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 agentmods add skills/yyz666ai/learning-agent/learner-onboardingnpx skills add yyz666ai/Learning-Agent --skill learner-onboardinggit clone --depth 1 https://github.com/yyz666ai/Learning-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/yyz666ai/learning-agent/learner-onboarding)<a href="https://agentmods.dev/skills/yyz666ai/learning-agent/learner-onboarding"><img src="https://agentmods.dev/badge/skills/yyz666ai/learning-agent/learner-onboarding.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 | $0.00036 | $0.00899 |
| Opus 5 | $0.00018 | $0.00449 |
| Sonnet 5 | $0.00007 | $0.00180 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
learner-onboarding 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 3d 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.
What it actually says
学习者建档（先摸情况，再开讲）
只补齐真正缺失的建档信息。若界面已经确认画像，立刻退出本 Skill，不能再用聊天重复摸底。
界面确认快速通道
当请求含 forbid_more_onboarding=true，或画像标记为界面已确认并且 active_plan 可读取时：
- 不再询问、复述或要求用户确认基础、主题、时间与偏好；
- 不重新写画像，不把界面选择降级成
unknown； - 直接交给教学 Skill，从活动计划的当前任务开始第一小步。
执行流程
- 只读
$USER_DIR/learning-state.json和$USER_DIR/profile.md；用户给了项目路径再读项目链接，别扫历史。 - 输入框就是新主题入口。用户直接打字或语音说新内容，先使用
learning-intent-router获得经多轮 slot filling 确认的意图槽位；不先要求他点“学新知识”。 - 已填的主题、目标、结果、基础证据、期限和范围直接复用，不重复询问；只有缺少会改变路线的信息才问。
- 用户的主题可以是语言、库、API、工程、项目、面试方向或单一概念；保留他真正说的主题，不套成默认 Go/Python。
- 用户自己说的是
self_reported，小诊断的结果是diagnostic_evidence，别混成「已掌握」。 - 情况问够了，用一两句话复述你听到的，对方点头才算确认。没确认不写状态。
- 确认后，按
assets/profile-template.md更新画像，按references/state-contract.md写状态。 - 最后只标记「该定学习计划了」，交给
learning-plan，别自己开始上课。
Plan 确认交互
- Plan 准备好后作为对话中的普通 Agent 消息展示，不打开另一个文档框或学习页。
- 审阅 Plan 时收起之前的选择题，只保留一个紧凑的确认按钮。
- 不提供“前面快一点”“多做项目”“我想调整”等预设选项。用户要改时直接在输入框说，立即路由
plan-revision。
概念速学分支
当用户输入“RAG 是什么”“幂等是什么”这类显式概念问句，直接改走 concept_clarity，不再追问学习路线：
- 只问一个会改变教法的点击题：“你只想理解概念，还要看代码实现？”
- 选“只理解概念”记为
meaning_only；不问基础，不问每日时长，不做诊断，直接交给短 Plan。 - 选“还要看代码实现”记为
code_walkthrough；只再问一次代码阅读熟悉度，然后立即开始。仍然不问每日时长，不加 3–4 道起点诊断。 - 不要因为界面有“时长”字段就反向追问。运行时默认值只用于超时与页数上限，不属于用户课表。
- “这个是什么”“这一页怎么理解”类指代问句是当前课程答疑，留在对话中，不新建学习项目。
边界
- 「看看我的进度」→ 走
learning-progress，不是重新建档。 - 目标代码默认只读；不读
.env、凭据、构建产物。 - 不知道的就记
unknown，别猜对方水平、时间。 - 写失败就说失败，别假装保存了。
What ships with it
4 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.
- 3d ago First seen · 51 lines · 36 tokens per session scan A 0167a71ab6ea
learner-onboarding is a skill published in the GitHub repository yyz666ai/Learning-Agent (1 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 899 once invoked, about $0.0002 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.
Other skills, from other repositories
flowchart
生成教育/科普类流程图、概念图、原理演示的动画 HTML 页面。用 CSS/SVG 绘制节点和连线，支持流动箭头、逐步出现、hover 高亮等动画效果。适合技术讲解、视频科普、PPT 配图场景。.
学霸笔记
生成手写笔记本风格的单文件 HTML 学习笔记。当用户需要将技术内容、漏洞分析、知识总结等转化为视觉精美的网页笔记时使用。.
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
consulting-analysis
Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This…
ppt-generation
Use this skill when the user requests to generate, create, or make presentations (PPT/PPTX). Creates visually rich slides by generating images for each slide and composing them into a PowerPoint file.
deerflow-maintainer-orchestrator
Use when a DeerFlow maintainer needs comment-only GitHub issue or PR handling: resolve issue/PR scopes with gh, analyze issues, post or draft issue comments, perform PR review comments, review PR or issue batches, compare competing PRs that target the same issue, give fix strategy, risk classification, and validation…