Memento Flashcards — Spaced-Repetition Flashcard Skill

Overview

Memento gives you a local, file-based flashcard system with spaced-repetition scheduling. Users can chat with their flashcards by answering in free text and having the agent grade the response before scheduling the next review. Use it whenever the user wants to:

• Remember a fact — turn any statement into a Q/A flashcard
• Study with spaced repetition — review due cards with adaptive intervals and agent-graded free-text answers
• Quiz from a YouTube video — fetch a transcript and generate a 5-question quiz
• Manage decks — organise cards into collections, export/import CSV

All card data lives in a single JSON file. No external API keys are required — you (the agent) generate flashcard content and quiz questions directly.

User-facing response style for Memento Flashcards:

• Use plain text only. Do not use Markdown formatting in replies to the user.
• Keep review and quiz feedback brief and neutral. Avoid extra praise, pep, or long explanations.

When to Use

Use this skill when the user wants to:

• Save facts as flashcards for later review
• Review due cards with spaced repetition
• Generate a quiz from a YouTube video transcript
• Import, export, inspect, or delete flashcard data

Do not use this skill for general Q&A, coding help, or non-memory tasks.

Quick Reference

User intent	Action
"Remember that X" / "save this as a flashcard"	Generate a Q/A card, call memento_cards.py add
Sends a fact without mentioning flashcards	Ask "Want me to save this as a Memento flashcard?" — only create if confirmed
"Create a flashcard"	Ask for Q, A, collection; call memento_cards.py add
"Review my cards"	Call memento_cards.py due, present cards one-by-one
"Quiz me on [YouTube URL]"	Call youtube_quiz.py fetch VIDEO_ID, generate 5 questions, call memento_cards.py add-quiz
"Export my cards"	Call memento_cards.py export --output PATH
"Import cards from CSV"	Call memento_cards.py import --file PATH --collection NAME
"Show my stats"	Call memento_cards.py stats
"Delete a card"	Call memento_cards.py delete --id ID
"Delete a collection"	Call memento_cards.py delete-collection --collection NAME

Card Storage

Cards are stored in a JSON file at:

~/.hermes/skills/productivity/memento-flashcards/data/cards.json

Never edit this file directly. Always use memento_cards.py subcommands. The script handles atomic writes (write to temp file, then rename) to prevent corruption.

The file is created automatically on first use.

Procedure

Creating Cards from Facts

Activation Rules

Not every factual statement should become a flashcard. Use this three-tier check:

1. Explicit intent — the user mentions "memento", "flashcard", "remember this", "save this card", "add a card", or similar phrasing that clearly requests a flashcard → create the card directly, no confirmation needed.
2. Implicit intent — the user sends a factual statement without mentioning flashcards (e.g. "The speed of light is 299,792 km/s") → ask first: "Want me to save this as a Memento flashcard?" Only create the card if the user confirms.
3. No intent — the message is a coding task, a question, instructions, normal conversation, or anything that is clearly not a fact to memorize → do NOT activate this skill at all. Let other skills or default behavior handle it.

When activation is confirmed (tier 1 directly, tier 2 after confirmation), generate a flashcard:

Step 1: Turn the statement into a Q/A pair. Use this format internally:

Turn the factual statement into a front-back pair.
Return exactly two lines:
Q: <question text>
A: <answer text>

Statement: "{statement}"

Rules:

• The question should test recall of the key fact
• The answer should be concise and direct

Step 2: Call the script to store the card:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py add \
  --question "What year did World War 2 end?" \
  --answer "1945" \
  --collection "History"

If the user doesn't specify a collection, use "General" as the default.

The script outputs JSON confirming the created card.

Manual Card Creation

When the user explicitly asks to create a flashcard, ask them for:

1. The question (front of card)
2. The answer (back of card)
3. The collection name (optional — default to "General")

Then call memento_cards.py add as above.

Reviewing Due Cards

When the user wants to review, fetch all due cards:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py due

This returns a JSON array of cards where next_review_at <= now. If a collection filter is needed:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py due --collection "History"

Review flow (free-text grading):

Here is an example of the EXACT interaction pattern you must follow. The user answers, you grade them, tell them the correct answer, then rate the card.

Example interaction:

Agent: What year did the Berlin Wall fall?

User: 1991

Agent: Not quite. The Berlin Wall fell in 1989. Next review is tomorrow. (agent calls: memento_cards.py rate --id ABC --rating hard --user-answer "1991")

Next question: Who was the first person to walk on the moon?

The rules:

1. Show only the question. Wait for the user to answer.
2. After receiving their answer, compare it to the expected answer and grade it:
   • correct → user got the key fact right (even if worded differently)
   • partial → right track but missing the core detail
   • incorrect → wrong or off-topic
3. You MUST tell the user the correct answer and how they did. Keep it short and plain-text. Use this format:
   • correct: "Correct. Answer: {answer}. Next review in 7 days."
   • partial: "Close. Answer: {answer}. {what they missed}. Next review in 3 days."
   • incorrect: "Not quite. Answer: {answer}. Next review tomorrow."
4. Then call the rate command: correct→easy, partial→good, incorrect→hard.
5. Then show the next question.

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py rate \
  --id CARD_ID --rating easy --user-answer "what the user said"

Never skip step 3. The user must always see the correct answer and feedback before you move on.

If no cards are due, tell the user: "No cards due for review right now. Check back later!"

Retire override: At any point the user can say "retire this card" to permanently remove it from reviews. Use --rating retire for this.

Spaced Repetition Algorithm

The rating determines the next review interval:

Rating	Interval	ease_streak	Status change
hard	+1 day	reset to 0	stays learning
good	+3 days	reset to 0	stays learning
easy	+7 days	+1	if ease_streak >= 3 → retired
retire	permanent	reset to 0	→ retired

• learning: card is actively in rotation
• retired: card won't appear in reviews (user has mastered it or manually retired it)
• Three consecutive "easy" ratings automatically retire a card

YouTube Quiz Generation

When the user sends a YouTube URL and wants a quiz:

Step 1: Extract the video ID from the URL (e.g. dQw4w9WgXcQ from https://www.youtube.com/watch?v=dQw4w9WgXcQ).

Step 2: Fetch the transcript:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/youtube_quiz.py fetch VIDEO_ID

This returns {"title": "...", "transcript": "..."} or an error.

If the script reports missing_dependency, use terminal with a PM-prepared Hermes checkout to prepare the declared youtube extra, then reactivate:

python -c "import pm; pm.sync_venv(['youtube'], explicit=True)"
source ./activate
python -c "import youtube_transcript_api; print(youtube_transcript_api.__file__)"

Follow the isolated development-home setup in Package Management before preparation. Retry youtube_quiz.py with that Python and the actual skill directory returned by skill_view. For a remote or sandbox terminal, prepare an independent helper environment on that host. Never pip-install into Hermes's selected environment.

Step 3: Generate 5 quiz questions from the transcript. Use these rules:

You are creating a 5-question quiz for a podcast episode.
Return ONLY a JSON array with exactly 5 objects.
Each object must contain keys 'question' and 'answer'.

Selection criteria:
- Prioritize important, surprising, or foundational facts.
- Skip filler, obvious details, and facts that require heavy context.
- Never return true/false questions.
- Never ask only for a date.

Question rules:
- Each question must test exactly one discrete fact.
- Use clear, unambiguous wording.
- Prefer What, Who, How many, Which.
- Avoid open-ended Describe or Explain prompts.

Answer rules:
- Each answer must be under 240 characters.
- Lead with the answer itself, not preamble.
- Add only minimal clarifying detail if needed.

Use the first 15,000 characters of the transcript as context. Generate the questions yourself (you are the LLM).

Step 4: Validate the output is valid JSON with exactly 5 items, each having non-empty question and answer strings. If validation fails, retry once.

Step 5: Store quiz cards:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py add-quiz \
  --video-id "VIDEO_ID" \
  --questions '[{"question":"...","answer":"..."},...]' \
  --collection "Quiz - Episode Title"

The script deduplicates by video_id — if cards for that video already exist, it skips creation and reports the existing cards.

Step 6: Present questions one-by-one using the same free-text grading flow:

1. Show "Question 1/5: ..." and wait for the user's answer. Never include the answer or any hint about revealing it.
2. Wait for the user to answer in their own words
3. Grade their answer using the grading prompt (see "Reviewing Due Cards" section)
4. IMPORTANT: You MUST reply to the user with feedback before doing anything else. Show the grade, the correct answer, and when the card is next due. Do NOT silently skip to the next question. Keep it short and plain-text. Example: "Not quite. Answer: {answer}. Next review tomorrow."
5. After showing feedback, call the rate command and then show the next question in the same message:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py rate \
  --id CARD_ID --rating easy --user-answer "what the user said"

6. Repeat. Every answer MUST receive visible feedback before the next question.

Export/Import CSV

Export:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py export \
  --output ~/flashcards.csv

Produces a 3-column CSV: question,answer,collection (no header row).

Import:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py import \
  --file ~/flashcards.csv \
  --collection "Imported"

Reads a CSV with columns: question, answer, and optionally collection (column 3). If the collection column is missing, uses the --collection argument.

Statistics

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py stats

Returns JSON with:

• total: total card count
• learning: cards in active rotation
• retired: mastered cards
• due_now: cards due for review right now
• collections: breakdown by collection name

Pitfalls

• Never edit cards.json directly — always use the script subcommands to avoid corruption
• Transcript failures — some YouTube videos have no English transcript or have transcripts disabled; inform the user and suggest another video
• Optional dependency — youtube_quiz.py needs youtube-transcript-api; use the PM preparation and interpreter check above if missing.
• Large imports — CSV imports with thousands of rows work fine but the JSON output may be verbose; summarize the result for the user
• Video ID extraction — support both youtube.com/watch?v=ID and youtu.be/ID URL formats

Verification

Verify the helper scripts directly:

python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py stats
python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py add --question "Capital of France?" --answer "Paris" --collection "General"
python3 ~/.hermes/skills/productivity/memento-flashcards/scripts/memento_cards.py due

If you are testing from the repo checkout, run:

scripts/run_tests.sh tests/skills/test_memento_cards.py tests/skills/test_youtube_quiz.py -q

Agent-level verification:

• Start a review and confirm feedback is plain text, brief, and always includes the correct answer before the next card
• Run a YouTube quiz flow and confirm each answer receives visible feedback before the next question