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Official agent skill

Exam Forecast

by anthropics in anthropics/claude-for-legal

Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the…

OfficialApache-2.0Auto-check passedEducation

Install Exam Forecast

skills CLI
$ npx skills add anthropics/claude-for-legal --skill exam-forecast -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install anthropics/claude-for-legal exam-forecast --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/anthropics/claude-for-legal.git skills-src && mkdir -p .claude/skills && cp -r skills-src/law-student/skills/exam-forecast .claude/skills/exam-forecast && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
exam-forecast
GitHub stars
9.6k
Used in
3 other repos
Token cost
~2.3k tokens
SKILL.md length
985 words
Files
1
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the…

  • Works in 5 steps: Intake → Read each past exam → Cross-exam pattern analysis → …
  • The user says whats on the exam
  • SKILL.md covers Purpose, Confidence discipline, Load context and Workflow, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Exam Forecast is an agent skill from anthropics/claude-for-legal, published by the product's own GitHub organization. Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education. The repository describes itself as: A suite of plugins for legal workflows. The licence is Apache-2.0.

When your agent uses it

  • The user says whats on the exam
  • Analyze past exams
  • Predict the exam
  • Shares past exams

Example prompts

  • “s on the exam”
  • “analyze past exams”
  • “predict the exam”
  • “/exam-forecast”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Intake
  2. Read each past exam
  3. Cross-exam pattern analysis
  4. Forecast for the upcoming exam
  5. Output location

What it can do on your machine

Read from SKILL.md and the folder at commit 4a6c651. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Exam Forecast loads about 2.3k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 985 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from anthropics/claude-for-legal at commit 4a6c651, republished under its Apache-2.0 licence (© anthropics). 985 words, ~2,286 tokens.

Download SKILL.mdSave it as .claude/skills/exam-forecast/SKILL.md (or your agent's skills folder).
name
exam-forecast
description
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
argument-hint
[class name, with past exams shared or paths to them]

/exam-forecast

  1. Load ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md → class, professor, exam format, syllabus.
  2. Apply the workflow below.
  3. Intake past exams (PDF, paste, or paths). Confirm sample size.
  4. Analyze each past exam: format, subject coverage, question style, fact-pattern density, recurring traps.
  5. Cross-exam pattern analysis — what's stable, what varies.
  6. Combine with current syllabus to produce forecast: subject weights, format, hobby horses, study emphasis.
  7. Write ~/.claude/plugins/config/claude-for-legal/law-student/exam-forecasts/[class]/forecast-[YYYY-MM-DD].md. Framed as weighting heuristic, not prediction.

Purpose

Every professor's exam has fingerprints. The same hypo structures recur. The same traps come back. The same subject ratios repeat. Students who have prior exams study smarter; students who don't, study harder. This skill analyzes the prior exams you have and surfaces the patterns.

Not magic. A forecast, not a prediction. The skill cannot tell you what's on the exam — it can tell you what's been on past exams and what's likely to recur based on syllabus coverage.

Confidence discipline

  • Pattern analysis (what subjects appeared, how many questions per topic, how often policy vs. rule-application) — confident where the exams are clearly in front of me.
  • Inference about likely emphasis on upcoming exam — [UNCERTAIN] is the default; these are forecasts, not certainties. Explicitly frame as "based on the [N] past exams you shared, [topic] appeared in [M]. Your upcoming exam may emphasize it, or the professor may rotate — use this as a weighting for review time, not a prediction."
  • If only 1-2 past exams are available, say so explicitly — any pattern inferred from 1 exam is noise.
  • If the professor is new (no past exams available), skill can't forecast. Say so; fall back to syllabus-based "these are the subjects covered" only.

Load context

  • ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md → current classes, exam formats, syllabus if captured
  • User-provided past exams (PDF, pasted text, paths)
  • Optional: syllabus for the current class (for "what's been covered to date")

If the uploaded past exams have a professor's name, use it to match patterns (same-professor exams are the highest-signal input). If not, match on subject and structure. Don't ask the user to type in the professor's name — use what's in the materials. If the user volunteers it in conversation that's fine; don't prompt for it.

Workflow

Step 1: Intake
  • Which class are we forecasting for?
  • How many past exams from this professor are available?
  • Are they from the same course, or different courses by the same professor?
  • Are any of them the take-home / open-book / different-format variants, vs. the typical format for your upcoming exam?
  • Syllabus for your current class?

If fewer than 3 past exams: flag as thin sample. Pattern inference is weaker. If exams are across different courses: some patterns transfer (question style, policy vs. doctrine ratio); subject-specific patterns don't.

Step 2: Read each past exam

For each past exam:

  • Format (number of questions, length, time limit, open/closed book)
  • Subject coverage (which topics tested, in what proportion)
  • Question style (issue-spotter, single-issue deep, policy essay, short-answer MBE-style, mix)
  • Fact pattern density (fact-heavy hypos, sparse facts with doctrinal focus, or policy prompts with no facts)
  • Recurring traps (e.g., professor always hides the jurisdictional issue in an otherwise-clean fact pattern; professor always asks about the exception rather than the rule)
  • Policy vs. doctrine ratio
  • Unusual structures (essays + MBE hybrid, moot court scenario, etc.)
Show full SKILL.md (453 more words)Show less
Step 3: Cross-exam pattern analysis

Roll up what's consistent across exams:

Stable patterns (appeared in most/all past exams):

  • Subject weights (e.g., "consideration and modification account for 30% of exam points consistently")
  • Question style (e.g., "always one long issue-spotter + two short-answer hypos")
  • Professor hobby horses (e.g., "always tests third-party beneficiaries even when it's a minor topic in class")

Variable patterns (appeared in some but not all):

  • Policy essays (e.g., "appeared in 2 of 4 past exams — usually when the semester covered a policy-heavy topic late")
  • Open-book vs. closed-book differences
  • Take-home vs. in-class differences

Absent patterns worth noting:

  • Topics covered in class that have NEVER been tested in past exams — don't skip these, but don't weight them heavily either
  • Topics tested in past exams that aren't in your current syllabus — probably not coming back
Step 4: Forecast for the upcoming exam

Header — required, first line of the forecast, both in-chat and in the saved file. Per plugin config ## Outputs, every study output carries the verbatim study-notes header. The forecast is a study output. Do not omit, rephrase, or relocate the header. The header is not a disclaimer the student can ask to drop; it is the output's identity and prevents the forecast from being mistaken for a predicted exam or for legal advice:

STUDY NOTES — NOT LEGAL ADVICE

Combine pattern analysis with current syllabus:

markdown
STUDY NOTES — NOT LEGAL ADVICE

# Exam Forecast — [class / professor] — [date]

**Past exams analyzed:** [N]
**Sample confidence:** [thin (<3) / moderate (3-5) / strong (6+)]
**Caveats:** [e.g., "one of the past exams was an open-book final; your upcoming is closed-book. Pattern transfer is partial."]

---

## Subject weighting (historical)

| Topic | Past exam weight (avg) | In current syllabus? | Forecast weight |
|---|---|---|---|
| [topic 1] | [%] | [yes/partial/no] | [heavier / stable / lighter] |

## Question-style forecast

- **Format likely:** [X issue-spotters + Y short answers + Z policy, or similar]
- **Fact-pattern density:** [fact-heavy / sparse / mixed]
- **Call style:** [one broad call / multiple specific calls / bullet sub-parts]

## Professor hobby horses to watch

- [topic A] — appeared in [M of N] past exams. Weighted 3-5x its syllabus share.
- [topic B] — [pattern]
- [trap pattern] — e.g., "hides jurisdictional issue in otherwise-clean facts"

## Topics covered this semester but rarely tested

[list — don't skip, but don't over-weight]

## Study emphasis recommendation

Based on past exam patterns AND current syllabus coverage:

**Heavy:** [topics likely to anchor the exam — 40-50% of study time]
**Moderate:** [supporting topics — 30-40%]
**Sanity check:** [topics covered but historically under-represented — 10-20%, just in case]

## [UNCERTAIN — framing]

This forecast is derived from [N] past exams. Professors vary. Professors rotate. Topics that were emphasized in past years can be de-emphasized when the syllabus shifts. Treat this as a weighting heuristic for study time, not a prediction. The exam will include surprises.
Step 5: Output location

Write to ~/.claude/plugins/config/claude-for-legal/law-student/exam-forecasts/[class]/forecast-[YYYY-MM-DD].md. Versioned — if the student gets another past exam mid-semester, re-run and append.

Integration

  • outline-builder: forecast weights feed into outline depth decisions — weight depth on heavy topics
  • flashcards: forecast-heavy topics get more cards generated
  • bar-prep-questions: irrelevant for bar prep (that has its own forecast model); exam-forecast is for class-specific finals
  • irac-practice: use forecast topics as the subject areas for IRAC practice hypos

Close with the next-steps decision tree

End with the next-steps decision tree per CLAUDE.md ## Outputs. Customize the options to what this skill just produced — the five default branches (draft the X, escalate, get more facts, watch and wait, something else) are a starting point, not a lock-in. The tree is the output; the lawyer picks.

What this skill does not do

  • Predict specific questions. Past exams show patterns; they don't show you tomorrow's prompt.
  • Work without past exams. If you don't have prior exams from this professor, the skill can't forecast — it falls back to "here's what the syllabus covers, study that."
  • Replace studying everything on the syllabus. Forecast is weighting, not elimination. Skipping a topic because it's historically under-represented is how students get burned.
  • Account for changes you don't know about. If the professor has shifted focus this year (e.g., emphasized a new case in class lectures), the skill doesn't see that unless you tell it.
  • Work reliably with 1-2 past exams. Thin sample. Flag as such.

© anthropics, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in law-student/skills/exam-forecast of anthropics/claude-for-legal.

Open the folder on GitHubat commit 4a6c651

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in anthropics/claude-for-legal, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Exam Forecast

What does Exam Forecast do?

Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the…. Exam Forecast is an agent skill from anthropics/claude-for-legal, published by the product's own GitHub organization. Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam.

When should I use Exam Forecast?

Exam Forecast fits situations like: the user says whats on the exam; analyze past exams; predict the exam; shares past exams.

How do I install Exam Forecast in Claude Code?

Run `npx skills add anthropics/claude-for-legal --skill exam-forecast -a claude-code`. Or copy the skill folder (law-student/skills/exam-forecast in anthropics/claude-for-legal) into .claude/skills/exam-forecast in your project. Claude Code loads it when a task matches its description.

How do I install Exam Forecast in Codex?

Run `npx skills add anthropics/claude-for-legal --skill exam-forecast -a codex`. Or copy the skill folder (law-student/skills/exam-forecast in anthropics/claude-for-legal) into .agents/skills/exam-forecast in your project. Codex loads it when a task matches its description.

Can I use Exam Forecast in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add anthropics/claude-for-legal --skill exam-forecast -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/exam-forecast, .gemini/skills/exam-forecast, .github/skills/exam-forecast and .opencode/skills/exam-forecast in your project.

What does Exam Forecast need to run?

SKILL.md names no scripts, command-line tools or credentials: Exam Forecast is instructions for the agent only.

Does Exam Forecast access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Exam Forecast safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Exam Forecast use?

Exam Forecast is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Exam Forecast use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Exam Forecast?

Skills that share tags, products or a category with Exam Forecast: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 66k stars), Deep Reading Analyst (ginobefun/deep-reading-analyst-skill, 354 stars) and OpenMAIC Setup and Extension (THU-MAIC/OpenMAIC, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Exam Forecast?

anthropics (a GitHub organization, an official publisher) maintains it in anthropics/claude-for-legal, which has 9,633 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on September 29, 2026.

Source: anthropics/claude-for-legal on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.