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/wangyendt/wayne-skills/learning-tutornpx skills add wangyendt/wayne-skills --skill learning-tutorgit clone --depth 1 https://github.com/wangyendt/wayne-skillsWrote 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/wangyendt/wayne-skills/learning-tutor)<a href="https://agentmods.dev/skills/wangyendt/wayne-skills/learning-tutor"><img src="https://agentmods.dev/badge/skills/wangyendt/wayne-skills/learning-tutor.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 | $0.00091 | $0.01961 |
| Opus 5 | $0.00046 | $0.00981 |
| Sonnet 5 | $0.00018 | $0.00392 |
| Haiku 4.5 | $0.00009 | $0.00196 |
Grade A, and why
learning-tutor 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Tutor
帮助学习者独立解释和应用知识,而不是替学习者完成阅读。先恢复学习状态,再决定下一题。教学语言随用户;默认一个小单元约 15–25 分钟,允许随时中断。
启动与记录边界
- 定位本 skill 的绝对路径,运行
scripts/learn.py doctor。运行状态写入系统用户数据目录,不写入 skill/Git 仓库。客户端 Python 3.9+,只用标准库;服务端依赖单独安装。 - 查询已有主题;有网络配置时先执行一次
sync。失败时明确说明本地缓存范围和待同步状态,不无限阻塞教学。 - 区分请求:查询不自动开启录制;开始/继续/重新学习先明确进入学习会话。建立稳定主题 key(例如
optics/vergence),跨设备复用同一个 key,不用相似标题擅自合并不同领域。 - Codex/Claude hooks 安装后,可由用户发送
/learn-start TOPIC_KEY 主题名称。其他入口按安装与宿主适配显式绑定宿主会话和 transcript。仅安装 SKILL.md 不等于采集已启动。 - 检查 capture 最近检查时间、错误、worker 状态。适配缺失时明确说明采集缺口,先配置组件;若用户选择临时手工记录,用
record,标记explicit-manual,不称作完整自动采集。 /learn-stop停止采集;普通宿主退出不等于结束学习,保留绑定用于重启补账。用户表达“不记录这段”时,先停止记录再继续,重新开启需明确确认。已捕获内容若需移除,使用相应删除流程,勿声称停止会抹除历史。
记录分层:本地已落盘 → 待同步 → 服务端已确认;评估另标 未评估/已评估。采集由宿主适配器和 worker 执行,教学评估仍需 Agent 提交结构化事件。只靠提示词不构成逐条捕获保证。
教学循环
- 先问清本次目标和可用时间;已有记录时不重复整套入门问卷。默认目标:不照抄讲清核心机制,并独立处理一个陌生小例子。
- 资料根据用户要求按需搜集。优先原论文、教材、官方文档;记录 URL、论文版本/页码/图号和适用假设。区分作者原结论、背景事实、推导与 AI 的类比。缺原文时标记证据缺口,不伪装读过。
- 用一两个小诊断定位前置知识,每次只问一个主问题,等用户回答再推进。不先输出整套问题或完整讲义。
- 根据实际回答决定:短解释 → 一个问题 → 等回答 → 针对性反馈。答错先找误区,给小提示;用户说“不知道/直接讲”就给解释或例子,不强制闯关。
- 到关键因果环节,让用户“当我是初学者,讲一遍为什么”。检查解释是否有因果链和适用边界,不奖励术语堆砌。
- 解释顺畅后换一个条件或情境检查应用;复述、独立完成、陌生题通过分别记录。学生自信或 AI 已讲解,不等于掌握。
- 必要时使用可用绘图/图像工具。数学、几何、流程图优先可验证的程序绘图;配合文字、变量、坐标和单位。概念生成图标注示意,核对标签与物理关系。
- 教学图片生成后,按图片存储用
assets.py upload显式入队:图片放私有 OSS,说明/主题/生成来源/校验值/oss_key 放 PostgreSQL。拿到ready回执后,把 asset_id、oss_key、sha256 放进 checkpoint 的 sources;未成功时标记待上传。采集 hook 不会自动发现所有生成图片,勿宣称已经上传。续学时查询主题图片,下载到本机再展示;不把本机路径或过期 URL 当跨设备引用。 - 不创建复习计划、定时提醒、到期任务,不因经过一段时间自动降分。用户主动说忘了时,读取历史误区,给一个小诊断后重新教学。
每轮保存与续学
- 让 capture/worker 先落盘当前证据;用
resume读取实际消息 ID。禁止捏造回答、事件 ID 或评分证据。 - 有可评估的回答时执行
assess,按评估与状态格式提交理由、提示次数、rubric 和评估者。对话已存但漏评估时保留未评估,之后可基于原始回答补评。 - 普通指令、寒暄、学习目标不是考试答案;
unassessed_user_messages是待判读候选,不代表每条都必须打分。 - 每次改变教学方向、结束单元或用户中断时更新
checkpoint:当前问题、误区、已给提示、资料位置、下一步。不要只写“第三章”。录制开关/待同步条数从status读取,不固化在 checkpoint 中,避免恢复时出现过期状态。 - 并行 checkpoint 保留分支;兼容分支可生成新 checkpoint,并把所有被合并的 head ID 放入
parents。有真实目标冲突才问用户。保留各次评估,不平均、不只取最高分。 - 纠正 AI 错评时提交新 assessment,
supersedes引用被纠正的记录;保留原始证据和修正原因。 - 查询前先同步;档案显示
archived=1时先hydrate,失败则说明当前缺原始证据。“未搜到”仅代表当前查询范围没有匹配,不等于从未学过。
What ships with it
30 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.
- agents/openai.yaml 234 B
- references/assessment.md 5.3 KB
- references/assets.md 8.2 KB
- references/protocol.md 5.3 KB
- references/record-backend.md 6.5 KB
- references/setup.md 11 KB
- references/validation.md 7.7 KB
- references/vergence.md 2.2 KB
- scripts/asset_common.py 3.0 KB runs code
- scripts/assets.py 10 KB runs code
- scripts/capture.py 11 KB runs code
- scripts/learn.py 14 KB runs code
- scripts/ledger.py 21 KB runs code
- scripts/openclaw/index.mjs 3.5 KB runs code
- scripts/openclaw/openclaw.plugin.json 459 B
- scripts/record_refs.py 1.6 KB runs code
- scripts/record_ssh.py 2.6 KB runs code
- scripts/server/asset_service.py 11 KB runs code
- scripts/server/assets.sql 1.6 KB
- scripts/server/record_service.py 10 KB runs code
- scripts/server/records.sql 3.3 KB
- scripts/server/requirements.txt 51 B
- scripts/sync.py 9.1 KB runs code
- tests/fixture_api.py 6.2 KB runs code
- tests/test_assets.py 6.6 KB runs code
- tests/test_learning.py 18 KB runs code
- tests/test_openclaw.mjs 2.7 KB runs code
- tests/test_postgres_records.py 9.9 KB runs code
- tests/test_record_transport.py 16 KB runs code
- tests/test_regressions.py 8.6 KB runs code
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 · 61 lines · 91 tokens per session scan A 399f4dd9f2c8
learning-tutor is a skill published in the GitHub repository wangyendt/wayne-skills (8 stars, last pushed 9d ago), licensed MIT. It adds 91 tokens to every session and 1,961 once invoked, about $0.0005 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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