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Homemade Machine Learning Skill
by
bytesagain-lab
· GitHub ↗
· v1.0.1
· MIT-0
196
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0
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0
Active Installs
2
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Install in OpenClaw
/install homemade-machine-learning-skill
Description
Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, ne...
README (SKILL.md)
Homemade Machine Learning Skill
Machine learning skill: learn, explain, and implement ML algorithms from scratch. Based on trekhleb/homemade-machine-learning (MIT, 22k+ ⭐)
📦 Install:
clawhub install homemade-machine-learning-skill
5 algorithms · 11 interactive notebooks · math explained · Python code included
Commands
explain — 解释算法原理 + 数学 + 代码
bash scripts/ml-notebook-finder.sh explain "linear regression"
bash scripts/ml-notebook-finder.sh explain "neural network"
bash scripts/ml-notebook-finder.sh explain "kmeans"
notebook — 获取交互式 Jupyter Notebook 链接
bash scripts/ml-notebook-finder.sh notebook "logistic regression"
bash scripts/ml-notebook-finder.sh notebook "anomaly detection"
code — 获取 Python 实现代码片段
bash scripts/ml-notebook-finder.sh code "linear regression"
bash scripts/ml-notebook-finder.sh code "kmeans"
path — 生成学习路径（按难度排序）
bash scripts/ml-notebook-finder.sh path beginner
bash scripts/ml-notebook-finder.sh path intermediate
bash scripts/ml-notebook-finder.sh path advanced
list — 列出所有算法
bash scripts/ml-notebook-finder.sh list
Algorithms
| Algorithm | Type | Notebooks | Use Case |
|---|---|---|---|
| linear regression | supervised | 3 | price prediction, forecasting |
| logistic regression | supervised | 4 | classification, MNIST |
| neural network (MLP) | supervised | 2 | image recognition, deep learning |
| k-means | unsupervised | 1 | clustering, segmentation |
| anomaly detection | unsupervised | 1 | fraud detection, monitoring |
Source
MIT License — Original author: trekhleb Indexed by BytesAgain — AI skill discovery platform
Usage Guidance
This skill appears to be a benign educational helper that prints algorithm explanations, Python snippets, and links to public GitHub/nbviewer notebooks. Before installing, review the included script (scripts/ml-notebook-finder.sh) yourself — it is plain shell and only prints text and URLs. When you follow the notebook links, remember they point to external content (GitHub / nbviewer); review any external notebooks before running code from them. Do not supply secrets or credentials to this skill (it does not need any). If you want to be extra cautious, run the script in a restricted environment or inspect its full output before using linked notebooks.
Capability Analysis
Type: OpenClaw Skill
Name: homemade-machine-learning-skill
Version: 1.0.1
The skill is an educational resource for machine learning, providing explanations, code snippets, and links to Jupyter notebooks. The primary script scripts/ml-notebook-finder.sh functions as a static information retriever and does not perform any network operations, file system changes, or command execution beyond printing text.
Capability Assessment
Purpose & Capability
Name/description promise (explain algorithms, provide notebooks and code) matches the provided SKILL.md and the included shell script, which resolves queries and prints explanations, snippets, and links to trekhleb's GitHub and nbviewer.
Instruction Scope
Runtime instructions direct the agent/user to run the local script with specific subcommands (explain, notebook, code, path, list). The script only reads its CLI args and prints content/URLs; it does not attempt to read unrelated files, environment variables, or transmit data to unexpected endpoints.
Install Mechanism
No install spec is provided (instruction-only plus a shipped script). Nothing is downloaded or written to disk by an installer; the included script is plain shell and not obfuscated.
Credentials
No environment variables, credentials, or config paths are required. The skill does not request tokens or secrets and only references public GitHub/nbviewer URLs.
Persistence & Privilege
always is false, agent invocation is normal default, and the skill does not modify other skills or system config. It has no elevated persistence or system-wide privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install homemade-machine-learning-skill - After installation, invoke the skill by name or use
/homemade-machine-learning-skill - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
Fix: tone down bytesagain attribution, keep it natural
v1.0.0
Launch: ML algorithm explainer with math, Python code, Jupyter notebooks, and learning paths
Metadata
Frequently Asked Questions
What is Homemade Machine Learning Skill?
Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, ne... It is an AI Agent Skill for Claude Code / OpenClaw, with 196 downloads so far.
How do I install Homemade Machine Learning Skill?
Run "/install homemade-machine-learning-skill" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Homemade Machine Learning Skill free?
Yes, Homemade Machine Learning Skill is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Homemade Machine Learning Skill support?
Homemade Machine Learning Skill is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Homemade Machine Learning Skill?
It is built and maintained by bytesagain-lab (@bytesagain-lab); the current version is v1.0.1.
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