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 chaterm/terminal-skills --skill log-analysisgit clone --depth 1 https://github.com/chaterm/terminal-skillsWrote 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/chaterm/terminal-skills/log-analysis)<a href="https://agentmods.dev/skills/chaterm/terminal-skills/log-analysis"><img src="https://agentmods.dev/badge/skills/chaterm/terminal-skills/log-analysis/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/chaterm/terminal-skills/log-analysis"><img src="https://agentmods.dev/badge/skills/chaterm/terminal-skills/log-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00007 | $0.01941 |
| Opus 5 | $0.00003 | $0.00971 |
| Sonnet 5 | $0.00001 | $0.00388 |
| Haiku 4.5 | $0.00001 | $0.00194 |
Grade A, and why
log-analysis 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
日志分析与处理
概述
日志聚合、分析工具、告警配置等技能。
日志查看
基础命令
# 实时查看
tail -f /var/log/syslog
tail -f /var/log/nginx/access.log
# 多文件同时查看
tail -f /var/log/nginx/*.log
multitail /var/log/nginx/access.log /var/log/nginx/error.log
# 查看最后N行
tail -n 100 /var/log/syslog
# 查看开头
head -n 100 /var/log/syslog
# 分页查看
less /var/log/syslog
less +F /var/log/syslog # 类似 tail -f
按时间过滤
# 使用 sed 按时间范围
sed -n '/2024-01-15 10:00/,/2024-01-15 11:00/p' /var/log/app.log
# 使用 awk
awk '/2024-01-15 10:/ && /2024-01-15 11:/' /var/log/app.log
# 使用 journalctl
journalctl --since "2024-01-15 10:00" --until "2024-01-15 11:00"
journalctl --since "1 hour ago"
journalctl --since today
文本搜索
grep
# 基础搜索
grep "error" /var/log/syslog
grep -i "error" /var/log/syslog # 忽略大小写
grep -r "error" /var/log/ # 递归搜索
# 正则表达式
grep -E "error|warning" /var/log/syslog
grep -P "\d{4}-\d{2}-\d{2}" /var/log/syslog # Perl 正则
# 上下文
grep -A 3 "error" /var/log/syslog # 后3行
grep -B 3 "error" /var/log/syslog # 前3行
grep -C 3 "error" /var/log/syslog # 前后3行
# 统计
grep -c "error" /var/log/syslog # 计数
grep -l "error" /var/log/*.log # 只显示文件名
# 排除
grep -v "debug" /var/log/syslog # 排除包含 debug 的行
ripgrep (rg)
# 更快的搜索
rg "error" /var/log/
rg -i "error" /var/log/ # 忽略大小写
rg -C 3 "error" /var/log/ # 上下文
rg --type log "error" # 按文件类型
文本处理
awk
# 打印特定列
awk '{print $1, $4}' /var/log/nginx/access.log
# 条件过滤
awk '$9 == 500' /var/log/nginx/access.log
awk '$9 >= 400 && $9 < 500' /var/log/nginx/access.log
# 统计
awk '{sum += $10} END {print sum}' /var/log/nginx/access.log
awk '{count[$9]++} END {for (c in count) print c, count[c]}' /var/log/nginx/access.log
# 自定义分隔符
awk -F: '{print $1}' /etc/passwd
awk -F'[ :]' '{print $1, $2}' /var/log/syslog
sed
# 替换
sed 's/old/new/g' file.log
sed -i 's/old/new/g' file.log # 原地修改
# 删除行
sed '/pattern/d' file.log
sed '1,10d' file.log # 删除前10行
# 提取行
sed -n '10,20p' file.log # 打印10-20行
sed -n '/start/,/end/p' file.log # 打印匹配范围
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.
- 9d ago First seen · 251 lines · 7 tokens per session scan A 9115c6bacaa9
log-analysis is a skill published in the GitHub repository chaterm/terminal-skills (58 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 7 tokens to every session and 1,941 once invoked, about $0.0000 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.
Other skills, from other repositories
log-diagnostic
A log-analysis tool that detects JSON, syslog, and Nginx formats and reports grouped errors, occurrence counts, severity levels, and time distribution. It can filter entries by level or time range and export JSON.
performance-analysis
Performance analysis, bottleneck detection, and optimization recommendations. Use when profiling slow code or systems, hunting a performance regression, or producing an optimization plan with measurable targets.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
phoenix-observability
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…