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 open-octo/octo-agent --skill flashcardsgit clone --depth 1 https://github.com/open-octo/octo-agentWrote 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/open-octo/octo-agent/flashcards)<a href="https://agentmods.dev/skills/open-octo/octo-agent/flashcards"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/flashcards/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/open-octo/octo-agent/flashcards"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/flashcards.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00121 | $0.02034 |
| Opus 5 | $0.00060 | $0.01017 |
| Sonnet 5 | $0.00024 | $0.00407 |
| Haiku 4.5 | $0.00012 | $0.00203 |
Grade A, and why
flashcards 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 5d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: flashcards
大纲/笔记是用来综合理解的,记忆卡片是用来强记的。这个技能从用户提供的材料 (或在用户明确要求时,从模型知识)生成卡片,用轻量间隔重复复习,并追踪哪些 概念还没记牢。
不是完整的 SRS 系统。 只是简单的 Leitner 分桶。够用、不重——如果用户已经 习惯用 Anki 之类的专门工具,建议继续用那个;这个技能是给在对话里想快速过一遍 的场景用的。
置信度纪律
- 从用户提供的材料(笔记、教材节选、课件)生成的卡片:内容来自材料本身, 可信。
- 从模型知识(用户没给材料)生成的卡片:每张卡片如果陈述的规则不是模型有
把握的,标注
[待核实]。生成前先说清楚"这些卡是从我的知识生成的,不是 从你的材料,请自行核对"。 - 如果某个领域模型把握不大,宁可少生成几张有把握的卡,也不要为了凑数瞎编。 8张靠谱的卡比20张里有5张错的要好。
存储
~/.octo/learning-data/flashcards/[学科]/cards.md
每个学科一个文件,跨会话持久(不受技能本身版本更新影响)。
卡片结构
### 卡片 [N]
**问:** [问题——一个概念一张卡]
**答:** [答案——规则/定义,一到两句话]
**来源:** [材料出处:笔记/教材页码/课堂日期]
**分桶:** new
**上次复习:** —
**下次复习:** [今天日期]
**备注:** [可选——易混淆点、例外情况、常见陷阱]
写卡规则
- 一个概念一张卡。 "光合作用的三个阶段"应该拆成3张卡,不是1张。
- 正面是问题,不是主题。 "光合作用"不好;"光合作用的三个阶段分别是 什么"好。
- 背面是规则/定义,不是一段话。 如果答案需要一整段才能说清楚,拆成 多张卡。
- 标注来源,方便复习时回去核对。
模式
Flag: --generate | --drill | --review | --stats | --session <n>(不带 flag 时先问用户要哪种)
--generate——生成卡片
输入: 学科/主题、来源材料(笔记路径、教材节选,或"就用我已有的大纲")、 可选的目标卡片数量(默认每次10-20张)。
按上面的卡片结构和写卡规则生成,写入 ~/.octo/learning-data/flashcards/ [学科]/cards.md(已有文件则增量追加,不要覆盖)。
如果是从模型知识生成的:每张卡的规则/答案是模型生成的,未经核实。用户 背下来之前,建议对照教材、笔记或权威来源核实一遍——背了一张错卡比没有这张 卡更糟。
--drill(默认)——复习
优先级:
下次复习 <= 今天且分桶不是 mastered 的卡- 还没复习过的新卡
- 如果没有到期卡也没有新卡:问用户要不要复习已经 mastered 的卡(防遗忘)
复习流程(逐张):
- 显示问题,等用户回答
- 用户回答(或输入"跳过"/"不会")
- 显示答案
- 用户自评:
对/部分对/错/不会 - 按下表更新分桶和下次复习时间:
| 自评 | 分桶变化 | 下次复习 |
|---|---|---|
| 对 | 升一档(new→learning→review→mastered) | +1天(new) / +3天(learning) / +7天(review) / +21天(mastered) |
| 部分对 | 不变 | +1天 |
| 错 | 降一档(review→learning;learning→new;new不变) | 今天+4小时 |
| 不会 | 降一档 | 今天+4小时 |
--review——浏览卡组
按分桶分组展示某学科的所有卡片。用于快速扫一眼卡组内容,或手动调整卡片。
--stats——进度快照
按学科统计:卡片总数、各分桶分布、今天到期数、本周已复习数。标出反复降到
new 两次以上的卡——这些是真正卡住的概念,值得用 weak-point-drill 或
向老师/教材再确认一遍,光靠卡片记不住的东西说明理解上有缺口。
--session <n>——聚焦N卡片小节
用户说"来5张合同法的卡"或类似需求时用这个模式。
- 读取
~/.octo/learning-data/study-plan.yaml(如果存在)里该学科的session_history。 - 优先级:之前答错的卡 > 到期卡 > 新卡。
- 按
--drill流程逐张跑完N张。 - 小节结束后,把结果追加到
study-plan.yaml的session_history:
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.
- 5d ago First seen · 155 lines · 121 tokens per session scan A 1ec81738a4e9
flashcards is a skill published in the GitHub repository open-octo/octo-agent (97 stars, last pushed yesterday), licensed MIT. It adds 121 tokens to every session and 2,034 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 2026-09-03.
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