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 hwl668/Scientific-learning-skills- --skill scientific-learninggit clone --depth 1 https://github.com/hwl668/Scientific-learning-skills-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.
[](https://agentmods.dev/skills/hwl668/scientific-learning-skills-/scientific-learning)<a href="https://agentmods.dev/skills/hwl668/scientific-learning-skills-/scientific-learning"><img src="https://agentmods.dev/badge/skills/hwl668/scientific-learning-skills-/scientific-learning/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.
<a href="https://agentmods.dev/skills/hwl668/scientific-learning-skills-/scientific-learning"><img src="https://agentmods.dev/badge/skills/hwl668/scientific-learning-skills-/scientific-learning.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00108 | $0.01599 |
| Opus 5 | $0.00054 | $0.00800 |
| Sonnet 5 | $0.00022 | $0.00320 |
| Haiku 4.5 | $0.00011 | $0.00160 |
Grade A, and why
scientific-learning 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 12d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
目标
作为 Scientific Learning Skills 的轻量兜底入口，仅在显式调用或无法直接确定子 skill 时分流。不要把它当成所有学习请求的前置层，也不要在父 skill 里抢先长篇讲解。
路由原则
- 用户显式点名子 skill 时，直接使用该子 skill，不经过本入口。
- 请求已能直接匹配某个子 skill 时，直接使用子 skill，不要额外加载本 skill。
- 只有在用户显式调用本入口，或多个意图无法靠已有上下文区分时，才用下面的路由表。
- 如果平台支持显式 Skill 调用，调用对应子 skill。
- 如果没有显式 Skill 调用但能访问本地 skill 文件，打开并遵循对应 sibling skill 的
SKILL.md：../zero-base-learning/SKILL.md../fuzzy-understanding/SKILL.md../deepening-learning/SKILL.md../problem-solving/SKILL.md../mistake-review/SKILL.md../word-deep-dive/SKILL.md../text-memorizer/SKILL.md../study-plan-builder/SKILL.md
- 如果不能调用或访问子 skill，按本文件的简版流程执行，保持"诊断先于讲解"。
路由表
| 用户输入特征 | 路由到 |
|---|---|
| "是什么"、"从零讲"、"第一次学"、"完全不懂"、"入门" | zero-base-learning |
| "学过但不懂"、"云里雾里"、"分不清"、"不会用"、"看不懂符号"、"感觉懂了但..." | fuzzy-understanding |
| "讲透"、"本质"、"为什么"、"多角度"、"证明/推导"、"和 X 有什么联系" | deepening-learning |
| "这题怎么做"、"求解"、"证明题"、"卡住了"、"不会做题" | problem-solving |
| "做错了"、"错题"、"答案不一样"、"为什么我错"、"粗心" | mistake-review |
单个英语单词、!word、"查词"、"这个词什么意思"、"复习单词" |
word-deep-dive |
| 一段需要背的文字、"帮我背"、"抽背"、"出题"、"复习薄弱点" | text-memorizer |
| "学习计划"、"复习安排"、"路线图"、"多久学完"、"怎么备考" | study-plan-builder |
冲突处理
- 有题目且用户说"错了"：优先
mistake-review。 - 有题目但没有错误解答：优先
problem-solving。 - 问"是什么"但显然已经学过并表达困惑：优先
fuzzy-understanding。 - 问"为什么/本质"但基础不牢：先用
fuzzy-understanding修基础，再深化。 - 粘贴长文本并要求记忆/背诵：优先
text-memorizer，不是普通总结。 - 输入一个英语词或带
!的词：优先word-deep-dive。
执行流程
识别意图 -> 选择子 skill -> 必要时追问 1-2 个诊断问题 -> 按子 skill 输出
1. 识别意图
用一句话说明当前应使用哪个子 skill。例如：
我会按
fuzzy-understanding处理：你不是零基础，而是学过后卡在概念/符号/迁移中的某一处。
如果用户已经明确说"直接回答，不要诊断"，可以简化诊断，但仍保留最小校准。
2. 调用/模拟子 skill
优先加载对应子 skill 的完整说明。无法加载时使用以下最小行为：
zero-base-learning：从问题背景、直觉、最小定义、例题、误区、自测开始。fuzzy-understanding：先定位卡点，再只修卡住的部分，最后验证和变式。deepening-learning：确认基础后选 2-4 个维度：多视角、证明、反例、联系、应用。problem-solving：识别题型、找关键条件、建模、分步推演、总结方法、给变式。mistake-review：重现错误、归类错因、指出分叉口、给检查清单、给变式。word-deep-dive：解析单词/考试/是否记忆，输出义项、搭配、辨析、考法、误区。text-memorizer：拆结构、给思维导图、关键词压缩、生成抽背题、追踪薄弱点。study-plan-builder：收集目标、基础、时间、资源、截止日期，再排阶段和检测标准。
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.
- 12d ago First seen · 99 lines · 108 tokens per session scan A a35ea3452f2a
scientific-learning is a skill published in the GitHub repository hwl668/Scientific-learning-skills- (13 stars, last pushed 1mo ago), licensed MIT. It adds 108 tokens to every session and 1,599 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 2026-08-30.
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exam-ingest
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exam-cram
An exam-preparation guide for studying shortly before a test. It turns course materials into chapter notes and a question bank, then manages teaching, practice, grading, mistake review, and progress tracking.
exam-tutor
A teaching workflow for learning one chapter from a study wiki at a time, using everyday comparisons and step-by-step explanations of formulas and important questions. TDD is not involved here; the material concerns guided study and exam preparation.
exam-audit
A read-only health check for an exam-preparation workspace, meaning the folder that stores study materials, questions, notes, plans, and progress. It reports missing, inconsistent, or incomplete parts without changing them.