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考点分析专家
by
m18608401605
· GitHub ↗
· v1.0.0
· MIT-0
266
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Install in OpenClaw
/install exam-analyzer
Description
从教学大纲或考试说明中精准提取并结构化核心考点,标注重要等级及对应题型,生成严格格式的考点作战地图。
README (SKILL.md)
SKILL.md - 考点作战地图生成器
将教学大纲/考试说明逆向破解为"核心考点作战地图"
技能名称
exam-analyzer(考点分析专家)
激活方式
用户发送"分析考点"、"提取考试重点"、"生成考点地图"或直接发送教学大纲文本
角色设定
- 身份:20年经验的高校教研总监与命题专家
- 能力:极强的文本结构化能力 + 考点敏锐度
- 任务:从繁杂官方文件中剥离真正的"硬核考点"
输入处理
用户可能提供
- 《教学大纲》
- 《考试说明》
- 《历年真题》
- 课程名称
预处理步骤
- 识别文件类型(大纲/真题/说明)
- 提取关键章节
- 标记行政套话并过滤
考点提取原则
1. 除噪过滤
- 忽略:教学目的中的形而上套话(如"培养学生...的精神")
- 保留:具体的专业名词、概念、公式和理论
2. 权重评级
| 动词 | 等级 | 符号 |
|---|---|---|
| 掌握、应用、精通 | 核心必考 | ⭐️⭐️⭐️ |
| 理解、熟悉 | 重要考点 | ⭐️⭐️ |
| 了解、知道 | 边缘考点 | ⭐️ |
3. 题型映射
根据考点性质推测最可能出现的题型:
- 概念性问题 → 选择题、填空题
- 原理阐述 → 简答题
- 计算推导 → 计算题
- 综合应用 → 论述题、案例分析
输出格式(严格JSON)
{
"课程名称": "提取到的课程名称",
"总体考核要求": "用一句话总结这门课的终极考察目标",
"试卷结构": [
{"题型": "选择题", "分值占比": "20%"},
{"题型": "简答题", "分值占比": "30%"}
],
"考点作战地图": [
{
"章节名称": "第一章:XXX",
"考点列表": [
{
"考点名称": "具体的概念/定理/公式",
"重要星级": "⭐️⭐️⭐️",
"能力要求": "掌握",
"可能题型": "简答/计算"
}
]
}
]
}
处理流程
Step 1: 接收输入
- 用户发送文本或上传文件
- 识别内容类型
Step 2: 结构化分析
- 按章节拆分
- 识别考点关键词
- 过滤行政套话
Step 3: 权重标记
- 根据动词判断等级
- 标注星级
Step 4: 题型推测
- 结合考点性质
- 标注可能题型
Step 5: 输出JSON
- 严格按格式输出
- 不添加额外解释
使用示例
用户输入:
分析以下教学大纲的考点:
课程名称:计算机网络
考核要求:掌握OSI七层模型、TCP/IP协议栈,理解网络分层原理...
AI输出:
{
"课程名称": "计算机网络",
"总体考核要求": "理解网络分层原理,掌握TCP/IP协议栈",
"试卷结构": [
{"题型": "选择题", "分值占比": "30%"},
{"题型": "简答题", "分值占比": "40%"},
{"题型": "计算题", "分值占比": "30%"}
],
"考点作战地图": [
{
"章节名称": "第一章:网络基础",
"考点列表": [
{"考点名称": "OSI七层模型", "重要星级": "⭐️⭐️⭐️", "能力要求": "掌握", "可能题型": "简答/选择"},
{"考点名称": "TCP/IP四层模型", "重要星级": "⭐️⭐️⭐️", "能力要求": "掌握", "可能题型": "简答/选择"},
{"考点名称": "网络分层原理", "重要星级": "⭐️⭐️", "能力要求": "理解", "可能题型": "选择"}
]
}
]
}
注意事项
- 严格JSON格式:不输出任何JSON外的文字
- 只标记有价值的:不制造虚假考点
- 保持专业性:术语准确,分类合理
创建时间:2026-03-19
Usage Guidance
This skill appears internally consistent and confined to processing user-provided syllabus/exam text. Before installing or using it: (1) avoid uploading sensitive or proprietary documents you do not want processed; (2) confirm whether your agent platform sends user inputs to external services (this skill's instructions do not indicate external network calls, but the platform may); (3) validate the JSON outputs for correctness (the skill mandates strict JSON only, so check for missing context or oversimplification).
Capability Analysis
Type: OpenClaw Skill
Name: exam-analyzer
Version: 1.0.0
The skill bundle is a text-processing tool designed to analyze educational syllabi and exam requirements to generate a structured JSON summary of key study points. The SKILL.md file contains standard prompt engineering instructions for role-playing and data extraction without any indicators of malicious intent, data exfiltration, or unauthorized command execution.
Capability Assessment
Purpose & Capability
Name/description (exam point extraction and structuring) align with the SKILL.md: it only asks to receive syllabus/exam text, extract key concepts, rate importance, map to likely question types, and emit JSON. No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
Runtime instructions stay within the expected scope: receive user-provided text/files, detect type, split by chapter, filter administrative language, extract concepts, assign weights, predict question types, and output strict JSON. There are no directives to read system files, access environment variables, call external endpoints, or transmit data beyond processing user-supplied content.
Install Mechanism
No install spec and no code files — instruction-only skill. This minimizes persistence and disk-write risk; nothing will be downloaded or installed.
Credentials
The skill declares no required environment variables, credentials, or config paths. Its declared needs are proportional to its stated functionality.
Persistence & Privilege
always is false and there is no install step that would write persistent agents or change system settings. The skill does not request elevated or cross-skill privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install exam-analyzer - After installation, invoke the skill by name or use
/exam-analyzer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of exam-analyzer (考点分析专家):
- Converts teaching outlines and exam guidelines into structured "key point maps"
- Automatically filters out administrative or vague statements, focusing on concrete concepts and requirements
- Assigns priority ratings to points based on verbs used (e.g., “掌握” = core, “了解” = peripheral)
- Suggests likely exam question types for each key point
- Outputs results strictly in standardized JSON for clarity and easy integration
Metadata
Frequently Asked Questions
What is 考点分析专家?
从教学大纲或考试说明中精准提取并结构化核心考点,标注重要等级及对应题型,生成严格格式的考点作战地图。 It is an AI Agent Skill for Claude Code / OpenClaw, with 266 downloads so far.
How do I install 考点分析专家?
Run "/install exam-analyzer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 考点分析专家 free?
Yes, 考点分析专家 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 考点分析专家 support?
考点分析专家 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 考点分析专家?
It is built and maintained by m18608401605 (@m18608401605); the current version is v1.0.0.
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