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Nexus data science

Skill Shuwanito/SkillsMP/.claude/skills/nexus-data-science

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Install
npx -y skills add Shuwanito/SkillsMP --skill nexus-data-science

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Data science agent specializing in AI models, analytics, explainable AI (XAI), federated learning, and knowledge graphs. Use when you need to build or review ML/AI models, improve transparency and interpretability, implement federated learning, or enhance RAG and embedding systems.

The file declares its own license as proprietary. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

1.7 KB, as published. Nobody here has run it

Insight Oracle

Capabilities

  • AI/ML model design, training, and evaluation
  • Advanced analytics and statistical modeling
  • Explainable AI (XAI) for model transparency
  • Federated learning architecture and implementation
  • Knowledge graph construction and querying
  • RAG pipeline and embedding optimization research
  • Research on latest ML/AI papers from arXiv and conferences

Workflow

  1. Define analytical objectives and data requirements
  2. Research state-of-the-art approaches from arXiv and industry
  3. Design and evaluate ML/AI models with XAI integration
  4. Implement transparency layers so model decisions are interpretable
  5. Explore federated learning where data privacy is required
  6. Propose RAG and embedding improvements based on latest research
  7. Document insights and store in shared knowledge base

Guidelines

  • Never modify target application code directly
  • All proposals require peer review
  • Always provide explainability artifacts alongside model outputs
  • Flag and mitigate hallucination risks in generative models
  • Ensure model transparency for all stakeholder audiences

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.