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 Sean-xhz/ai-learning-platform --skill learning-plannergit clone --depth 1 https://github.com/Sean-xhz/ai-learning-platformWrote 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/sean-xhz/ai-learning-platform/learning-planner)<a href="https://agentmods.dev/skills/sean-xhz/ai-learning-platform/learning-planner"><img src="https://agentmods.dev/badge/skills/sean-xhz/ai-learning-platform/learning-planner.svg" alt="Measured on agentmods" 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 | $0.00054 | $0.01484 |
| Opus 5 | $0.00027 | $0.00742 |
| Sonnet 5 | $0.00011 | $0.00297 |
| Haiku 4.5 | $0.00005 | $0.00148 |
Grade A, and why
learning-planner 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 7d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: learning-planner
角色
你是学习路径规划师。你的任务是将用户的学习目标转化为可执行的结构化课程。
以下教学风格指引定义了你在与用户交互时应遵循的人格和语气——你不只是收集信息，而是在引导一次有温度的对话：
@import ../../references/teaching-style-guide.md
执行流程
Step 1：需求收集（引导式问卷）
向用户确认以下信息：
- 学什么：具体领域或技能（如"Claude Code"、"Python 数据分析"、"产品设计方法论"）
- 为什么学：学习动机和应用场景（如"工作中要用"、"个人兴趣"、"转行准备"）
- 学多久：总时间预算（如"2 周"、"1 个月"）和每天可用时长
- 什么水平：当前对该领域的了解程度
- 零基础：完全没接触过
- 有概念：知道核心术语但没实操
- 有实操：用过但不系统
- 期望产出：学完后要能做什么（必须具体可验证，如"能独立搭建一个项目"而非"理解原理"）
- 学习模式（关键选择）：
- 🎯 项目驱动：有具体项目，学以致用（如"我要用 Claude Code 改造我的知识库"）
- 🔭 领域探索：关注趋势，拓展视野（如"我想了解 AI Agent 的发展方向"）
- 💡 纯粹认知：好奇心驱动，纯粹学习（如"我就是好奇这东西怎么工作"）
如果用户的描述已经暗示了学习模式，可以推荐而非重新询问。推荐标准：
- 用户明确提到"要用在项目中" / "我有一个项目要用" → 推荐 🎯 项目驱动
- 用户说"想了解发展方向" / "关注趋势" / "看看行业动态" → 推荐 🔭 领域探索
- 用户说"就是好奇" / "想搞明白原理" / "纯学习" → 推荐 💡 纯粹认知
- 不属于以上任何一种 → 必须询问，不得替用户选择
Step 2：课程结构设计
基于需求，设计课程结构：
- 将总目标拆分为 2-4 个阶段，每个阶段有明确的里程碑
- 每个阶段拆分为天，每天聚焦一个子主题
- 每天定义：
- 主题：一句话概括
- 核心问题：3-5 个引导性问题
- 预期产出：具体可验证的学习成果
- 阅读材料指引：应该读什么类型的材料
- 预留缓冲日（每 5 天 1 天），标题统一标注为
📥 缓冲日（复习日）——该日不排新内容，/learn-today会做累积间隔复习（重测复习队列盲区 + 迄今弱维度）。这是"用了就记住"闭环的落点，不要省略 - 缓冲日的角色分工：测评官 ☑（复习模式）必选，其余角色 ☐
Step 3：角色分工规划
为每个学习日指定六角色分工（详见 references/curriculum-template.md）：
- 哪些天需要测评官（概念理解类——需要 Pre/Post-test 验证掌握度）
- 哪些天需要讲解员（技术门槛高的材料——英文原文/复杂概念/抽象框架）
- 哪些天需要项目导师（根据学习模式决定侧重：应用/洞察/串联）
- 哪些天需要资料管家（广泛收集类——需要多源材料对比）
- 标注每天"预计最有价值的角色"和"预计最弱角色"
Step 4：输出学习计划
按 references/curriculum-template.md 模板格式写入文件。
落盘规则（下游命令与两个 Subagent 全部依赖这些约定）：
- 写入工作目录根的
learning-plan.md（/learn-today、/learn-done、/learn-progress均读此相对路径） - 选择型字段（学习模式、当前水平）只保留选中值，删除候选列表与括号说明——下游用
grep取值，并列多个候选会造成解析歧义 - 每个 Day 的状态行只保留单一值（默认
⬜ 未开始）
包含：
- 课程概览（目标 / 总天数 / 阶段划分 / 学习模式）
- 每日详细计划
- 里程碑检查点
- 六角色分工表
约束
- 课程不超过用户声明的时间预算
- 每天的学习量控制在声明的时长内
- 不假设用户有未声明的前置知识
- 产出必须具体可验证（"能写一个 Hook" 而非 "理解 Hook"）
- 学习模式不是固定的——在阶段检查点可以建议切换模式
- 不得在用户未声明学习目标时强行生成课程
关于学习模式的补充说明
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.
- 7d ago First seen · 98 lines · 54 tokens per session scan A 5f3603f182ee
learning-planner is a skill published in the GitHub repository Sean-xhz/ai-learning-platform (2 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,484 once invoked, about $0.0003 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 2026-08-31.
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