exam-forecast

exam-forecast is a skill for Claude Code, Codex from open-octo/octo-agent. It costs 134 tokens per session (2,143 once invoked), scanned A, original, MIT.

An exam-pattern guide that compares several past papers from the same teacher or course. It identifies recurring topics, question formats, common traps, and changes over time, then estimates which areas may deserve more study.

In plain words
What is it for?
It helps analyze past exam papers, compare topic and question weights, spot a teacher’s habits, and plan revision around the available evidence.
Why use it?
It makes revision time easier to prioritize, while clearly separating observed patterns from uncertain predictions.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps analyze past exam papers, compare topic and question weights, spot a teacher’s habits, and plan revision around the available evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/open-octo/octo-agent/exam-forecast
Install

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.

Any agent
npx skills add open-octo/octo-agent --skill exam-forecast
Clone the repo
git clone --depth 1 https://github.com/open-octo/octo-agent

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for exam-forecast

README.md
[![agentmods](https://agentmods.dev/badge/skills/open-octo/octo-agent/exam-forecast/github.svg)](https://agentmods.dev/skills/open-octo/octo-agent/exam-forecast)
Your own site
<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.

agentmods 80×15 button for exam-forecast

Your own site · 80×15
<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>
Per session 134 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,143 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 7d ago against content hash bf4366335531, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

internal/skills/experts/exam-forecast/SKILL.md · 154 lines

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。

Read the full file on GitHub · 154 lines

Files

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.

Changes

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.

  1. 7d ago First seen · 154 lines · 134 tokens per session scan A bf4366335531

Subscribe to this mod's changes

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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