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 serejaris/kimi-skills --skill flashcard-studiogit clone --depth 1 https://github.com/serejaris/kimi-skillsWrote 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/serejaris/kimi-skills/flashcard-studio)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/flashcard-studio"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/flashcard-studio/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/serejaris/kimi-skills/flashcard-studio"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/flashcard-studio.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 | $0.00099 | $0.01134 |
| Opus 5 | $0.00049 | $0.00567 |
| Sonnet 5 | $0.00020 | $0.00227 |
| Haiku 4.5 | $0.00010 | $0.00113 |
Grade A, and why
flashcard-studio 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 8d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
flashcard-generator
从学习材料中自动提取知识点，生成「正面问题 + 反面答案」格式的闪卡，输出可直接导入 Anki 的 CSV 文件。
支持两种工作模式：
- auto 模式：基于规则从 Markdown/纯文本中提取定义、Q&A、列表等结构化知识点
- json 模式：接收预构造的 JSON 闪卡数据，格式化为 Anki CSV
Quick Start
# 从 Markdown 笔记自动提取闪卡
python scripts/generate_flashcards.py --input notes.md --output flashcards.csv
# 从 JSON 数据生成 Anki CSV（适合 agent 调用）
python scripts/generate_flashcards.py --mode json --input cards.json --output flashcards.csv
# 通过 stdin/stdout 使用
cat notes.md | python scripts/generate_flashcards.py > flashcards.csv
Agent 工作流
当用户提供学习材料要求生成闪卡时，推荐流程：
- 读取材料：读取用户提供的学习材料文件
- 智能提取：分析材料内容，提取核心知识点，生成高质量的问答对。遵循以下原则：
- 每张卡片聚焦一个知识点（最小信息原则）
- 正面用精确的问题形式，避免模糊提问
- 反面给出简洁但完整的答案
- 覆盖核心概念、定义、公式、因果关系、对比等
- 生成 CSV：将提取的问答对写为 JSON，调用脚本转为 Anki CSV
- 交付文件：告知用户输出路径和导入方法
Agent 调用示例
将提取的知识点构造为 JSON 数组，通过 --mode json 转为 CSV：
cat <<'EOF' > /tmp/cards.json
[
{"front": "什么是光合作用？", "back": "植物利用光能将CO₂和H₂O转化为有机物并释放O₂的过程", "tags": "biology"},
{"front": "光合作用的化学方程式是什么？", "back": "6CO₂ + 6H₂O → C₆H₁₂O₆ + 6O₂", "tags": "biology"}
]
EOF
python scripts/generate_flashcards.py --mode json --input /tmp/cards.json --output flashcards.csv
参数说明
| 参数 | 说明 | 默认值 |
|---|---|---|
--input, -i |
输入文件路径 | stdin |
--output, -o |
输出 CSV 文件路径 | stdout |
--mode, -m |
提取模式：auto（规则提取）或 json（结构化输入） |
auto |
--no-tags |
不输出 tags 列 | 包含 tags |
--separator, -s |
CSV 分隔符：\t、;、, |
Tab |
输出格式
生成的 CSV 遵循 Anki 导入规范：
#separator:Tab
#html:true
#columns:Front Back Tags
什么是光合作用？ 植物利用光能将CO₂和H₂O转化为有机物并释放O₂的过程 biology
导入 Anki 的步骤
- 打开 Anki → 文件 → 导入
- 选择生成的 CSV 文件
- Anki 会自动识别分隔符和列映射
- 确认后点击「导入」
Auto 模式支持的知识结构
| 结构类型 | 示例 | 生成的闪卡 |
|---|---|---|
| 定义（Term: Definition） | 光合作用：植物利用光能... |
Q: 什么是光合作用？ A: 植物利用光能... |
| Q&A 对 | Q: 什么是DNA？ A: 脱氧核糖核酸 |
直接提取为闪卡 |
| 标题+列表 | ## 细胞器 - 线粒体 - 核糖体 |
Q: 细胞器的关键要点有哪些？ A: 列表 |
| 标题+段落 | ## 牛顿第一定律 一切物体... |
Q: 请解释：牛顿第一定律 A: 段落内容 |
What ships with it
2 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.
- 8d ago First seen · 96 lines · 99 tokens per session scan A a7f5169b3061
flashcard-studio is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 99 tokens to every session and 1,134 once invoked, about $0.0005 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 2026-09-03.
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