flashcards

name

flashcards

description

Produces ASCII flashcards (question on the front, answer on the back, separated by a fold marker) for retrieval practice on a topic or piece of code. Each card targets one atomic fact, anchored to a citation. The user generates the question phrasing; the agent verifies the answer against the real artefact. Use when the user wants reusable retrieval material after a learning session, when capturing facts from a /explain-and-check or /quiz-me session, or anytime they say "give me flashcards" or "I want to drill this".

Flashcards

Speak to the user in their language; these instructions are in English for the agent.

A generative skill that produces a markdown file of ASCII flashcards under learn/flashcards/. Each card is one atomic fact, with a clearly-folded answer. Cards are a vehicle for retrieval practice — see ADR 0001 — not a methodology in themselves. This skill produces the artefact; /quiz-me is the methodology that uses it.

Quick start

User: /flashcards src/auth/session.ts
Agent: Project read first. Stack: TypeScript + NextAuth 5.0.
       Cards will be saved to learn/flashcards/session-cards.md. I'll prompt you for
       the question phrasing on each card; I'll verify and write the answer.
       Tell me the first thing you want to be able to recall from memory.

The file is plain markdown. Compatible with eye-review and with simple flashcard importers via copy-paste; we don't generate binary deck formats.

Workflows

Mode detection

Inspect the argument:

• Code-mode — argument is a file or PR. Cards target facts about this code (invariants, signatures, behaviour edges).
• Topic-mode — argument is a concept. Cards target facts about the topic at the project's stack version.

Card and file format

See CARD-FORMAT.md for the exact card block, file structure, and the three non-negotiable properties (atomic, question-from-user, cited). Default file size: 5–15 cards.

Generation protocol

1. Project read. Read package.json / lockfile / pyproject.toml. Cards must reflect the actual version in use.
2. Read the artefact (code-mode) or fetch the docs (topic-mode). No card is written if the answer cannot be sourced.
3. Ask the user for the first question front. Examples of good prompts:
   • "What's one thing about this code you'd want to recall a week from now without looking?"
   • "What's the first specific fact about this topic you want to drill?"
4. Atomicity check. If the front contains "and" or multiple clauses, ask the user to split.
5. Source the answer. Read code (Read tool) or docs (WebFetch / context7) to write a verified back. Cite. Never write an answer from memory.
6. Loop. Continue until the user says stop, or 15 cards, whichever first.
7. Write the file to learn/flashcards/<slug>-cards.md using the Write tool.
8. Offer the follow-up. After saving, suggest: "Run /quiz-me learn/flashcards/<slug>-cards.md when you want to practise these."

Workspace mode

If CURRICULUM.md exists in the current directory, the skill is running inside a Scenario B workspace. In workspace mode:

• Cards go to flashcards/<slug>-cards.md (not learn/flashcards/).

If CURRICULUM.md is absent (legacy mode), cards go to learn/flashcards/ as before.

Using the cards later

This skill doesn't practise the cards — it only generates them. Practice happens via /quiz-me on the file. The agent reading the cards file should treat each Front as a recall prompt and verify the user's answer against the Back + citation.

Anti-patterns

• Don't author question fronts for the user — the user phrases what they want to recall, because what they choose to be tested on is itself diagnostic information about their model; agent-authored fronts collapse retrieval into recognition.
• Don't write answers from memory — every back is sourced before being written, otherwise the card becomes a durable carrier of borrowed confidence that the user will absorb on every review.
• Don't write compound cards (two facts in one) — compound cards make it impossible to distinguish "knew one fact and guessed the other" from "knew both"; atomicity is what makes recall signal interpretable.
• Don't generate without citations because "it's basic" — cite anyway; cards are durable, and basic-looking facts often turn out to be the version-specific ones that drift over time.
• Don't put the answer above the fold — the <details> fold is the entire point of the format; visible answers turn cards into reading material, which doesn't drive retrieval.
• Don't write more than 15 cards in one session — fatigue produces lower-quality phrasing, which produces lower-quality recall later; the user pays the cost on every future review.

Governing principles (this skill satisfies all five)

1. Agent withholds — answers folded; the user produces the front phrasing.
2. Student speaks first — the user's recall need is the input.
3. Artefact is the judge — every back is verified against the real code/docs.
4. Source fidelity — every back ends with a citation to an authoritative source; stack version anchored before generation. Policy: ../../docs/sources.md.
5. Exit is a transfer test — the cards themselves are transfer-test fuel for /quiz-me later.