debug（调试）

Debug

Systematic debugging for errors, test failures, unexpected behavior, and production issues.

Usage

/debug [issue] [--logs] [--correlate] [--trace] [--type bug|build|perf|deploy]

Options

When to Use

• Error messages or stack traces appear
• Tests are failing
• Code behaves unexpectedly
• User says "it's broken" or "not working"
• Production errors need investigation (--logs)
• Need to correlate errors across systems (--correlate)
• Deep stack analysis required (--trace)

Debugging Process

1. Capture - Get error message, stack trace, and reproduction steps
2. Isolate - Narrow down the failure location
3. Hypothesize - Form theories about the cause
4. Test - Validate hypotheses with evidence
5. Fix - Implement minimal fix
6. Verify - Confirm solution works

Investigation Steps

# Check recent changes that might have caused the issue
git log --oneline -10
git diff HEAD~3

# Find error patterns in logs
grep -r "error\|Error\|ERROR" logs/ 2>/dev/null | tail -20

# Check test output
npm test 2>&1 | tail -50  # or pytest, cargo test, etc.

Log Analysis (--logs)

Find Errors

# Recent errors with context
grep -B 5 -A 10 "ERROR" /var/log/app.log

# Count by error type
grep -oE "Error: [^:]*" app.log | sort | uniq -c | sort -rn

# Errors in time range
awk '/2024-01-15 14:/ && /ERROR/' app.log

# Find repeated errors
grep "ERROR" app.log | cut -d']' -f2 | sort | uniq -c | sort -rn | head -20

# Find error spikes
grep "ERROR" app.log | cut -d' ' -f1-2 | uniq -c | sort -rn

Common Patterns

Correlation Queries (--correlate)

-- Errors by endpoint
SELECT endpoint, count(*) as errors
FROM logs
WHERE level = 'ERROR' AND time > NOW() - INTERVAL '1 hour'
GROUP BY endpoint ORDER BY errors DESC;

-- Error rate over time
SELECT
  date_trunc('minute', time) as minute,
  count(*) filter (where level = 'ERROR') as errors,
  count(*) as total
FROM logs
WHERE time > NOW() - INTERVAL '1 hour'
GROUP BY minute ORDER BY minute;

-- Correlate request IDs across services
SELECT service, message, time
FROM logs
WHERE request_id = 'req-12345'
ORDER BY time;

Stack Trace Analysis (--trace)

Parse Stack Traces

import re

def parse_stack_trace(log_content: str) -> list[dict]:
    pattern = r'(?P<exception>\w+Error|\w+Exception): (?P<message>.*?)\n(?P<trace>(?:\s+at .+\n)+)'
    traces = []
    for match in re.finditer(pattern, log_content):
        traces.append({
            'type': match.group('exception'),
            'message': match.group('message'),
            'trace': match.group('trace').strip().split('\n')
        })
    return traces

Investigation Checklist

1. Capture - Get full error message and stack trace
2. Timestamp - When did it start?
3. Frequency - How often? Increasing?
4. Scope - All users or specific?
5. Changes - Recent deployments?
6. Dependencies - External services affected?

Output Format

## Debug Report

**Issue:** [Brief description]
**Root Cause:** [What's actually wrong]

### Evidence

- [Finding 1]
- [Finding 2]

### Fix

[Code or configuration change]

### Verification

[How to confirm the fix works]

### Prevention

[How to prevent this in the future]

Examples

Input: "TypeError: Cannot read property 'map' of undefined" Action: Trace the undefined value, find where data should be initialized, fix the source

Input: "Tests are failing" Action: Run tests, capture failures, analyze each failure, fix underlying issues

Input: /debug --logs "API returning 500 errors" Action: Search logs for 500 status, find stack traces, identify root cause, check error frequency

Input: /debug --correlate "intermittent failures" Action: Run correlation queries to find patterns, identify affected endpoints, correlate with events