flashcards

flashcards is a skill for Claude Code, Codex from open-octo/octo-agent. It costs 121 tokens per session (2,034 once invoked), scanned A, original, MIT.

A lightweight flashcard and review method for learning facts and concepts in a conversation. It uses Leitner boxes, a simple system that shows cards at increasing intervals as they are remembered.

In plain words
What is it for?
It helps create subject-based cards, run quick study sessions, review difficult items, and track basic progress without replacing dedicated tools such as Anki.
Why use it?
It helps turn notes or supplied material into small recall exercises and highlights concepts that still need practice.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps create subject-based cards, run quick study sessions, review difficult items, and track basic progress without replacing dedicated tools such as Anki.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/open-octo/octo-agent/flashcards
Install

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.

Any agent
npx skills add open-octo/octo-agent --skill flashcards
Clone the repo
git clone --depth 1 https://github.com/open-octo/octo-agent

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for flashcards

README.md
[![agentmods](https://agentmods.dev/badge/skills/open-octo/octo-agent/flashcards/github.svg)](https://agentmods.dev/skills/open-octo/octo-agent/flashcards)
Your own site
<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.

agentmods 80×15 button for flashcards

Your own site · 80×15
<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>
Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,034 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 5d ago against content hash 1ec81738a4e9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

internal/skills/experts/flashcards/SKILL.md · 155 lines

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

  1. 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。
  2. 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。
  3. 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。
  4. 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:

  1. 下次复习 <= 今天 且分桶不是 mastered 的卡
  2. 还没复习过的新卡
  3. 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):

  1. 显示问题,等用户回答
  2. 用户回答(或输入"跳过"/"不会")
  3. 显示答案
  4. 用户自评: / 部分对 / / 不会
  5. 按下表更新分桶和下次复习时间:
自评 分桶变化 下次复习
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
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。
  • --drill 流程逐张跑完N张。
  • 小节结束后,把结果追加到 study-plan.yamlsession_history

Read the full file on GitHub · 155 lines

Files

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.

Changes

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.

  1. 5d ago First seen · 155 lines · 121 tokens per session scan A 1ec81738a4e9

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

baoyu-comic

A tool for creating educational comics from supplied text, files, links, or topics. It can cover explanations, biographies, and tutorials in different visual styles, tones, layouts, sizes, and languages.

StarryCod/cogitum · 17 tokens

manim-video

Manim CE animations: 3Blue1Brown math/algo videos.

StarryCod/cogitum · 19 tokens

ha-skill-creator

Create, edit, improve, or audit Hope Agent skills. Use when the user wants to: (1) create a new skill from scratch, (2) edit or improve an existing skill, (3) review or clean up a SKILL.md file, (4) run evaluations to test skill effectiveness, (5) optimize skill descriptions for better trigger accuracy. Trigger…

shiwenwen/hope-agent · 106 tokens

ha-mac-control

Hope Agent native macOS desktop control — the standard maccontrol status / diagnostics / apps / dock / spaces / snapshot / visual / windows / menu / clipboard / dialog loop, target-first action rules, no-blind-coordinate policy, and recovery for stale AX/window/menu/dialog state. Load whenever using maccontrol, or…

shiwenwen/hope-agent · 139 tokens

ha-browser

Hope Agent browser automation — the standard status → tabs → snapshot → act loop, stale-ref recovery rules, and what to do when login / 2FA / captcha / camera-prompt / dialog blocks progress. Load this skill whenever you reach for the browser tool. Trigger on: user asks the agent to open / control / click / scrape /…

shiwenwen/hope-agent · 140 tokens

ha-logs

A read-only troubleshooting skill for querying Hope Agent’s local SQLite databases, which store logs, conversations, and background-job status.

shiwenwen/hope-agent · 183 tokens