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 skills add niracler/skill --skill anki-card-generatorgit clone --depth 1 https://github.com/niracler/skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/niracler/skill/anki-card-generator)<a href="https://agentmods.dev/skills/niracler/skill/anki-card-generator"><img src="https://agentmods.dev/badge/skills/niracler/skill/anki-card-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/niracler/skill/anki-card-generator"><img src="https://agentmods.dev/badge/skills/niracler/skill/anki-card-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00133 | $0.01246 |
| Opus 5.5 | $0.00053 | $0.00498 |
| Sonnet 5.5 | $0.00027 | $0.00249 |
| Haiku 4.5 | $0.00013 | $0.00125 |
Grade A, and why
anki-card-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 1mo 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anki Card Generator
Generate high-quality Anki cards based on LessWrong best practices and simple-anki-sync format.
Pre-Generation Clarification
Before generating cards, clarify with user if unclear:
- Scope: "Which aspects to focus on?" (for broad topics)
- Depth: "Basic concepts only, or detailed Level 2/3 cards?"
- Quantity: "How many cards? (Recommend 5-10 for core concepts)"
- Context: "Any specific exam or application scenario?"
Proceed only after understanding requirements.
Output Format
Use simple-anki-sync format:
#anki/[domain]/[topic]
| [Question] |
| ---------- |
| [Core answer]<br><br><small>💡 [Supplementary info]</small> |
Format Options
Option A (Recommended): HTML tags
| 唐朝建立时间 |
| ---------- |
| 618年，李渊建立<br><br><small>💡 隋末农民起义后起兵</small> |
Option B: Separator
| 唐朝建立时间 |
| ---------- |
| 618年，李渊建立 ——— 💡 隋末农民起义后起兵 |
Option C: Parentheses
| 唐朝建立时间 |
| ---------- |
| 618年，李渊建立（趣闻：其子李世民功劳最大） |
Atomization Rules
Word Limits
- English: Max 9 words, absolute limit 18 words
- Chinese: Recommended 15-20 characters, absolute limit 30-35 characters
- Max items: 3 bullet points per card
Core Principle
If a card can be split into two shorter cards, split it.
Question Design
Standardized Templates
- Time: "X 时间" (not "X发生于何时？")
- Definition: "X 定义" (not "什么是X？")
- Person: "who X" (not "谁做了X？")
- Pros/Cons: "X 利弊" (not "X的优势是什么？")
Key Rules
- Match real-world recall scenarios
- Use plain, unremarkable wording
- Avoid words in question that appear in answer
- Keep all critical info in answer, not question
Answer Construction
Core Answer
- Strictly follow word limits
- Answer should be meaningful without the question
- All key information in answer
Supplementary Info (Optional)
Format: <br><br><small>💡 content</small>
Emoji Guide:
- 💡 Fun fact / trivia
- 📝 Note / explanation
- 🔗 Related concept
- ⚡ Tip / key point
- 📊 Data / statistics
- 📅 Date / timeline
What ships with it
1 file 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.
- 1mo ago First seen · 191 lines · 133 tokens per session scan A 9ffb2fe5575e
anki-card-generator is a skill published in the GitHub repository niracler/skill (11 stars, last pushed 3mo ago), licensed MIT. It adds 133 tokens to every session and 1,246 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.