Log Analysis

When to use this skill

Error debugging: analyze the root cause of application errors
Performance analysis: analyze response times and throughput
Security audit: detect anomalous access patterns
Incident response: investigate the root cause during an outage

Instructions

Step 1: Locate Log Files

# Common log locations
/var/log/                    # System logs
/var/log/nginx/              # Nginx logs
/var/log/apache2/            # Apache logs
./logs/                      # Application logs

Step 2: Search for Error Patterns

Common error search:

# Search ERROR-level logs
grep -i "error\|exception\|fail" application.log

# Recent errors (last 100 lines)
tail -100 application.log | grep -i error

# Errors with timestamps
grep -E "^\[.*ERROR" application.log

HTTP error codes:

# 5xx server errors
grep -E "HTTP/[0-9.]+ 5[0-9]{2}" access.log

# 4xx client errors
grep -E "HTTP/[0-9.]+ 4[0-9]{2}" access.log

# Specific error code
grep "HTTP/1.1\" 500" access.log

Step 3: Pattern Analysis

Time-based analysis:

# Error count by time window
grep -i error application.log | cut -d' ' -f1,2 | sort | uniq -c | sort -rn

# Logs for a specific time window
grep "2025-01-05 14:" application.log

IP-based analysis:

# Request count by IP
awk '{print $1}' access.log | sort | uniq -c | sort -rn | head -20

# Activity for a specific IP
grep "192.168.1.100" access.log

Step 4: Performance Analysis

Response time analysis:

# Extract response times from Nginx logs
awk '{print $NF}' access.log | sort -n | tail -20

# Slow requests (>= 1 second)
awk '$NF > 1.0 {print $0}' access.log

Traffic volume analysis:

# Requests per minute
awk '{print $4}' access.log | cut -d: -f1,2,3 | uniq -c

# Requests per endpoint
awk '{print $7}' access.log | sort | uniq -c | sort -rn | head -20

Step 5: Security Analysis

Suspicious patterns:

# SQL injection attempts
grep -iE "(union|select|insert|update|delete|drop).*--" access.log

# XSS attempts
grep -iE "<script|javascript:|onerror=" access.log

# Directory traversal
grep -E "\.\./" access.log

# Brute force attack
grep -E "POST.*/login" access.log | awk '{print $1}' | sort | uniq -c | sort -rn

Output format

Analysis report structure

# Log analysis report

## Summary
- Analysis window: YYYY-MM-DD HH:MM ~ YYYY-MM-DD HH:MM
- Total log lines: X,XXX
- Error count: XXX
- Warning count: XXX

## Error analysis
| Error type | Occurrences | Last seen |
|----------|-----------|----------|
| Error A  | 150       | 2025-01-05 14:30 |
| Error B  | 45        | 2025-01-05 14:25 |

## Recommended actions
1. [Action 1]
2. [Action 2]

Best practices

Set time range: clearly define the time window to analyze
Save patterns: script common grep patterns
Check context: review logs around the error too (-A, -B options)
Log rotation: search compressed logs with zgrep as well

Constraints

Required Rules (MUST)

Perform read-only operations only
Mask sensitive information (passwords, tokens)

Prohibited (MUST NOT)

Do not modify log files
Do not expose sensitive information externally

References

Examples

Example 1: Basic usage

Example 2: Advanced usage

Quick Start

Unity3D 콘솔 로그 분석 시나리오 (unity-mcp: read_console 사용):

1. unity-mcp로 콘솔 출력 수집
   unity-mcp: read_console → Error/Warning/Exception 목록

2. 에러 패턴 분류
   - NullReferenceException: 컴포넌트 참조 누락
   - MissingReferenceException: 오브젝트 파괴 후 접근
   - IndexOutOfRangeException: 배열 범위 초과

3. 분석 실행
   Grep: "NullReferenceException" in read_console output
   → 발생 빈도 및 호출 스택 정리

4. 수정 우선순위 결정
   - Critical (게임 크래시): 즉시 수정
   - High (기능 오류): 이번 스프린트 내 처리
   - Low (경고): 다음 스프린트로 이관

5. 스토리 생성
   → bmad-gds-create-story 입력으로 전달

Workflow Context

OMU VERIFY loop에서 unity-mcp: read_console 출력을 분석합니다.

트리거: OMU Workflow 2 (C# 구현), Workflow 5 (성능 최적화) VERIFY 단계
입력: unity-mcp read_console 출력 (Error/Warning/Exception)
출력: 에러 분류 + 수정 우선순위 + bmad-gds-create-story 입력
연동: omu → unity-mcp → log-analysis → bmad-gds-create-story

log-analysis