Install
$ agentstack add skill-wvov7-course-revision-skills-closed-book-exam-focus ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Closed-Book Exam Focus
闭卷考试考点优先级分析。基于历年试题与课件索引,输出加权考点表及复习优先级。
Applicability
- 闭卷考试前的考点筛选与排序
- 输入需同时包含
@lectureSlides与@oldResource(或等价路径) - 可结合
full-course-highlights产出的页码索引作为交叉引用基础
Output Contract
默认输出 exam_lecture_summary.md 或用户指定文件名:
# Exam Focus Analysis
## Weighting Method
(说明权重公式与假设)
## Master Topic Table
| Topic | Weight | Past papers | Lecture PDF | Pages | Priority |
|-------|--------|-------------|-------------|-------|----------|
## Review Priority List
1. …
Weighting Rules
须在报告中显式声明所采用的权重规则。推荐信号如下:
| Signal | Weight tier | |--------|-------------| | 往年试题高分值 / 大题 | Highest | | 课件内多页且跨多讲出现 | Highest | | 任意年份试题出现过 | High | | 课件内重复出现 | Medium | | 年份越近 | 累加权重 |
Workflow
1. Resource inventory
- 枚举
lectureSlides/全部 PDF/PPT - 枚举
oldResource/历年卷、quiz、样题 - 扫描件须经 OCR;中间产物存放于
_extracted/
2. Past-paper extraction
- 按年份记录题型结构、分值分布、覆盖知识点
- 为每份试卷分配基础权重(近期年份权重更高)
3. Topic normalization
- 从试题中提取考点短语
- 归一化至统一 taxonomy
- 映射至
(lecture_pdf, page)索引
4. Master table synthesis
- 合并频率、权重、页码指向为单一总表
- 按权重降序排列
- 不确定匹配须标注
pending verification
5. Slide-grounded Q&A(可选扩展)
若用户要求根据课件作答试题或复核答案:
- 结论须附
(PDF filename, page)引用 - 答案须严格基于课件;课件未涵盖的内容须标注为推断
- 批量输出默认写入
answer.md
Constraints
- 不得虚构试题中未出现的考点
- 每条考点须可回溯至具体 PDF 与页码
- 仅保留一张 Master Topic Table,避免重复排序表
Examples
使用 closed-book-exam-focus:@oldResource @lectureSlides 生成 exam_lecture_summary.md,按权重降序列出考点及对应页码。
使用 closed-book-exam-focus,根据课件回答 @期末考试 全部题目并写入 answer.md,每题附 PDF 页码依据。
Short anonymized sample: references/sample-output.md.
Attribution
- Author: JohanHuang2005
- Tool: Cursor Composer 2.5
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: wvov7
- Source: wvov7/course-revision-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.