Skip to content
cccskills
FreePublished on GitHub

flashcards

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

The English summary is being prepared. Showing the original description.

See how to install

Files included (3)

  • SKILL.md3.4 KB
  • examples.md1.7 KB
  • README.md767 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

  1. Extract — Identify 3–7 key concepts worth card-ifying. Prioritize mechanisms, tradeoffs, application patterns. Skip long-mastered concepts.
  2. 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.
  3. Test — Present front only; learner responds before seeing back. Self-score: Easy (fluent) / Hard (needed effort) / Failed (wrong/blank). Update interval.
  4. Handle Failures — Failed card: re-test after 10 minutes in same session. Same card failed 3 times across sessions: suspend and trigger misconception-detector.
  5. 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 Never leave outdated cards in the deck.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.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

The English summary is being prepared. Showing the original description.

yugash007/edu-agent-skills10Updated May 19, 2026

Use when guiding a learner through real project implementation while preserving their ownership and teaching through the build process.

The English summary is being prepared. Showing the original description.

yugash007/edu-agent-skills10Updated May 19, 2026

Use when generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.

The English summary is being prepared. Showing the original description.

yugash007/edu-agent-skills10Updated May 19, 2026

Use when verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.

The English summary is being prepared. Showing the original description.

yugash007/edu-agent-skills10Updated May 19, 2026

Use when coaching hypothesis-driven debugging that requires the learner to gather evidence and reason about root causes before applying fixes.

The English summary is being prepared. Showing the original description.

yugash007/edu-agent-skills10Updated May 19, 2026

Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.

The English summary is being prepared. Showing the original description.

yugash007/edu-agent-skills10Updated May 19, 2026

See all skills by yugash007

Report a problem with this skill