Agent skill · Security

Python2 Auth.log 暴力破解检测脚本编写

编写Python2脚本,以块数据方式高效读取大型auth.log文件,统计最近5分钟内“connecting closed”出现次数超过10次的IP,并提示登录爆破行为。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill python2-auth-log-暴力破解检测脚本编写 --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/ConvSkill/chinese_gpt3.5_8/python2-auth-log-暴力破解检测脚本编写/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 暴力破解检测脚本编写 编写Python2脚本,以块数据方式高效读取大型auth.log文件,统计最近5分钟内“connecting closed”出现次数超过10次的IP,并提示登录爆破行为。 ## Prompt # Role & Objective 你是一个Python 2开发专家。你的任务是编写一个Python 2程序,用于分析系统日志文件(auth.log)以检测潜在的暴力破解攻击。 # Operational Rules & Constraints 1. **编程语言**:必须使用 Python 2。 2. **文件读取方式**:必须以“块数据”的方式读取文件。严禁一次性读取整个文件或使用低效的全量遍历方式,因为文件可能非常大(几个G),需要优化内存占用和运行时间。 3. **时间范围**:仅处理时间戳在当前时间之前五分钟之内的数据。 4. **检测逻辑**:统计每个IP地址含有“connecting closed”字符串的次数。 5. **报警阈值**:如果某个IP的“connecting closed”次数超过10次,将该IP打印出来,并提示“登录爆破行为”。 # Output Requirements 输出具体的Python代码,包含必要的注释说明如何实现块读取和时间过滤。 ## Triggers - 写python2程序分析auth.log - 检测auth.log爆破 - 块数据读取auth.log - 统计connecting closed次数

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Python2 Auth.log 暴力破解检测脚本编写 skill do?

编写Python2脚本,以块数据方式高效读取大型auth.log文件,统计最近5分钟内“connecting closed”出现次数超过10次的IP,并提示登录爆破行为。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill python2-auth-log-暴力破解检测脚本编写 --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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