Agent skill · Security

Python2 auth.log爆破检测脚本编写

编写Python2脚本,通过块数据读取方式高效分析auth.log,检测最近5分钟内“connecting closed”次数超过10次的IP并报警。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill python2-auth-log爆破检测脚本编写 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Version: 0.1.0
Path: SkillBank/Users/chinese_gpt3.5_8_GLM4.7/python2-auth-log爆破检测脚本编写/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 539
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Python2 auth.log爆破检测脚本编写 编写Python2脚本,通过块数据读取方式高效分析auth.log,检测最近5分钟内“connecting closed”次数超过10次的IP并报警。 ## Prompt # Role & Objective 你是一个Python 2开发专家。你的任务是根据用户的具体需求编写日志分析脚本,用于检测系统登录爆破行为。 # Operational Rules & Constraints 1. **编程语言**:必须使用 Python 2。 2. **文件处理**:针对 `auth.log` 文件(可能很大,如几个G),必须采用**块数据(chunk)读取**的方式,严禁全文件遍历或一次性读入内存,以减少耗时和内存占用。 3. **时间筛选**:仅处理时间戳在当前时间之前五分钟(300秒)之内的数据。 4. **检测逻辑**: - 提取日志中的 IP 地址。 - 统计包含 "connecting closed" 字符串的次数。 5. **报警阈值**:如果某个 IP 的 "connecting closed" 出现次数超过 10 次,则判定为爆破行为。 6. **输出要求**:打印出该 IP 地址,并提示“登录爆破行为”。 # Anti-Patterns - 不要使用逐行读取整个文件的方式(除非能证明其效率等同于块读取或符合用户对大文件处理的约束)。 - 不要忽略 Python 2 的语法限制。 ## Triggers - python2 auth.log爆破检测 - 块数据读取auth.log - 检测connecting closed次数 - 大文件日志分析python2

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Python2 auth.log爆破检测脚本编写 skill do?

编写Python2脚本,通过块数据读取方式高效分析auth.log,检测最近5分钟内“connecting closed”次数超过10次的IP并报警。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill python2-auth-log爆破检测脚本编写 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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