首页/数据分析/log-analysis
L

log-analysis

by @supercent-iov
4.5(425)

此技能用于日志分析,包括错误调试、性能分析、安全审计、合规性检查及容量规划,从日志数据中提取有价值的洞察。

elk-stacksplunklog-managementanomaly-detectiondata-visualizationGitHub
安装方式
npx skills add supercent-io/skills-template --skill log-analysis
compare_arrows

Before / After 效果对比

1
使用前

在没有系统日志分析方法时,面对海量日志文件,手动查找错误信息或性能瓶颈如同大海捞针,故障排查耗时且效率低下。

使用后

掌握日志分析技巧和工具(如ELK Stack, Splunk, grep命令),能够快速过滤、聚合和可视化日志数据,从而高效地发现错误、分析性能问题、检测安全异常,显著缩短故障响应时间。

SKILL.md

log-analysis

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 grep manual awk guide Log analysis best practices Examples Example 1: Basic usage Example 2: Advanced usageWeekly Installs10.3KRepositorysupercent-io/sk…templateGitHub Stars53First SeenJan 24, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled oncodex10.3Kgemini-cli10.3Kopencode10.3Kgithub-copilot10.2Kcursor10.2Kamp10.2K

用户评价 (0)

发表评价

效果
易用性
文档
兼容性

暂无评价

统计数据

安装量10.6K
评分4.5 / 5.0
版本
更新日期2026年5月9日
对比案例1 组

用户评分

4.5(425)
5
36%
4
49%
3
14%
2
1%
1
0%

为此 Skill 评分

0.0

兼容平台

🔧Claude Code
🔧OpenClaw
🔧OpenCode
🔧Codex
🔧Gemini CLI
🔧GitHub Copilot
🔧Amp
🔧Kimi CLI

时间线

创建2026年3月17日
最后更新2026年5月9日