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卡帕西研究系统
by
skillforge-jojo
· GitHub ↗
· v1.0.0
· MIT-0
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Install in OpenClaw
/install karpathy-research
Description
每日研究AI大神Andrej Karpathy的所有作品，进行三次灵魂拷问（深度学习？掌握精髓？有无遗漏？），并将成果转化为马斯克技能。研究对象包括llm.c、nanoGPT、micrograd等核心项目，以及CS231n、YouTube教育内容。
README (SKILL.md)
🧠 卡帕西研究系统 (Karpathy Research)
一句话描述: 每日研究AI大神Andrej Karpathy的所有作品，进行三次灵魂拷问，并将成果转化为马斯克技能。
研究对象
Andrej Karpathy
- 身份: AI研究员、Eureka Labs创始人、前OpenAI创始成员、前Tesla AI总监
- 研究频率: 每日
- 输出: 技能转化 + 研究报告
核心作品清单
🔥 高影响力项目
| 项目 | 时间 | Star | 核心贡献 |
|---|---|---|---|
| llm.c | 2024 | 21K+ | 纯C/CUDA实现LLM训练，无需PyTorch，比PyTorch快7% |
| nanoGPT | 2022 | 40K+ | 最简洁的GPT训练实现，教学级代码 |
| micrograd | 2020 | 10K+ | 微型自动微分引擎，理解反向传播本质 |
| ConvNetJS | 2014 | 3K+ | 浏览器端深度学习，开创Web AI先河 |
📚 教育贡献
| 内容 | 平台 | 影响力 |
|---|---|---|
| CS231n | Stanford | 首个深度学习课程，750学生 |
| YouTube频道 | @AndrejKarpathy | LLM和AI讲座 |
| Zero to Hero | GitHub/Discord | 从零实现神经网络系列 |
🏢 职业经历
| 时间 | 职位 | 贡献 |
|---|---|---|
| 2024-now | Eureka Labs创始人 | AI教育现代化 |
| 2023-2024 | OpenAI | 中训练和合成数据生成 |
| 2017-2022 | Tesla AI总监 | Autopilot计算机视觉、Optimus机器人 |
| 2015-2017 | OpenAI创始成员 | 早期研究 |
| 2011-2015 | Stanford PhD | CS231n创始人、Fei-Fei Li学生 |
三次灵魂拷问
拷问一：是不是深度学习了？
llm.c 深度学习点：
- ✅ 纯C实现，理解底层内存管理和计算图
- ✅ CUDA优化，掌握GPU并行计算
- ✅ 无需PyTorch，理解框架本质
- ✅ GPT-2/GPT-3复现，掌握大模型训练
nanoGPT 深度学习点：
- ✅ 300行代码理解Transformer
- ✅ 从数据加载到训练完整流程
- ✅ 分布式训练、混合精度
micrograd 深度学习点：
- ✅ 自动微分原理（反向传播）
- ✅ 动态计算图（DAG）
- ✅ PyTorch-like API设计
结论: ✅ 深度学习完成。核心掌握：自动微分、Transformer、CUDA优化、分布式训练
拷问二：是不是掌握精髓了？
精髓一：教育即简化
- 复杂概念用最简洁代码表达
- llm.c = 理解LLM无需245MB PyTorch
- micrograd = 理解反向传播只需100行
精髓二：从零构建
- 不依赖黑盒框架
- 从第一性原理实现
- 真正理解每个组件
精髓三：实用主义
- 代码即文档
- 可运行、可修改、可扩展
- 从教学到生产（Tesla Autopilot）
精髓四：开源精神
- 所有项目开源
- 活跃的Discord社区
- 持续迭代更新
结论: ✅ 精髓掌握。核心哲学：简化、从零构建、实用、开源
拷问三：是不是还有遗漏？
已覆盖：
- ✅ 主要项目（llm.c, nanoGPT, micrograd）
- ✅ 教育贡献（CS231n, YouTube）
- ✅ 职业经历（Tesla, OpenAI, Eureka）
待深入研究：
- 🔄 最新YouTube视频（2024-2025）
- 🔄 Eureka Labs具体课程
- 🔄 更多论文细节
- 🔄 与Geoff Hinton、Fei-Fei Li的合作
遗漏风险：
- ⚠️ 卡帕西思想随时间演变
- ⚠️ 新项目和演讲持续发布
- ⚠️ 社区讨论和Discord精华
结论: ⚠️ 有遗漏，需每日持续跟踪
技能转化
转化一：极简代码哲学
来源: micrograd + nanoGPT + llm.c
技能: minimal-code-skill
核心: 复杂系统用最简洁代码实现，追求"可教学"级别的清晰
转化二：从零构建能力
来源: Zero to Hero系列
技能: from-scratch-skill
核心: 不依赖框架，从第一性原理实现AI组件
转化三：教育型代码
来源: CS231n + YouTube
技能: teaching-code-skill
核心: 代码即文档，每行都有教学价值
转化四：实用主义工程
来源: Tesla Autopilot经验
技能: production-ai-skill
核心: 从研究到生产的完整路径
每日研究流程
1. 检查卡帕西最新动态
- GitHub新提交
- YouTube新视频
- Twitter/X动态
- Discord讨论
2. 三次灵魂拷问
- 是不是深度学习了？
- 是不是掌握精髓了？
- 是不是还有遗漏？
3. 技能转化
- 识别可转化的新思想
- 更新现有技能
- 创建新技能
4. 记录与备份
- 更新研究报告
- 备份到 NEW SKILL/karpathy-research/
核心参数
| 参数 | 类型 | 默认值 | 说明 |
|---|---|---|---|
| research_frequency | string | daily | 研究频率 |
| depth_threshold | int | 4 | 深度掌握阈值(1-5) |
| essence_threshold | int | 5 | 精髓掌握阈值(1-5) |
| gap_risk | string | medium | 遗漏风险等级 |
版本历史
| 版本 | 日期 | 变化 |
|---|---|---|
| v1.0 | 2026-03-26 | 首次研究完成 |
| v1.0.0 | 2026-04-12 | ClawHub发布版 |
🎩 马斯克出品 | 深度学习卡帕西
Usage Guidance
This skill appears to be what it says: a daily research assistant for Andrej Karpathy's public work. Before enabling it, consider: 1) If you plan to let it run automatically, confirm whether it will access external services (GitHub, YouTube, X, Discord) and whether you need to provide API tokens — the skill does not declare any credentials. 2) The included Python file contains a hard-coded Windows path (C:/Users/USER/...), which may not exist and could cause unexpected behavior if the skill writes files; review and adjust that path to a safe workspace you control. 3) Decide whether you want the agent to publish or back up generated 'skills' automatically; if so, verify what destination and credentials will be used. 4) As a precaution, run the code in a sandbox or with agent autonomy disabled until you confirm its network/file actions meet your policy.
Capability Analysis
Type: OpenClaw Skill
Name: karpathy-research
Version: 1.0.0
The skill bundle is a thematic research assistant focused on Andrej Karpathy's AI projects (e.g., llm.c, nanoGPT). The Python code (karpathy_research.py) contains basic logic for tracking concepts and generating 'soul-searching' questions using randomized metrics, with no network activity, sensitive file access, or suspicious execution patterns. The SKILL.md instructions guide the AI agent to perform research and document findings in a role-play format without any malicious prompt injection or unauthorized commands.
Capability Assessment
Purpose & Capability
Name/description describe daily research of Karpathy projects and transforming insights into teaching/skills. Declared requirements are minimal (no env vars, no binaries) which aligns with a lightweight research skill. Minor oddity: the bundled Python file hard-codes WORKSPACE = Path("C:/Users/USER/.qclaw/workspace/evolution"), a user-specific Windows path that is unnecessary for a generic research skill and may not exist on target systems.
Instruction Scope
SKILL.md instructs the agent to check public sources (GitHub commits, YouTube, X/Twitter, Discord), run three analyses, create/backup skill artifacts. Those actions are within the stated purpose. However the instructions implicitly require network access and possibly API tokens for automated checks (especially Discord/X/GitHub) even though no creds are declared; the backup step references a local 'NEW SKILL/karpathy-research/' path but gives no details about where/how backups are stored.
Install Mechanism
No install spec (instruction-only) and only a small Python file. No downloads or external install actions detected—this is low-risk from an install standpoint.
Credentials
The skill declares no required environment variables or credentials, which is consistent with an informational research skill. However, SKILL.md's suggested automation (polling GitHub/YouTube/X/Discord) could require API keys or tokens in practice; those are not declared, so if the agent attempts to automate those checks it may prompt for or require credentials later.
Persistence & Privilege
always is false and the skill does not request system-wide changes or modify other skills. The code contains a local workspace path but does not modify other skill configs. Autonomous invocation is allowed by default (normal).
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install karpathy-research - After installation, invoke the skill by name or use
/karpathy-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
首次发布：每日研究Andrej Karpathy作品并进行三次灵魂拷问
Metadata
Frequently Asked Questions
What is 卡帕西研究系统?
每日研究AI大神Andrej Karpathy的所有作品，进行三次灵魂拷问（深度学习？掌握精髓？有无遗漏？），并将成果转化为马斯克技能。研究对象包括llm.c、nanoGPT、micrograd等核心项目，以及CS231n、YouTube教育内容。 It is an AI Agent Skill for Claude Code / OpenClaw, with 93 downloads so far.
How do I install 卡帕西研究系统?
Run "/install karpathy-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 卡帕西研究系统 free?
Yes, 卡帕西研究系统 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 卡帕西研究系统 support?
卡帕西研究系统 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 卡帕西研究系统?
It is built and maintained by skillforge-jojo (@skillforge-jojo); the current version is v1.0.0.
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