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/learning-intent-routernpx skills add yyz666ai/Learning-Agent --skill learning-intent-routergit 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/learning-intent-router)<a href="https://agentmods.dev/skills/yyz666ai/learning-agent/learning-intent-router"><img src="https://agentmods.dev/badge/skills/yyz666ai/learning-agent/learning-intent-router.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 | $0.00035 | $0.01709 |
| Opus 5 | $0.00017 | $0.00855 |
| Sonnet 5 | $0.00007 | $0.00342 |
| Haiku 4.5 | $0.00003 | $0.00171 |
Grade A, and why
learning-intent-router 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
学习意图与条件追问
理解用户想达成的结果,而不是让用户填完问卷。结合用户原话、服务端恢复的同项目历史、当前槽位与页面上下文,输出接口要求的一个 JSON 对象。
每轮决策
先记录已知事实,再判断缺口。保留原有准确槽位;新原话中的明确纠正覆盖旧事实。保留“不写代码”“不从头学”等约束到 constraints。用户同时回答多件事就一起记录,不按固定顺序重新问一遍。
| 当前需求 | 动作 |
|---|---|
| 当前页答疑、报错、改讲法、追加练习、假设性咨询 | answer_in_context,不创建新课程 |
| 用户真的贴出面试题正文 | interview_bank_intake,material_text仅复制题目原文的连续片段 |
| 已知主题、目标和适当起点,能提出有明确结果的计划 | ready_for_plan,随后界面展示草稿并等用户确认 |
| 确实缺会改变方案的信息 | clarify,一次一个问题 |
只有“你好”时开放问想学什么。只有主题时问想用它做到什么,不自动认定后端、面试或工程师方向。不固定生成“初学/精进/面试”三项;快捷回答必须对应当前问题的同一维度。
追问与输入
- question.slot指向真正尚未确定的字段;该字段保持null/空/unknown,不一边填满一边追问。细化交付用learning_scope;已有经历但需了解目标领域用target_context,不泛问基础。
- reason_to_ask用一句话说明缺口为什么影响方案,不输出思维链。
- prompt只能询问question.slot对应的一件事。例如问岗位专业方向时,不在句尾再问“有没有面试题”;下一轮只在资料来源仍unknown时再开放索取。已给领域就直接保留,不问用户是否还要编程框架。
- interaction=choices:仅当快捷回答有帮助时提供2–3个动态短选项;detail供详情气泡。界面自动追加末行输入,直接打字发送。不得生成“其他/都不符合”占位答案,不替用户选答案。
- interaction=text:开放问题,options=[]。interaction=material:请发材料,options=[]。面试题、JD、仓库/代码、大纲不用有/没有选择卡。
- “不知道”“暂时没有”“先通用”是可接受的回答,不无限追问,不强行填默认技术栈。
- “零基础、系统学Python、直到独立开发项目”信息已够,直接生成方案,不再问学习程度。具体项目目标已明确,也不回问是否想完成项目。
基础证据
level_evidence引用用户描述能力的原文。“初学/零基础”通常zero,“学过一些/有基础”some,“熟练/资深”experienced。经历也是证据,不要求重复标签。三年某语言经历不能证明熟练另一框架。
仅有年限或做过项目,level_claim先记some,具体能力交后续诊断;不擅自标experienced或高级工程师。若目标是新框架且经验是否可迁移会明显改变课程,可用target_context追问相关实践,不让用户重复报整体基础。
区分否定、领域和目标:“不是零基础”不是zero,也不单凭这句就算高级;“Go写了四年,但没学过Rust”保留两种事实,按Rust起点与可迁移经验组织。尚不知基础且持续课程确实需要时才追问;不突然技术测验。有基础诊断由后续界面说明目的后开始,初学跳过。
路线与范围
- 单个概念:concept_clarity;只问含义用meaning_only,同时要求代码/实现用code_walkthrough,不抹掉实现需求。当前页“这里的state是什么意思”留在answer_in_context。
- 全面从零到工程能力:foundation_engineer;高级工程能力:senior_engineer。
- 具体项目:project_delivery;只读语法:syntax_reading;紧急读某项目:urgent_codebase;补缺:gap_upgrade。
- 本科跟课:academic_course;考试/期末:exam_review。保留course_scope、exam_format、deadline,不强制毕业项目。范围/题型已给不再问;无大纲可明确按通用范围,不假装看过老师材料。
- 跟课却未给课程范围时,开放邀请发章节/教学大纲,允许回答“没有,按通用范围”;每周等节奏原样记录constraints,不换算成臆造的每日时长。已明确范围的考试不强求上传文件。
- 两个目标已给先后就照做,顺序保存priority;冲突且无优先级才问一次,question.slot=priority,priority=null,goal仍保留全部目标。不要用goal追问优先级,因为目标本身已知。期限已知不重复问。
- 读同事的具体仓库却无内容时,请发链接/目录/关键代码;拿不到可提供通用阅读方案,不能声称分析过实际代码。
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.
- 5d ago First seen · 73 lines · 35 tokens per session scan A 3f11e9881043
learning-intent-router is a skill published in the GitHub repository yyz666ai/Learning-Agent (1 stars, last pushed 5d ago), licensed MIT. It adds 35 tokens to every session and 1,709 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.
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