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 open-octo/octo-agent --skill exam-forecastgit clone --depth 1 https://github.com/open-octo/octo-agentWrote 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/open-octo/octo-agent/exam-forecast)<a href="https://agentmods.dev/skills/open-octo/octo-agent/exam-forecast"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/exam-forecast/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/open-octo/octo-agent/exam-forecast"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/exam-forecast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00134 | $0.02143 |
| Opus 5 | $0.00067 | $0.01071 |
| Sonnet 5 | $0.00027 | $0.00429 |
| Haiku 4.5 | $0.00013 | $0.00214 |
Grade A, and why
exam-forecast 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 7d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: exam-forecast
每个出题人的考试都有"指纹":常见的题型结构会重复出现,常踩的坑会重复出现, 各章节的分值比例往往也相对稳定。这个技能分析用户提供的历年真题,把这些规律 找出来。
这是预测权重,不是预测答案。 这个技能没法告诉用户考试会考什么,只能告诉 用户历年考过什么、按现在的大纲覆盖情况,哪些内容可能被重点考。
置信度纪律
- 真题本身的规律分析(哪些章节考了、每个考点占几道题、偏重规则还是偏重 应用)——只要真题就在眼前,这部分有把握,直接说。
- 对即将到来的考试的重点推断——默认标
[不确定],这些是预测权重,不是 确定的事。明确说清楚:"根据你给的[N]份历年真题,[考点]出现在[M]份里。 这次考试可能延续这个重点,也可能出题人换了侧重,把这个当复习时间的权重 参考,不是考试范围的定论。" - 只有1-2份真题时明确说样本太小——从1份真题里归纳出的规律基本等于噪音。
- 如果是新老师/新考试,没有历年真题可分析,这个技能没法预测,如实说清楚, 退回到"按大纲覆盖面复习"这个基本建议。
工作流程
第一步:收集信息
- 分析哪门课/哪场考试?
- 有几份这个出题人的历年真题?
- 是同一门课,还是同一个人出的不同课?
- 历年真题里有没有格式不一样的(开卷/闭卷/居家考试),和即将到来的考试 格式是否一致?
- 有没有本学期的教学大纲?
不足3份真题:标注样本偏薄,规律推断的把握相应降低。 如果真题横跨不同课程:题型风格、理论vs应用的比例这类规律可能能迁移;具体 学科内容的规律不能迁移。
第二步:逐份分析历年真题
对每份真题记录:题型结构(几道题、时长、开卷/闭卷)、考点覆盖分布(哪些 章节考了、占比多少)、题目风格(案例分析/单点深挖/论述/简答/混合)、材料 密度(信息量大的应用题 vs 纯概念题)、常见陷阱(比如某个出题人总喜欢在 干净的题干里藏一个容易忽略的前提条件)、理论vs应用的比例、不寻常的结构。
第三步:跨卷规律归纳
稳定模式(大多数/全部真题都出现):
- 考点权重(比如"某个知识点在历年真题里稳定占25%左右的分值")
- 题型风格(比如"总是一道大案例分析题+两道简答")
- 出题人的"偏好考点"(比如"某个小知识点在课堂上占比不高,但年年必考")
浮动模式(部分真题出现,不是全部):
- 论述题(比如"4份里出现2份,通常是这学期理论内容讲得多的那年")
- 开卷/闭卷、居家/教室考试之间的差异
缺席模式(值得记录但不代表不会考):
- 课堂讲过但历年真题从没考过的知识点——不要跳过复习,但也不用重点分配 时间
- 历年真题考过但现在大纲里已经没有的知识点——大概率不会再考
第四步:结合大纲给出预测
报告开头必须有这行标注,不能省略、改写或挪到别处:
学习笔记——基于历年真题规律的权重分析,不是考试预测
这不是可有可无的免责声明,是这份报告的身份标识——防止使用者把"权重分析" 误认成"确定会考的内容"。
学习笔记——基于历年真题规律的权重分析,不是考试预测
# 考试预测:[课程/老师] - [日期]
**分析的历年真题数:** [N]
**样本可信度:** [薄弱(<3) / 一般(3-5) / 较强(6+)]
**注意事项:** [例如"其中一份是开卷居家考,这次是闭卷,规律迁移打折扣"]
---
## 考点权重分布(历史)
| 考点 | 历年真题平均权重 | 是否在本学期大纲里 | 预测权重 |
|---|---|---|---|
| [考点1] | [百分比] | [是/部分/否] | [加重/持平/减轻] |
## 题型预测
- **可能的题型结构:** [X道案例分析 + Y道简答 + Z道论述,或类似]
- **材料密度:** [信息量大/信息量小/混合]
- **提问方式:** [一个大问题 / 多个具体小问题 / 分点小题]
## 出题人的固定偏好
- [考点A]——历年[M/N]份真题出现,权重是大纲占比的3-5倍
- [陷阱模式]——例如"总在干净的题干里藏一个容易被忽略的前提"
## 本学期讲过但历年很少考的内容
[列表——不要跳过,但不必重点分配时间]
## 复习时间建议
**重点(40-50%时间):** [最可能是考试重心的内容]
**次重点(30-40%时间):** [支撑性内容]
**保底检查(10-20%时间):** [讲过但历史上不常考的内容,以防万一]
## [不确定——重要说明]
这份预测基于[N]份历年真题。出题人会变化,也会调整侧重点。往年重点考的
内容,这次可能被弱化,因为大纲已经调整。把这个当复习时间的权重参考,
不是考试范围的定论。考试里出现意外内容是正常的。
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 154 lines · 134 tokens per session scan A bf4366335531
exam-forecast is a skill published in the GitHub repository open-octo/octo-agent (97 stars, last pushed today), licensed MIT. It adds 134 tokens to every session and 2,143 once invoked, about $0.0007 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 2026-09-03.
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