Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add laolaoshiren/claude-code-skills-zh --skill log-analyzergit clone --depth 1 https://github.com/laolaoshiren/claude-code-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/laolaoshiren/claude-code-skills-zh/log-analyzer)<a href="https://agentmods.dev/skills/laolaoshiren/claude-code-skills-zh/log-analyzer"><img src="https://agentmods.dev/badge/skills/laolaoshiren/claude-code-skills-zh/log-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/laolaoshiren/claude-code-skills-zh/log-analyzer"><img src="https://agentmods.dev/badge/skills/laolaoshiren/claude-code-skills-zh/log-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00749 |
| Opus 5 | $0.00012 | $0.00375 |
| Sonnet 5 | $0.00005 | $0.00150 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
log-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
📋 log-analyzer — 日志分析助手
触发条件
当用户要求以下操作时激活此技能：
- 分析应用日志文件（.log, .txt, journalctl 输出等）
- 排查线上问题和异常
- 从日志中提取统计信息
- 生成日志分析报告
工作流程
1. 日志格式识别
自动检测日志格式：
- 结构化: JSON logs, Logfmt
- 常见格式: Nginx/Apache access log, Spring Boot, Django, Rails
- 系统日志: syslog, journalctl, Windows Event Log
- 自定义格式: 根据正则提取字段
2. 日志预处理
- 按时间范围过滤（如「最近 1 小时」「今天」）
- 按日志级别筛选（ERROR, WARN, INFO, DEBUG）
- 按来源/模块/服务过滤
- 去重和聚合相似错误
3. 异常模式检测
- 错误聚类: 将相似错误归为一组，避免重复分析
- 频率分析: 识别高频错误和突发异常
- 时间关联: 发现错误是否与特定事件（部署、流量高峰）相关
- 因果链追踪: 从根因错误到最终表现
4. 关键信息提取
- 错误堆栈分析（定位源码位置）
- HTTP 状态码分布（4xx/5xx 比例）
- 响应时间统计（P50/P95/P99）
- 内存/CPU 异常指标
5. 生成分析报告
## 📊 日志分析报告
### 概览
- 时间范围: 2024-01-15 10:00 ~ 12:00
- 总日志量: 12,847 条
- 错误数: 234 (1.8%)
- 警告数: 567 (4.4%)
### Top 5 异常
| 排名 | 错误类型 | 次数 | 首次出现 | 最近出现 |
|------|---------|------|---------|---------|
| 1 | NullPointerException | 89 | 10:15 | 11:42 |
| 2 | ConnectionTimeout | 67 | 10:23 | 11:58 |
### 根因分析
最可能的原因：数据库连接池耗尽（线索：连续出现 ConnectionTimeout 后跟大量 NullPointerException）
### 建议
1. 检查数据库连接池配置（当前可能 maxActive 太小）
2. 排查是否存在连接泄漏
3. 添加连接池监控告警
支持的日志来源
- 文件系统日志（*.log）
- Docker/Kubernetes 容器日志
- journalctl 系统日志
- AWS CloudWatch / GCP Logging
- ELK Stack (Elasticsearch 查询结果)
- 应用直接输出的文本
注意事项
- 敏感信息脱敏（密码、token、手机号等）
- 大文件分块处理，避免内存溢出
- 时区统一处理
- 不要臆造日志中不存在的信息
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 80 lines · 24 tokens per session scan A edc38a3a85f9
log-analyzer is a skill published in the GitHub repository laolaoshiren/claude-code-skills-zh (828 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 749 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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