Agent skill

Examprep AI

by sickn33 in sickn33/agentic-awesome-skills

Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap.

MITAuto-check passedEducation

Install Examprep AI

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a claude-code

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

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills examprep-ai --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/examprep-ai .claude/skills/examprep-ai && 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/

Paste into Claude Code

Install the "examprep-ai" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/examprep-ai into .claude/skills/examprep-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "examprep-ai", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/examprep-ai

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills examprep-ai --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/examprep-ai .agents/skills/examprep-ai && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "examprep-ai" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/examprep-ai into .agents/skills/examprep-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "examprep-ai", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills examprep-ai --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/examprep-ai .cursor/skills/examprep-ai && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "examprep-ai" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/examprep-ai into .cursor/skills/examprep-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "examprep-ai", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/examprep-ai

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills examprep-ai --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/examprep-ai .gemini/skills/examprep-ai && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "examprep-ai" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/examprep-ai into .gemini/skills/examprep-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "examprep-ai", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills examprep-ai

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/examprep-ai .github/skills/examprep-ai && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "examprep-ai" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/examprep-ai into .github/skills/examprep-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "examprep-ai", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills examprep-ai --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/examprep-ai .opencode/skills/examprep-ai && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "examprep-ai" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/examprep-ai into .opencode/skills/examprep-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "examprep-ai", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: examprep-ai
GitHub stars: 47k
Used in: 1 other repo
Token cost: ~3.7k tokens
SKILL.md length: 1,063 words
Files: 1
Skills in repo: 1,493
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap.

Works in 3 steps: Collect at least one of: syllabus, past… → Confirm course code if OCR confidence <… → Ask time available. If no answer →…
Last-minute revision
SKILL.md covers When to Use, 🎯 Selective Reading Rule —…, Shared Foundations and Limitations, plus 12 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Examprep AI is an agent skill from sickn33/agentic-awesome-skills. Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap. Covers theory, numericals, MCQs, coding, and lab prep, ordered Easy → Medium → Hard. Use for last-minute revision, important topics, and question prediction.

Its SKILL.md is about 3.7k 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, covering Study guides and flashcards. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

Last-minute revision
Important topics
Question prediction

Example prompts

“/examprep-ai”

Requirements

Pre-approved tools (allowed-tools): Read, Glob, Grep

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

Collect at least one of: syllabus, past question papers, notes, or subject name + university.
Confirm course code if OCR confidence < 80%: "I detected [X] — is this correct?"
Ask time available. If no answer → default Standard Mode (6–12 hrs) and state the assumption.

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Read
- Glob
- Grep
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
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

Examprep AI loads about 3.7k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 1,063 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~69
When it runs · the whole SKILL.md, loaded when a task matches: ~3.7k

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 sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 1,063 words, ~3,726 tokens.

Download SKILL.mdSave it as .claude/skills/examprep-ai/SKILL.md (or your agent's skills folder).

name: examprep-ai
description: Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap. Covers theory, numericals, MCQs, coding, and lab prep, ordered Easy → Medium → Hard. Use for last-minute revision, important topics, and question prediction.
allowed-tools: Read, Glob, Grep
risk: safe
source: community
date_added: 2026-06-05
author: WHOISABHISHEKADHIKARI
user-invokable: true
tags: education, exam-prep, study-guide, question-prediction, syllabus-analysis, revision, students

ExamPrep AI

When to Use

Use this skill when you need to:

Convert a syllabus, past papers, or study notes into a prioritized roadmap.
Focus on specific types of exam questions (Theory, Numerical, MCQ, Coding, Lab).
Create flashcards, predicted exam papers, or check your overall exam readiness.
Perform last-minute revision or deep-dive into important exam topics.

🎯 Selective Reading Rule — Read ONLY the section matching the request

What the student asks for	Jump to
Full roadmap / "what to study" / syllabus + past papers uploaded	Full Roadmap Mode
Theory questions only / definitions / explanations	Theory Notes
Numerical / calculation / derivation problems	Numerical Notes
MCQ / True-False / objective practice	MCQ Notes
Coding / algorithm / trace / debug	Coding Notes
Lab / practical / viva prep	Lab Notes
Flashcards only	Flashcards
Mock exam paper	Predicted Exam Paper
Readiness check / score projection	Exam Readiness Dashboard

Rule: Read the matched section and the Shared Foundations block. Skip everything else. Do not load all sections for a focused request.

Shared Foundations

Load this block for every request. It is small and always needed.

Difficulty Scale (Universal)

Level	Signal Words	Student Goal
🟩 Easy	define, state, list, name, identify, what is	Guaranteed marks — study first
🟨 Medium	explain, describe, compare, calculate, implement, trace	Mid-paper marks
🟥 Hard	derive, prove, optimize, analyze, evaluate, design, why	Score separators — study last

Order rule: Always present Easy → Medium → Hard. Never reverse.

Intake (ask once, then proceed)

Collect at least one of: syllabus, past question papers, notes, or subject name + university.
Confirm course code if OCR confidence < 80%: "I detected [X] — is this correct?"
Ask time available. If no answer → default Standard Mode (6–12 hrs) and state the assumption.

Study Modes

Mode	Time	Load
🚨 Emergency	1–2 hrs	🟩 Easy only, top 10 questions
⚡ Sprint	3–5 hrs	🟩 + 🟨, top 25 questions
📚 Standard (default)	6–12 hrs	All difficulties, full roadmap
🗓️ Advance	Days+	Daily schedule + mock papers

Syllabus Guardrail

Map every question to a syllabus unit (≥ 70% match → [IN SYLLABUS]).
Never generate content for topics absent from the uploaded syllabus.
Out-of-syllabus items → flag, ask student before including.

Probability Score

Score = (Frequency × 0.40) + (Recency × 0.30) + (Unit Weight × 0.20) + (Marks × 0.10)

Frequency: appearances ÷ max appearances × 100
Recency: last 2 yrs = 100 · 3–4 yrs = 60 · older = 30
Unit Weight: core = 100 · elective = 50
Marks: 10+ = 100 · 5–9 = 60 · 2–4 = 30 · MCQ = 20

Limitations

This skill supports study planning and revision, but it cannot guarantee exam questions, marks, grading outcomes, or instructor expectations.
Probability scores are heuristics based on supplied syllabi, notes, and past papers; sparse, outdated, or incomplete inputs reduce reliability.
The skill should not fabricate syllabus coverage. If source material is missing, ambiguous, or out of scope, ask the student to confirm before adding predicted content.
It is not a substitute for official course guidance, accessibility accommodations, academic-integrity policies, or instructor feedback.
Do not request or process private student records beyond the study material needed for the current revision task.

Full Roadmap Mode

Use when: student uploads syllabus + past papers, or asks "what should I study?"

Step 1 — Extract. Pull all questions; note year/source for each. Confirm: "Extracted [N] questions from [M] papers for [Course]. Found: 📝[A] 🔢[B] 🔘[C] 💻[D] 🧪[E]. Proceed?"

Step 2 — Classify + tag difficulty. Use the five-type table:

Type	Identify By
📝 Theory	define, explain, discuss, compare, differentiate
🔢 Numerical	calculate, find, solve, derive, prove, numbers in question
🔘 MCQ/T-F	options listed, "true or false", "which of the following"
💻 Coding	write a program, implement, trace output, algorithm, flowchart
🧪 Lab	experiment, procedure, observation, aim, apparatus, viva

Step 3 — Build ranked tables (one per type):

| # | Question | Times | Marks | Difficulty | Unit | Priority |
|---|----------|-------|-------|------------|------|----------|
| 1 | [question text] | [N]× | [X] | 🟩/🟨/🟥 | Unit [X] | 🔥 Must / ✅ Do |

Step 4 — Generate notes using the matching type section below. Order: Easy across all types first → then Medium → then Hard.

Step 5 — Coverage tracker:

Unit 1: [Name]  →  📝✅  🔢✅  🔘⚠️ PREDICTED  💻—  🧪—
Legend: ✅ past paper  ⚠️ predicted  — not applicable

For any gap: generate one predicted question + note, label [PREDICTED — not from past papers].

Step 6 — Offer: "Would you like (a) Flashcards, (b) Predicted Exam Paper, or (c) Readiness Dashboard?"

Theory Notes

Use when: student asks about definitions, explanations, long-answer questions.

🟩 Easy — Definition / List (30 sec)

📝🟩 [Question] | [N]× | [X] marks
─────────────────────────────────
ANSWER: [2–4 bullets max]
KEY TERM: [single most important word]
MEMORY HOOK: [one-liner trick]

🟨 Medium — Explanation / Comparison (2 min)

📝🟨 [Question] | [N]× | [X] marks
─────────────────────────────────
DEFINITION: [1 sentence]
MAIN POINTS: • P1 • P2 • P3 • P4
DIAGRAM: [text description — student sketches from this]
EXAM TIP: [what examiner rewards]

🟥 Hard — Discussion / Evaluation (5 min read · 10 min write)

📝🟥 [Question] | [N]× | [X] marks | Unit [X]
─────────────────────────────────────────────
INTRO: [2–3 sentences]
SECTION 1 — [subtopic]: • point • point
SECTION 2 — [subtopic]: • point • point
SECTION 3 — [subtopic]: • point • point
DIAGRAM: [sketch description]
CONCLUSION: [1–2 lines]
MARKS HINT: Intro ~2 · each section ~3 · diagram ~2 · conclusion ~1
MEMORY: [acronym or order trick]

Show full SKILL.md (414 more words)Show less

Numerical Notes

Use when: student asks for calculation problems, derivations, formulas.

🟩 Easy — Direct formula plug-in

🔢🟩 [Problem Type] | [N]× | [X] marks
──────────────────────────────────────
FORMULA:        [clearly written]
GIVEN → FIND:   [what's given / what to find]
WORKED EXAMPLE:
  Step 1: [substitute]
  Step 2: [calculate]
  Answer: [result + unit]
COMMON MISTAKE: [the one error students make]
MEMORY HOOK:    [how to remember formula]

🟨 Medium — Multi-step with condition

🔢🟨 [Problem Type] | [N]× | [X] marks
──────────────────────────────────────
FORMULA(S): [all needed]
APPROACH:   [which formula when — decision rule]
WORKED EXAMPLE:
  Step 1: [setup / draw table]
  Step 2: [apply condition]
  Step 3: [calculate]
  Step 4: [verify / interpret]
  Answer: [result]
WATCH OUT:  [condition that trips students]
EXAM TIP:   [show working — marks for method too]

🟥 Hard — Derivation / Proof

🔢🟥 [Problem / Derivation] | [N]× | [X] marks
───────────────────────────────────────────────
PREREQUISITES: [what student must know first]
DERIVATION:
  Step 1: [first principles]
  Step 2: [key transformation]
  ...Final: [result / QED]
WORKED EXAMPLE: [concrete numbers applied]
MARKS BREAKDOWN: [method marks vs answer marks]
COMMON ERRORS: [2–3 errors that lose marks]

MCQ Notes

Use when: student asks for MCQ practice, true/false, objective questions.

🟩 Easy — Recall

🔘🟩 [Question] | [N]×
──────────────────────
CORRECT: [option + text]
WHY CORRECT: [one sentence]
WHY OTHERS WRONG: • A: ... • B: ... • C: ...
KEY FACT: [the one thing this tests]

🟨 Medium — Application

🔘🟨 [Question] | [N]×
──────────────────────
CORRECT: [option + text]
REASONING: [identify concept] → [apply rule] → [eliminate wrong]
TRAP: [why students pick the wrong answer]

🟥 Hard — Trap / Edge-case

🔘🟥 [Question] | [N]×
──────────────────────
CORRECT: [option + text]
WHY TRICKY: [what assumption is exploited]
ELIMINATE: • Drop [A]: [reason] • Drop [B]: [reason] • Keep [C]: [reason]
RULE: [the precise rule that settles this type]

Coding Notes

Use when: student asks to write programs, trace output, implement algorithms, debug.

🟩 Easy — Syntax / Pattern recall

💻🟩 [Task] | [N]× | [X] marks
────────────────────────────────
PATTERN:     [algorithm/structure name]
TEMPLATE:    [minimal working skeleton — pseudocode or language-specific]
KEY LINES:   [1–2 lines examiner looks for]
MEMORY HOOK: [how to recall under pressure]

🟨 Medium — Logic construction

💻🟨 [Task] | [N]× | [X] marks
────────────────────────────────
APPROACH:
  1. [sub-tasks]  2. [data structures]  3. [step-by-step logic]
ANNOTATED CODE: [code with inline comments]
EDGE CASES:  [inputs needing special handling]
EXAM TIP:    [comment code — examiners reward clarity]

🟥 Hard — Optimize / Trace / Debug

💻🟥 [Task] | [N]× | [X] marks | TYPE: [Optimize / Trace / Debug]
──────────────────────────────────────────────────────────────────
TRACE →   Input | Trace Table (Iter · VarA · VarB · Output) | Final Output
OPTIMIZE → Naive O(?) → Optimized O(?) | Key Insight: [what enables it]
DEBUG →   Bug Location | Bug Type | Fix | Why it works

Lab Notes

Use when: student asks about experiments, procedures, observations, viva prep.

🟩 Easy — Name / Identify

🧪🟩 [Experiment] | [N]×
─────────────────────────
AIM:      [one sentence]
APPARATUS: [bullet list]
RESULT:   [expected outcome to state]
KEY TERM: [most important term]

🟨 Medium — Write procedure

🧪🟨 [Experiment] | [N]×
─────────────────────────
AIM / APPARATUS: [brief]
PROCEDURE: Step 1 → Step 2 → Step 3 → Step 4
OBS TABLE: [column headers + example row]
RESULT:    [how to state conclusion]
PRECAUTIONS: [2–3 points examiners look for]

🟥 Hard — Analysis / Viva

🧪🟥 [Experiment] | [N]×
─────────────────────────
ANALYSIS: • result in context • formula used • source of error
VIVA:
  Q1: [question]  A: [2–3 sentence answer]
  Q2: [question]  A: [2–3 sentence answer]
  Q3: [question]  A: [2–3 sentence answer]
EXAM TIP: [what viva examiner always asks]

Flashcards

Use when: student asks for flashcards or quick-recall cards.

One card per question:

[TYPE EMOJI][DIFFICULTY EMOJI]
Q: [question]
A: [answer in 1–2 lines]
Key: [formula / term / pattern — if applicable]

Predicted Exam Paper

Use when: student asks for a mock paper or practice test.

Generate one paper with all types represented. Label every question with type + difficulty.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
AI PREDICTION — Not official. For practice only.
Course: [Name]  |  Total Marks: [X]  |  Time: [X] hrs
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

SECTION A — Short / Objective  [🟩 Easy]
  [MCQ / T-F / 1-mark definitions]

SECTION B — Medium Answer      [🟨 Medium]
  [Theory explanations + medium numericals]

SECTION C — Long Answer        [🟥 Hard]
  [Long theory + derivations + coding]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Exam Readiness Dashboard

Use when: student asks for a score estimate or readiness check.

📊 EXAM READINESS
──────────────────────────────────────────────────────
TYPE          EASY    MEDIUM   HARD    OVERALL
📝 Theory     [X]%    [X]%     [X]%    [X]%
🔢 Numerical  [X]%    [X]%     [X]%    [X]%
🔘 MCQ/T-F    [X]%    [X]%     [X]%    [X]%
💻 Coding     [X]%    [X]%     [X]%    [X]%
🧪 Lab        [X]%    [X]%     [X]%    [X]%
──────────────────────────────────────────────────────
PREPAREDNESS  : [X]%
MARKS RANGE   : [Low]–[High] out of [Total]
──────────────────────────────────────────────────────
STRONG        : [types + topics]
WEAK → FOCUS  : [types + topics]
──────────────────────────────────────────────────────
Confidence: [High/Medium/Low]  |  Based on: [N] papers

Worked Example

Concrete before/after demonstrating the skill.

Input:

"I have my OS exam tomorrow. Here's the syllabus [paste] and 3 past papers [upload]. I have 4 hours."

Skill routes to: Full Roadmap Mode → Sprint Mode (3–5 hrs)

Output sequence:

Extraction confirm: "Extracted 47 questions from 3 papers for Operating System (CSC-207). Found: 📝18 🔢12 🔘10 💻7 🧪0. Proceed?"
Ranked tables for all types, Easy → Medium only (Sprint Mode skips Hard except top-1 per unit)
Notes for top 25 questions — Easy across all types first, then Medium
Coverage tracker showing which units are covered
Offer: flashcards, mock paper, or dashboard

Quality Checks (run before every output)

Check	Rule
Syllabus compliance	Every note maps to a syllabus unit
Difficulty order	Easy before Medium before Hard — never reversed
Numerical accuracy	Worked examples compute correctly
Code validity	Snippets are syntactically correct
Note length	Readable in ≤ 2–5 min per note
No hallucination	No facts absent from uploaded materials
Course code confirmed	OCR-detected code verified by student

Error Responses

Situation	Say
No syllabus	"Without a syllabus I can't guarantee on-topic notes. Paste your unit list as text?"
1 past paper only	"One paper = lower prediction confidence. More papers = better accuracy."
OCR failure	"Couldn't read part of the image. Can you retype those questions?"
Out-of-syllabus question	"This doesn't match your syllabus — skipping it. Want me to include it anyway?"
Mixed subjects	"Found questions from two subjects. Should I separate them?"
No time given	"Defaulting to Standard Mode (6–12 hrs). Tell me if you have less time."
No numericals/coding found	"No numerical/coding questions found. Share a paper that includes them if your exam has these."

Files

Just SKILL.md in skills/examprep-ai of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 680176d

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

lingxling/awesome-skills-cnantigravity-awesome-skills/skills/examprep-ai antigravity-awesome-skills/plugins/agentic-awesome-skills/skills/examprep-ai+1 more

Compare with similar skills

Examprep AI next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Examprep AI compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Examprep AI this skillsickn33/agentic-awesome-skills	47k	1 repos	~3.7k	Automated safety check: Pass	MIT	today
Deep Reading Analystginobefun/deep-reading-analyst-skill	353	4 repos	~3.6k	Automated safety check: Pass	MIT	11 mo ago
NihaishaJuneYaooo/nihaisha-nishi-tcm	2.2k	—	~4k	Automated safety check: Pass	None	23 days ago
Claude Certification Tutorrohitg00/ai-engineering-from-scratch	66k	—	~3k	Automated safety check: Pass	MIT	today
StudyVault Quiz Tutorbevibing/tutor-skills	1.3k	—	~1.4k	Automated safety check: Pass	MIT	7 mo ago
Project Mastery Coachtudoumashu/ai-memory-skillpack	443	—	~1.8k	Automated safety check: Pass	MIT	1 mo ago

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Questions about Examprep AI

What does Examprep AI do?

Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap. Examprep AI is an agent skill from sickn33/agentic-awesome-skills. Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap.

When should I use Examprep AI?

Examprep AI fits situations like: last-minute revision; important topics; question prediction.

How do I install Examprep AI in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a claude-code`. Or copy the skill folder (skills/examprep-ai in sickn33/agentic-awesome-skills) into .claude/skills/examprep-ai in your project. Claude Code loads it when a task matches its description.

How do I install Examprep AI in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill examprep-ai -a codex`. Or copy the skill folder (skills/examprep-ai in sickn33/agentic-awesome-skills) into .agents/skills/examprep-ai in your project. Codex loads it when a task matches its description.

Can I use Examprep AI 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 sickn33/agentic-awesome-skills --skill examprep-ai -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/examprep-ai, .gemini/skills/examprep-ai, .github/skills/examprep-ai and .opencode/skills/examprep-ai in your project.

What does Examprep AI need to run?

SKILL.md names no scripts, command-line tools or credentials: Examprep AI is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Glob, Grep.

Does Examprep AI 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 Examprep AI 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 Examprep AI use?

Examprep AI is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Examprep AI use?

About 3.7k tokens (SKILL.md is roughly 15k 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 Examprep AI?

Skills that share tags, products or a category with Examprep AI: Deep Reading Analyst (ginobefun/deep-reading-analyst-skill, 353 stars), Nihaisha (JuneYaooo/nihaisha-nishi-tcm, 2.2k stars), Claude Certification Tutor (rohitg00/ai-engineering-from-scratch, 66k stars) and StudyVault Quiz Tutor (bevibing/tutor-skills, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Examprep AI?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Examprep AI

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

ExamPrep AI

When to Use

🎯 Selective Reading Rule — Read ONLY the section matching the request

Shared Foundations

Difficulty Scale (Universal)

Intake (ask once, then proceed)

Study Modes

Syllabus Guardrail

Probability Score

Limitations

Full Roadmap Mode

Theory Notes

Numerical Notes

MCQ Notes

Coding Notes

Lab Notes

Flashcards

Predicted Exam Paper

Exam Readiness Dashboard

Worked Example

Quality Checks (run before every output)

Error Responses

Files

Used in 1 other repository

Compare with similar skills

Similar skills

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Questions about Examprep AI