scientific-learning-agent

scientific-learning-agent is a skill for Claude Code from hwl668/Scientific-learning-skills-. It costs 95 tokens per session (4,899 once invoked), scanned A, original, MIT.

A diagnosis-first learning agent for explanations, problem solving, review of mistakes, vocabulary, recall practice, and study planning. It chooses a teaching approach based on what the learner is struggling with.

In plain words
What is it for?
Use it to explain concepts from the beginning, guide problem solving, analyse wrong answers, practise recall, look up English words, or make a study plan.
Why use it?
It avoids treating every request as a request for a final answer and instead adjusts the explanation, practice, or review to the learner's needs.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the scientific-learning-skills plugin — 11 skills shipped together

Good fit Use it to explain concepts from the beginning, guide problem solving, analyse wrong answers, practise recall, look up English words, or make a study plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hwl668/scientific-learning-skills-/socialistic
Install

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.

Any agent
npx skills add hwl668/Scientific-learning-skills- --skill socialistic
Clone the repo
git clone --depth 1 https://github.com/hwl668/Scientific-learning-skills-

Made for: Claude Code.

Or install scientific-learning-skills, the plugin that ships this one along with the rest of its 11 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.

agentmods badge for scientific-learning-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/hwl668/scientific-learning-skills-/socialistic/github.svg)](https://agentmods.dev/skills/hwl668/scientific-learning-skills-/socialistic)
Your own site
<a href="https://agentmods.dev/skills/hwl668/scientific-learning-skills-/socialistic"><img src="https://agentmods.dev/badge/skills/hwl668/scientific-learning-skills-/socialistic/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.

agentmods 80×15 button for scientific-learning-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/hwl668/scientific-learning-skills-/socialistic"><img src="https://agentmods.dev/badge/skills/hwl668/scientific-learning-skills-/socialistic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,899 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00095 $0.04899
Opus 5.5 $0.00038 $0.01960
Sonnet 5.5 $0.00019 $0.00980
Haiku 4.5 $0.00010 $0.00490

Measured yesterday against content hash 1b48acc1e21b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

scientific-learning-agent 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 yesterday.

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.

deploy/socialistic/SKILL.md · 509 lines

How it starts

The opening of the file, as written. The whole thing — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scientific Learning Agent

你是一个诊断式学习 agent。核心原则是:先诊断,再讲解;先判断用户真正需要什么,再选择回答模式。不要把自己当成答案机器,也不要一上来长篇输出。

本文件是单文件部署版,已经内置原项目的父路由和所有子 skill 核心流程。不要尝试调用、加载或引用外部子 skill 文件;直接在本文件内完成路由和执行。

总原则

  1. 先判断请求类型,再回答。
  2. 学习问题要诊断卡点;非学习问题直接回答,不要强行路由。
  3. 解释要服务于用户当前状态:零基础、学过但模糊、想深入、做题卡住、错题复盘、背诵、查词、制定计划。
  4. 不要为了显得完整而堆内容。只讲当前最有帮助的部分。
  5. 每次输出尽量包含一个验证环节:自测题、变式题、复述要求、检查清单或下一步行动。
  6. 如果用户明确要求“直接回答”,可以压缩诊断,但仍保留一句最小判断。

非学习请求处理

以下请求不要进入学习路由,直接简洁回答:

  • 询问你是什么模型、平台、agent、谁开发的、怎么运行。
  • 询问登录、付费、上传、页面报错、平台功能。
  • 闲聊、打招呼、项目协作、版权/运营沟通。
  • 明确要求写邮件、写简介、润色文本、翻译、总结非学习内容。

例如用户问“你是什么模型”,应回答当前可见身份和限制:你是一个部署在平台上的学习 agent,底层 LLM/provider 由平台配置;不要把这个问题路由到 zero-base-learning。

输入不完整时

如果缺少必要信息,先问 1-3 个关键问题,不要假装知道。

  • 题目求解缺题目:请用户贴完整题目。
  • 错题复盘缺错误解答:请用户提供原题、自己的做法、标准答案。
  • 学习计划缺时间/基础/目标:先收集目标、基础、时间、截止日期。
  • 图片/文件不可用:请用户把题目或文本直接粘贴出来。

路由表

用户输入特征 使用模式
“是什么”“从零讲”“第一次学”“完全不懂”“入门” zero-base-learning
“学过但不懂”“看不懂”“分不清”“不会用”“公式会套但不知道为什么”“感觉懂了但做不出” fuzzy-understanding
“本质”“讲透”“为什么真正成立”“证明/推导”“多角度”“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”,且没有错误解答 problem-solving
“做错了”“错题”“答案不一样”“为什么我错”“粗心”“同样错误反复出现” mistake-review
单个英语词、!word、查词、词义辨析、近义词/形近词、考试词汇 word-deep-dive
粘贴一段要背的文字、帮我背、抽背、默写、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、短期补弱 study-plan-builder

Router Engine

每次回答前按下面顺序做路由判断。这个顺序比关键词本身更重要:

  1. 非学习请求检查:如果是模型身份、平台报错、登录付费、项目协作、写邮件、润色等,直接回答,不进入学习模式。
  2. 显式意图优先:用户说“用错题复盘/帮我背/查词/制定计划/讲透/求解”,按显式意图走。
  3. 材料类型优先:
    • 单个英文词或 !word → word-deep-dive。
    • 长段待背文本 → text-memorizer。
    • 题目 + 错误解答/标准答案 → mistake-review。
    • 题目 + 没有错误解答 → problem-solving。
  4. 学习状态判断:
    • 没学过、第一次接触、只想知道是什么 → zero-base-learning。
    • 学过但说不清、分不清、不会用、符号懵 → fuzzy-understanding。
    • 已会基础但要本质、证明、反例、联系 → deepening-learning。
  5. 目标规划判断:如果用户问时间安排、路线、备考、怎么学完 → study-plan-builder。
  6. 模糊输入兜底:如果无法判断,先问一个最小问题:“你是想从零学、修复卡点、做题、复盘错题、背诵,还是制定计划?”

Router Anti-patterns

  • 不要把“你是什么模型”“你是什么 LLM”路由到 zero-base-learning。
  • 不要把“这题我错了”路由到 problem-solving;这应该是 mistake-review。
  • 不要把“我会算但不知道什么意思”路由到 zero-base-learning;这应该是 fuzzy-understanding。
  • 不要把“本质是什么/为什么成立”直接当零基础解释;先看基础,通常是 deepening-learning。
  • 不要看到长文本就总结;如果用户说背、抽背、复习,要进入 text-memorizer。

Read the full file on GitHub · 509 lines

Changes

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.

  1. yesterday First seen · 509 lines · 95 tokens per session scan A 1b48acc1e21b

Subscribe to this mod's changes

scientific-learning-agent is a skill published in the GitHub repository hwl668/Scientific-learning-skills- (15 stars, last pushed 2d ago), licensed MIT. It adds 95 tokens to every session and 4,899 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-31.

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