Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jayrha/agentskills/flashcard-generatornpx skills add JayRHa/AgentSkills --skill flashcard-generatorgit clone --depth 1 https://github.com/JayRHa/AgentSkillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00113 | $0.01024 |
| Opus 5 | $0.00056 | $0.00512 |
| Sonnet 5 | $0.00023 | $0.00205 |
| Haiku 4.5 | $0.00011 | $0.00102 |
Grade A, and why
flashcard-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flashcard Generator
Overview
Convert material into flashcards that are actually effective for long-term memory: each card tests one atomic fact, is phrased for active recall, and is unambiguous. Follow the formulation principles in references/card-principles.md (based on SuperMemo's "20 rules" and spaced-repetition research).
Keywords: flashcards, Anki, spaced repetition, SRS, active recall, cloze deletion, memorization, study cards, Q&A, mnemonics, learning.
Workflow
- Identify the learning goal. What must the user be able to recall or do? Cards should serve that goal, not just restate text.
- Extract atomic facts. Break the material into the smallest meaningful units. One card = one fact. Split any "and"/list-laden card.
- Choose a card type per fact (see
references/card-principles.md):- Basic Q/A — for a single fact ("What year…?", "What does X do?").
- Cloze deletion — for facts embedded in a sentence (
The {{c1::mitochondria}} is the powerhouse of the cell). - Reversed — when recall is needed both directions (term↔definition).
- Image/application — for procedures or visual recall.
- Formulate for recall, not recognition. Questions must have a specific, unambiguous answer. Avoid yes/no and "list everything about X" cards.
- Apply the minimum information principle. Simple, short cards review faster and stick better than dense ones.
- Add context only when needed to disambiguate (e.g. tag the subject) — but keep cues out of the answer.
- Review for interference — cards that are too similar cause mix-ups. Differentiate them.
- Export. Produce a clean list and/or run
scripts/to_anki_csv.pyto generate an Anki-importable file (Basic and Cloze note types).
Decision Framework
| Fact shape | Card type |
|---|---|
| Single discrete fact | Basic Q→A |
| Fact inside a sentence/definition | Cloze |
| Term you must recall both ways | Basic + reverse |
| Enumeration/list | Split into N cards, or cloze each item, or use a mnemonic card |
| Procedure/steps | One card per step, or "what comes after step N?" |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 75 lines · 113 tokens per session scan A a2bdfc8203ca
flashcard-generator is a skill published in the GitHub repository JayRHa/AgentSkills (4 stars, last pushed 1mo ago), licensed MIT. It adds 113 tokens to every session and 1,024 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
agent-newbie-guide
Skill "agent-newbie-guide" from hashgraph-online/awesome-codex-plugins, covering codex 新手引导, 🚫 新手术语过滤（绝对禁止）, 统一行为规范（内嵌）, 📍 阶段位置 and 启动自检.
agent-learning-coach
中文学习教练技能。用于学习编程、英语、设计、产品、AI、数学或任何技能时，先诊断水平，再用讲解、练习、反馈、复习的循环推进。触发语包括"进入学习模式""我想学""带我练""帮我制定学习计划""像教练一样教我"。.
awesome-web-security
Looks up curated web security learning resources (XSS, SQLi, CSRF, SSRF, OAuth/JWT, deserialization, SAML, recon, evasion, defensive tooling, CTF). Filters by topic, difficulty, language, and resource type. Returns top references with archive fallbacks. Defensive and educational use only.
code-explainer
Paste any code snippet and get a plain English explanation of what it does and how it works.
course-fit-check
Evaluates whether a specific course, certification, bootcamp, or training is worth the time and money for the user's stated career goal, weighing recruiter signal, time and opportunity cost, risks, and the artifact it produces. Use when the user asks whether a course, certification, bootcamp, or training is worth it…
explain
Explains selected or visible content in plain language, including the core idea, jargon, and background, while researching further when the page falls short and staying evidence-backed. Use when the user says "explain this", "what does this mean", "simplify", "break this down", "ELI5", or "help me understand" about…