- name
- system-log-analytics
- description
- 系统日志分析技能，基于 Google SRE 框架对特定时间段的日志进行检查、总结系统运行情况、分析主要报错异常，并给出改善意见。使用场景：需要检查系统日志、分享运行总结、排查异常问题、获取优化建议。System log analytics skill, based on Google SRE framework, checks logs for specific time periods, summarizes system operation, analyzes major errors and exceptions, and provides improvement suggestions. Usage scenarios: need to check system logs, share operation summary, troubleshoot exceptions, get optimization suggestions.
System Log Analytics | 系统日志分析
English | 中文
English
Overview
This skill provides a systematic log analysis workflow based on Google SRE (Site Reliability Engineering) framework.
Trigger Conditions: Use this skill when:
- User asks to "check logs"
- User asks to "analyze system operation"
- User asks to "summarize errors"
- User asks to "share a log report"
- Need to troubleshoot abnormal system problems
Core Capabilities
1. Time Range Filtering
Support filtering logs by the following methods:
- Specify absolute time range (
YYYY-MM-DDtoYYYY-MM-DD) - Relative time range (today, yesterday, last 3 days, last week, last hour, etc.)
- Read by line offset for large log files
2. Exception Classification and Aggregation
Classify exceptions based on SRE best practices:
- Availability exceptions: Service outage, 5xx errors, connection timeout
- Latency exceptions: Slow queries, high request latency
- Resource exceptions: High CPU/memory usage, disk full, OOM
- Rate limiting & degradation: Circuit breaker triggered, traffic throttled
- Dependency exceptions: Third-party service call failure, database connection error
3. Health Scoring
Give a 1-5 system health score based on error rate and exception frequency:
- 5 points: Running well, no serious exceptions
- 4 points: Minor issues exist, does not affect overall availability
- 3 points: Moderate exceptions exist, needs attention
- 2 points: Serious exceptions exist, affects partial services
- 1 point: Service unavailable, needs immediate handling
4. Improvement Suggestions
Provide suggestions based on Google SRE principles:
- Short-term measures: Emergency handling plan
- Mid-term measures: Monitoring and alert optimization, capacity planning adjustment
- Long-term measures: Architecture optimization, redundancy design, error budget adjustment
Workflow
Step 1: Get Logs
- Determine the log file path (system logs are usually in
/var/log/, application logs are determined by deployment location) - Filter log content according to time range
- Use grep/awk for timestamp filtering (logic description only) - For large log files, use tail/head for segmented reading
Step 2: Structured Analysis
Analysis dimensions based on Google SRE framework:
| Analysis Dimension | Check Content |
|---|---|
| Error Rate | Proportion of error logs in total logs |
| Error Type Distribution | Aggregate statistics by error type |
| Error Timing | Time distribution of errors, whether sudden |
| Resource Usage | Whether resource exhaustion exists |
| Dependency Status | Whether it is caused by external dependency failure |
Step 3: Exception Aggregation
Merge similar exceptions to avoid duplicate reporting:
- Group by error keyword
- Count the number of occurrences of each group of exceptions
- Sort by impact (descending order of severity)
Step 4: Generate Report
Output structured report including:
- Analysis Overview: Analysis time range, log file, data volume
- Health Score: Overall health score
- Operation Summary: Overview of normal operation
- Exception Details: Sorted list of exceptions by severity
- Improvement Suggestions: Short-term/mid-term/long-term suggestions
See references/report-template.md for reference output template.
Step 5: Share Report
According to user needs:
- Output the report directly in the conversation
- If you need to save, you can export it as a Markdown file
- Can be further created as a Feishu cloud document for sharing
Filtering Logic Description
Below is the logic description for common log filtering operations, no actual scripts included:
1. Filter by Time Range
Logic Steps:
1. Input: log_file_path, start_time, end_time
2. Initialize empty result list
3. For each line in log_file:
a. Extract timestamp string from the line
b. Parse timestamp to datetime object
c. If start_time <= datetime <= end_time:
i. Add line to result list
4. Output: result list2. Extract Error Logs
Logic Steps:
1. Input: log_lines
2. Initialize empty error list
3. Define error keywords: ["ERROR", "FATAL", "SEVERE", "Exception", "Error:"]
4. For each line in log_lines:
a. If any keyword matches the line:
i. Add line to error list
5. Output: error list, error_count = len(error_list)3. Aggregate Exceptions by Keyword
Logic Steps:
1. Input: error_lines
2. Initialize empty aggregation dictionary
3. For each line in error_lines:
a. Extract error type keyword from line (e.g., OOM, connection refused, timeout)
b. If keyword exists in aggregation:
i. aggregation[keyword].count += 1
ii. Add line to aggregation[keyword].samples
c. Else:
i. Create new entry in aggregation with count = 1, samples = [line]
4. Sort aggregation by count descending (or by severity)
5. Output: sorted aggregation result中文
概述
本技能基于 Google SRE (Site Reliability Engineering) 框架，提供系统化的日志分析工作流。
触发条件： 当以下情况时使用本技能：
- 用户要求「检查日志」
- 用户要求「分析系统运行情况」
- 用户要求「总结报错」
- 用户要求「分享日志报告」
- 需要排查系统异常问题
核心能力
1. 时间范围过滤
支持按以下方式筛选日志：
- 指定绝对时间范围（
YYYY-MM-DD到YYYY-MM-DD） - 相对时间范围（今天、昨天、近 3 天、近一周、近一小时等）
- 对于大日志文件，按行数偏移读取
2. 异常分类与聚合
基于 SRE 最佳实践对异常进行分类：
- 可用性异常：服务宕机、5xx 错误、连接超时
- 延迟异常：慢查询、请求耗时过高
- 资源异常：CPU/内存使用率过高、磁盘满、OOM
- 限流降级：触发熔断、流量被限流
- 依赖异常：第三方服务调用失败、数据库连接错误
3. 运行状况评分
基于错误率、异常频次给出 1-5 的系统健康评分：
- 5 分：运行良好，无严重异常
- 4 分：存在轻微问题，不影响整体可用性
- 3 分：存在中度异常，需要关注
- 2 分：存在严重异常，影响部分服务
- 1 分：服务不可用，需要立即处理
4. 改善建议输出
根据分析结果，结合 Google SRE 原则给出建议：
- 短期措施：紧急处理方案
- 中期措施：监控告警优化、容量规划调整
- 长期措施：架构优化、冗余设计、错误预算调整
工作流
步骤 1: 获取日志
- 确定日志文件路径（系统日志通常在
/var/log/，应用日志根据部署位置确定） - 根据时间范围过滤日志内容
- 使用 grep/awk 进行时间戳筛选（仅逻辑描述） - 对于大日志文件，使用 tail/head 分段读取
步骤 2: 结构化分析
基于 Google SRE 框架分析维度：
| 分析维度 | 检查内容 |
|---|---|
| 错误率 | 错误日志占总日志比例 |
| 错误类型分布 | 按错误类型聚合统计 |
| 错误时序 | 错误发生的时间分布，是否突发 |
| 资源使用 | 是否存在资源耗尽情况 |
| 依赖状态 | 是否因外部依赖故障引发 |
步骤 3: 异常聚合
将同类异常合并，避免重复报告：
- 按错误关键词分组聚合
- 统计每组异常发生次数
- 按影响程度排序（严重程度降序）
步骤 4: 生成报告
输出结构化报告，包含：
- 分析概览：分析时间范围、日志文件、数据量
- 健康评分：整体健康状况评分
- 运行总结：正常运行情况概述
- 异常详情：按严重程度排序的异常列表
- 改善建议：分短期/中期/长期给出建议
参考输出模板请见 references/report-template.md
步骤 5: 分享报告
根据用户需求：
- 直接在对话中输出报告
- 如果需要保存，可以导出为 Markdown 文件
- 可进一步创建为飞书云文档分享
过滤逻辑描述
以下是常见日志过滤操作的逻辑描述，不包含实际脚本：
1. 按时间范围过滤
逻辑步骤:
1. 输入: 日志文件路径, 开始时间, 结束时间
2. 初始化空结果列表
3. 遍历日志文件每一行:
a. 从行中提取时间戳字符串
b. 将时间戳解析为日期时间对象
c. 如果 开始时间 <= 日期时间 <= 结束时间:
i. 将行添加到结果列表
4. 输出: 结果列表2. 提取错误日志
逻辑步骤:
1. 输入: 日志行列表
2. 初始化空错误列表
3. 定义错误关键词: ["ERROR", "FATAL", "SEVERE", "Exception", "Error:"]
4. 遍历日志每一行:
a. 如果任何关键词匹配该行:
i. 将行添加到错误列表
5. 输出: 错误列表, 错误计数 = len(错误列表)3. 按关键词聚合异常
逻辑步骤:
1. 输入: 错误行列表
2. 初始化空聚合字典
3. 遍历每个错误行:
a. 从行中提取错误类型关键词 (例如: OOM, connection refused, timeout)
b. 如果关键词已在聚合中:
i. 聚合[关键词].count += 1
ii. 将行添加到聚合[关键词].samples
c. 否则:
i. 在聚合中创建新条目，count = 1, samples = [line]
4. 按计数降序排序聚合 (或按严重程度)
5. 输出: 排序后的聚合结果