symptoms != understanding root cause
"One more attempt" (after 2+)3+ failures = architectural problem

Quick Reference

PhaseKey ActivitiesSuccess Criteria
1. Root CauseRead errors, reproduce, check changes, gather evidenceUnderstand WHAT and WHY
2. PatternFind working examples, compareIdentify differences
3. HypothesisForm theory, test minimallyConfirmed or new hypothesis
4. ImplementationCreate test, fix, verifyBug resolved, tests pass

Real-World Impact

ApproachTime to FixFirst-Time Fix RateNew Bugs
Systematic15-30 min95%Near zero
Random fixes2-3 hours40%Common