Flashcards

Skill yugash007/edu-agent-skills/skills/productivity/flashcards

Reusable educational skills for AI coding agents. Turn agents (Gemini CLI, Claude Code, Cursor, etc.) into Socratic mentors and active learning companions with a single command.

Install

npx -y skills add yugash007/edu-agent-skills --skill flashcards

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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Use when creating, testing, or updating active-recall flashcards grounded in session concepts to reinforce retention.

SKILL.md

3.4 KB, as published. Nobody here has run it

Purpose

Convert session concepts into structured active-recall flashcards. Cards must test reasoning and application, not simple definitions, to build durable understanding.

Activation

Concept teaching session just completed. Learner requests cards. spaced-repetition needs card generation/update. Revision period (exam, interview, milestone).
Skip if: concept hasn't been taught yet. Goal is deep exploration → deep-dive. Active debugging/project work → would interrupt flow.
Routing: generate cards after understanding is confirmed. Feed into spaced-repetition for scheduling. Cards failed 3 times → trigger misconception-detector.

Inputs

Concepts/skills covered, learner's confirmed level, existing card set (if updating), error patterns from assessment skills.

Card Types

Concept: "What is X?" → vocabulary accuracy.
Mechanism: "Trace what happens when X executes." → process understanding.
Tradeoff: "When would you NOT use X?" → decision reasoning.
Application: "Given [context], which [tool/pattern] and why?" → transfer.
Debug: "What's wrong with this code?" → diagnostic thinking.

Prefer Mechanism, Tradeoff, and Application types (higher transfer value). At least 60% of cards should be these types.

Workflow

Extract — Identify 3–7 key concepts worth card-ifying. Prioritize mechanisms, tradeoffs, application patterns. Skip long-mastered concepts.
Generate — 1–2 cards per concept. Front = question (not keyword). Back = complete model response (~100 words max). Verify: front is unambiguous, back is concise but complete, card tests reasoning not verbatim recall.
Test — Present front only; learner responds before seeing back. Self-score: Easy (fluent) / Hard (needed effort) / Failed (wrong/blank). Update interval.
Handle Failures — Failed card: re-test after 10 minutes in same session. Same card failed 3 times across sessions: suspend and trigger misconception-detector.
Update — After any misconception correction: update affected card backs. Never leave outdated cards in the deck.

Rules

DO: card fronts must be questions, not keywords.
DO: backs must require explanation, not one-word answers.
DO: test at least 3 cards in active-recall mode during generation session.
DO: cross-reference cards for connected concepts.
DON'T: generate all "What is X?" cards — that's definition-only bias.
DON'T: let decks grow unbounded — flag cards with ease_factor > 2.5 for 5+ sessions as mastered.
DON'T: generate cards before understanding is confirmed.
DON'T: skip active testing — generation is not the end state.

Output

Card generation: topic, date, cards (front + back + type + difficulty). Active recall: front shown, learner responds, back revealed, score + next review date. Format naturally.

Checklist

60%+ cards are Mechanism/Tradeoff/Application type.
Each card front is a clear question; back is a complete response.
At least 3 cards tested in active-recall this session.
Cards updated after any misconception correction.