universal-flashcard-generator

name	universal-flashcard-generator
description	Converts any study material (notes, textbooks, syllabus topics, PYQs) into optimized flashcards for ANY university worldwide. Supports 6 flashcard types across 3 difficulty tiers with built-in Spaced Repetition System (SRS) metadata. Exports to Anki, Quizlet, CSV, and text formats. Works for ALL subjects, ALL departments.

Universal Flashcard Generator

Overview

Transforms any study material into exam-optimized flashcards. Uses cognitive science principles (active recall, spaced repetition, elaboration, interleaving) to maximize retention. Generates SRS-ready decks with proper scheduling metadata for long-term memory consolidation.

How This Skill Works

1. User provides: Source material (notes, textbook chapters, syllabus topics, question papers, or any text)
2. System analyzes: Content structure, subject type, Bloom's level distribution, exam patterns
3. System selects: Optimal flashcard types based on content characteristics and learning goals
4. System generates: Formatted flashcard deck with SRS scheduling metadata
5. System exports: In requested format (Anki APKG, Quizlet CSV, plain text, or markdown)

1. Flashcard Types

Type A — Basic Fact (Recall)

Element	Description
Front	Direct question or prompt
Back	Concise answer (1-3 sentences)
Best for	Definitions, dates, formulas, terminology, lists
Bloom's Level	Remember
Example Front	What is the time complexity of binary search?
Example Back	O(log n) in the average and worst case

Type B — Cloze Deletion

Element	Description
Front	Sentence with key term blanked: The capital of France is {{c1::Paris}}
Back	Full sentence with revealed term
Best for	Fill-in-the-blank style recall, language learning, definitions
Bloom's Level	Remember, Understand
Example Front	The three pillars of {{c1::sustainable development}} are economic, social, and environmental.

Type C — Concept Association

Element	Description
Front	Concept, term, or scenario
Back	Related concept, application, or counter-example
Best for	Linking related ideas, compare/contrast, "why" questions
Bloom's Level	Understand, Apply
Example Front	How does DHCP differ from DNS?
Example Back	DHCP assigns IP addresses dynamically; DNS resolves domain names to IP addresses.

Type D — Multi-Step Problem

Element	Description
Front	Problem or calculation
Back	Step-by-step solution with reasoning
Best for	Numerical problems, derivations, proofs, algorithms
Bloom's Level	Apply, Analyze
Example Front	Solve: Find the determinant of [[2,3],[1,4]]
Example Back	det = (24) - (31) = 8 - 3 = 5

Type E — Application Scenario

Element	Description
Front	Real-world scenario or case snippet
Back	Diagnosis, solution, or analysis
Best for	Case studies, clinical scenarios, engineering design, business cases
Bloom's Level	Analyze, Evaluate
Example Front	A user reports "Connection refused" when accessing port 443 on a server. What is likely?
Example Back	The service on port 443 (likely HTTPS) is not running or a firewall is blocking the port.

Type F — Compare & Contrast

| Element | Description | | ----------------- | --------------------------------------------------------- | -------------------------------------------------- | | Front | Two related concepts | | Back | Structured comparison (similarities / differences table) | | Best for | Comparative questions, distinguishing confusable concepts | | Bloom's Level | Analyze | | Example Front | Compare TCP vs UDP | | Example Back | TCP: connection-oriented, reliable, ordered, slower | UDP: connectionless, unreliable, unordered, faster |

2. Difficulty Tiers

Tier	Question Style	Cognitive Load	Use Case
Tier 1 — Recognition	Multiple choice, true/false, "which of the following"	Low	Initial learning, building confidence
Tier 2 — Recall	Direct question, cloze deletion, fill-in-the-blank	Medium	Active recall practice, memory consolidation
Tier 3 — Application	Scenario-based, problem-solving, "why/how" questions	High	Deep understanding, exam readiness

Each deck includes a progression path: Tier 1 -> Tier 2 -> Tier 3.

3. SRS Scheduling Metadata

Every generated flashcard includes these scheduling fields for SRS system import:

Field	Example	Description
Deck	Computer Networks	Subject or chapter name
Tag	cn_3_transport	Hierarchical topic tag
Due	2026-05-24	Next review date
Interval	1	Days until next review (starts at 1)
Ease	250	Starting ease factor (default 250%)
Difficulty	3	Card difficulty (1=Easy, 5=Hard)
LastReviewed	2026-05-23	Last review timestamp

4. Export Formats

Format	Extension	Compatible With	Notes
Anki	.apkg	Anki desktop, AnkiDroid, AnkiMobile	Full SRS metadata preserved
CSV	.csv	Anki import, Quizlet, Excel, custom tools	Tab-separated by default
Markdown	.md	Any markdown renderer, Obsidian, Notion	Readable, version-controllable
Plain Text	.txt	Any text editor	Minimal formatting
JSON	.json	Programmatic processing	Structured data export

International Export Examples

Example CSV Header

Front, Back, Tags, Deck, Due

Example Quizlet Import Format

Front	Back	Tags
What is TCP?	Transmission Control Protocol	cn_unit3
Define OSI model	7-layer conceptual model	cn_unit1

JSON Export Schema

{
  "deck": "Subject_Unit",
  "cards": [
    {
      "front": "Question text",
      "back": "Answer text",
      "tags": ["tag1", "tag2"],
      "due": "2026-06-01",
      "interval": 1,
      "ease": 250,
      "difficulty": 3
    }
  ]
}

Anki APKG Packaging Note

APKG files are SQLite databases with a collection.anki21 table containing the card/note data and a media directory for images/audio. The system generates APKG-format output compatible with Anki 2.1+. Import via: Anki Desktop → File → Import → Select .apkg file.

5. Generation Modes

Mode 1 — From Lecture Notes

• Input: Raw notes or textbook chapters
• Output: Comprehensive deck covering all key concepts
• Algorithm: Extract definitions, named concepts, lists, important figures, and causal relationships

Mode 2 — From Syllabus

• Input: Syllabus topics and subtopics
• Output: Coverage-optimized deck ensuring every syllabus point has at least one card
• Algorithm: Map syllabus items to question templates

Mode 3 — From PYQs

• Input: Previous year question papers
• Output: Exam-focused deck targeting historically tested concepts
• Algorithm: Frequency-weight topics and generate cards proportional to historical importance

Mode 4 — Exam Cram

• Input: High-priority topics (from imp topics analysis)
• Output: Condensed deck covering only high-probability questions
• Algorithm: Selects Tier 1 and Tier 2 cards for fastest coverage

6. Deck Organization

Subject Name/
  Unit 1 - Topic Name/
    Section 1.1 - Subtopic/
      [F] Definition of concept X
      [C] Cloze: The three types of Y are ___
      [P] Problem: Calculate Z given W
    Section 1.2 - Subtopic/
      ...
  Unit 2 - Topic Name/
    ...
  Cross-Unit Connections/
    [A] How does concept A from Unit 1 relate to concept B from Unit 3?

Prefix legend: [F] = Fact, [C] = Cloze, [P] = Problem, [A] = Association, [S] = Scenario

7. Active Recall Triggers

For each card, the system adds memory-triggering cues:

• Visual cues: Parent diagram references "(see Fig 3.2 in textbook)"
• Mnemonic triggers: Acronym hints (e.g., "PEMDAS: Parentheses, Exponents...")
• Association chains: "This concept is related to X which you studied in Unit 2"
• Contrast prompts: "Not to be confused with Y (see card #42)"

8. Common Use Cases

Scenario	Mode	Card Types	Deck Size (approx)
Learning a new subject	From Lecture Notes	A, B, C	200-500 cards
Exam revision (1 week)	From PYQs + Syllabus	A, B, D	100-300 cards
Last-night cramming	Exam Cram	A, B	50-100 cards
Language vocabulary	From Lecture Notes	A, B	500-1000 cards
Medical/legal memorization	From Syllabus	A, B, C, E	500-2000 cards
Formula revision	From Syllabus	D	30-100 cards

9. Example

User: Generate flashcards for Computer Networks Unit 3 (Transport Layer) for SPPU TE Comp. Include TCP, UDP, congestion control.

System generates:

Deck: SPPU_TE_CN_Unit3

Card 1 [F]: What does TCP stand for?
Back: Transmission Control Protocol

Card 2 [C]: {{c1::TCP}} is connection-oriented, while {{c2::UDP}} is connectionless.

Card 3 [D]: A sender uses AIMD with cwnd=16 MSS. After packet loss, what is the new cwnd in TCP Reno?
Back: cwnd = 16/2 = 8 MSS (multiplicative decrease)

Card 4 [E]: A video streaming app uses UDP. Why?
Back: Streaming tolerates minor packet loss but requires low latency. UDP provides faster delivery without retransmission delays.

... (full deck generated)

10. Export Command (for Anki)

# The generated .apkg file can be imported via:
# Anki Desktop: File > Import > Select .apkg
# AnkiDroid: Tap + > Import .apkg
# AnkiMobile: Share > Open in Anki

# CSV format follows Anki's preferred tab-separated schema:
# Front\tBack\tTags\tDeck

Session Config

This skill integrates with the session config system (deps/session-profile.json). Before executing, check for an existing session profile:

• If deps/session-profile.json exists, read university, subject, pattern, and exam_type fields to auto-configure the skill.
• If the file does not exist, fall back to user-provided context or prompt the user to run setup-exam-prompt (or npm run init) first.
• Session config eliminates redundant context detection — detection happens once and is reused across all skill calls.

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Error Handling

Situation	Action
Source material too short	Respond: "Material too sparse for flashcard generation. Minimum of 500 characters of meaningful content required."
Unsupported file format	Respond: "Unsupported format. Accepted formats: PDF, DOCX, TXT, MD, or pasted text."
SRS metadata conflict	Auto-resolve by prioritizing session config defaults over card-level overrides
Export format failure	Log error, fall back to Markdown export, notify user of format limitation

Quality Gate — Check Before Output

• Every syllabus point has at least one corresponding flashcard
• No duplicate cards (same front text across different cards)
• Difficulty progression exists (Tier 1 → Tier 2 → Tier 3 cards)
• SRS metadata fields are populated for all cards
• Export format matches user's requested format exactly
• Deck naming convention follows University_Subject_Unit pattern

11. Integration with Other Skills

Skill	Integration
universal-session-config	Reads university/subject/pattern from session profile
universal-notes-generator	Takes generated notes as input and converts them to flashcards
universal-pyq-analyzer	Uses PYQ frequency analysis to weight card importance
universal-imp-topics-generator	Generates cram-mode decks from imp topics output
universal-mcq-practice-generator	Creates exam-simulated MCQ practice from same material