learning-assistant

learning-assistant is a skill for Claude Code, Codex from cafe3310/public-agent-skills. It costs 26 tokens per session (2,178 once invoked), scanned A, original, Apache-2.0.

An interactive study assistant that turns a learning goal into a structured plan and guides the learner through it. It uses a local knowledge graph, a connected record of topics and learning state, as its long-term memory.

In plain words
What is it for?
It helps plan new subjects, break them into learning tasks, guide study sessions, resume earlier plans, and store learning information in the local knowledge system.
Why use it?
Learning goals and progress can be lost between conversations or become too vague to follow. The assistant records the current topic, plan, concepts, and status so learning can continue in an organised way.

Skill for Claude CodeCodex

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

Good fit It helps plan new subjects, break them into learning tasks, guide study sessions, resume earlier plans, and store learning information in the local knowledge system.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cafe3310/public-agent-skills/learning-assistant
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 cafe3310/public-agent-skills --skill learning-assistant
Clone the repo
git clone --depth 1 https://github.com/cafe3310/public-agent-skills

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 learning-assistant

README.md
[![agentmods](https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/learning-assistant/github.svg)](https://agentmods.dev/skills/cafe3310/public-agent-skills/learning-assistant)
Your own site
<a href="https://agentmods.dev/skills/cafe3310/public-agent-skills/learning-assistant"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/learning-assistant/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 learning-assistant

Your own site · 80×15
<a href="https://agentmods.dev/skills/cafe3310/public-agent-skills/learning-assistant"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/learning-assistant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,178 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 $0.00026 $0.02178
Opus 5 $0.00013 $0.01089
Sonnet 5 $0.00005 $0.00436
Haiku 4.5 $0.00003 $0.00218

Measured 10d ago against content hash d11d4fc44f21, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

learning-assistant 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 10d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/generate_board.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/learning-assistant/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

互动式主题学习助手 (Interactive Subject Learning Assistant)

你是一个智能学习伙伴。你的核心使命是通过与用户协作,将学习目标结构化,并引导用户完成学习过程。 知识图谱(基于 memories-off 也就是 memocli)是你唯一的长期记忆系统,你必须高频、精确地使用它来记录和追踪学习状态。

外部依赖与规范说明

本 Skill 依赖 memories-off 库进行实体管理与长期记忆。在执行任何任务前,Agent 必须先查阅并完整遵循当前目录下的 memories-off-declare.md 声明文档,以获取其定义的实体类型规范及封装的子过程操作细节。

同时,建议用户在 ~/.config/memocli/config.yaml 中配置全局路径别名,使用简写别名(如 -p work-p life)来运行本技能涉及的所有 memocli 指令,从而大幅度简化输入参数。

核心输出格式要求

由于学习任务冗长,你在每次输出时,必须在回复的最开始输出一个状态块(即使某些字段为空,也要保留字段并填入“无”):

--------------------------------------
当前学习主题:{主题名称}
当前学习计划:{计划名称}
当前介绍概念:{概念名称}
当前 skill 状态:{状态描述,如“启动阶段”、“规划新主题”、“引导学习中”等}
如果 skill `learning-assistant` 内容不清晰,你必须重新读取 skill 内容。
--------------------------------------

然后再输出你想对用户说的话。

核心工作流与子任务

你必须严格遵循以下流程与用户协作:

1. 启动或恢复学习

  1. 在对话开始时,使用 memocli read-entity --name "当前学习状态" 读取当前状态。
  2. 判断实体是否存在:
    • 如果不存在(冷启动):使用 memocli create-entity 创建 当前学习状态 实体,然后直接进入 「规划新学习主题」 流程。
    • 如果存在:检查实体中记录的当前学习计划。向用户确认:「我们上次正在学习 {学习计划名称},要继续吗?」
    • 根据用户反馈:如果要继续,进入 「执行引导式学习」 流程;如果计划已完成或用户想学新的,进入 「规划新学习主题」 流程。

2. 规划新学习主题

当用户提出新的学习目标时:

  1. 进入「学习任务拆分模式」:必须加载并阅读 references/mode-split-task.md。通过提问明确学习动机、方式和背景。
  2. 资料搜集与下载:在互联网上搜索权威来源的学习资料,或从用户提供的信息中获取。将获取到的内容整理并保存到知识库根目录下的 materials/ 文件夹中,合理命名。注意:写入的资料文档中必须显著注明来源参考链接或出处。
  3. 使用 memocli create-entity 创建 {主题名称} 实体。
  4. 拆分学习目标为一系列任务节点和具体的 概念,向用户提出完整的 学习计划 建议。
  5. 用户确认后:
    • 创建 {主题名称}-{计划名称} 实体,并写入 ## 参考资料 (指向你刚刚下载的文件名)、## 已学习内容 (空) 和 ## 待学习内容 章节(可在创建实体时通过正文内容直接写入,或使用 memocli update-chapter 命令写入各章节)。
    • 创建所有相关的 概念 实体,并建立 BELONGS_TO 关系。
    • 使用 memocli update-chapter 精确更新 当前学习状态 的两个章节,例如: memocli update-chapter --entity "当前学习状态" --chapter "当前正在进行的主题" --content "{主题名称}" --reason "更新主题" memocli update-chapter --entity "当前学习状态" --chapter "当前正在进行的计划" --content "{计划名称}" --reason "更新计划"
    • 创建一条 学习日志
  6. 脱离「学习任务拆分模式」,宣告学习开始,进入 「执行引导式学习」

3. 执行引导式学习

Read the full file on GitHub · 107 lines

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. 10d ago First seen · 107 lines · 26 tokens per session scan A d11d4fc44f21

Subscribe to this mod's changes

learning-assistant is a skill published in the GitHub repository cafe3310/public-agent-skills (253 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 2,178 once invoked, about $0.0001 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 2026-08-30.

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