agentCLAWHUBUnverified
Homemade Machine Learning Skill
Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, ne... Skill: Homemade Machine Learning Skill Owner: bytesagain-lab Summary: Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, ne... Tags: latest:1.0.1 Version history: v1.0.1 | 2026-04-20T14:19:37.696Z | user Fix: tone down bytesagain attribution, keep it natural v1.0.0 | 2026-04-20T14:15:36.388Z | user Launch: ML algor
OpenClaw
Rank
62
Safety
84
Downloads
1.2k
Updated
Oct 11, 2026
Version
1.0.1
Source
CLAWHUB
About
What it does, and when to use it.
Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.
Avoid when
- Contract metadata is missing or unavailable for deterministic execution.
Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing
Public facts
Every fact links back to the source it came from.
- Vendor
- Clawhubvendor · observed Oct 11, 2026
- Protocol compatibility
- OpenClawcompatibility · observed Oct 11, 2026
- Adoption signal
- 1.2K downloadsadoption · observed Oct 11, 2026
- Latest release
- 1.0.1release · observed Apr 20, 2026
- Handshake status
- UNKNOWNsecurity
Install and run
Setup complexity: low.
clawhub skill install s174pq5f3gzez3838vt2642fmx848f1d:homemade-machine-learning-skill- Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
- Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Contract: missing
curl -s "https://www.xpersona.co/api/v1/agents/clawhub-bytesagain-lab-homemade-machine-learning-skill/snapshot"
Documentation
CLAWHUB
8,603 characters of source documentation, loaded on request.
Extracted files
3 files captured from the source.
SKILL.md
--- name: homemade-machine-learning-skill description: "Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection — with math, Python code, and skill demos." version: "1.0.0" author: "BytesAgain" homepage: "https://bytesagain.com/skill/homemade-machine-learning-skill" tags: ["machine learning", "jupyter notebook", "python", "data science", "education", "linear regression", "neural network", "skill"] --- # Homemade Machine Learning Skill Machine learning skill: learn, explain, and implement ML algorithms from scratch. Based on [trekhleb/homemade-machine-learning](https://github.com/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 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 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 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 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 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](https://github.com/trekhleb/homemade-machine-learning) Indexed by [BytesAgain](https://bytesagain.com) — AI skill discovery platform
_meta.json
{
"ownerId": "kn76vqrf94wk924mddj8fp4p5x8497tb",
"slug": "homemade-machine-learning-skill",
"version": "1.0.1",
"publishedAt": 1776694777696
}skill-card.md
## Description: Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection, with math, Python code, and skill demos. This skill is ready for commercial/non-commercial use. ## Publisher: [bytesagain-lab](https://clawhub.ai/user/bytesagain-lab) ### License/Terms of Use: MIT-0 ## Use Case: Developers, data science learners, and external users use this skill to get concise explanations, learning paths, notebook links, and Python examples for foundational machine learning algorithms. ### Deployment Geography for Use: Global ## Known Risks and Mitigations: Risk: Users may open third-party hosted notebook or source links from the helper output. Mitigation: Review external notebook and source links before opening them, especially in environments with restrictions on third-party hosted content. Risk: Educational explanations and snippets may be unsuitable as production machine learning implementations without validation. Mitigation: Treat the generated examples as learning material and validate any adapted code against the intended dataset, model requirements, and review process. ## Reference(s): - [ClawHub skill page](https://clawhub.ai/bytesagain-lab/skills/homemade-machine-learning-skill) - [Publisher profile](https://clawhub.ai/user/bytesagain-lab) - [BytesAgain skill homepage](https://bytesagain.com/skill/homemade-machine-learning-skill) - [homemade-machine-learning source project](https://github.com/trekhleb/homemade-machine-learning) - [homemade-machine-learning notebooks](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks) ## Skill Output: **Output Type(s):** [text, markdown, code, shell commands, guidance] **Output Format:** [Markdown-style terminal text with inline shell commands, Python code snippets, and external notebook links] **Output Parameters:** [1D] **Other Properties Related to Output:** [The packaged shell helper prints static educational content and links; it does not fetch or execute remote code.] ## Skill Version(s): 1.0.1 (source: ClawHub release evidence) ## Ethical Considerations: Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
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Machine-readable data
The same record, as JSON, for agents and crawlers.
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