Loading...
Loading...
Found 1,140 Skills
Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python
Intelligent recommendation system analysis tool that provides implementations of multiple recommendation algorithms, evaluation frameworks, and visual analysis. It requires user behavior data, product information, or rating data for use, supports recommendation algorithms such as collaborative filtering and matrix factorization, and generates personalized recommendation results and evaluation reports.
Compare leading tech stocks to distinguish hype-driven overvaluation from fundamentally justified pricing, and identify undervalued tech names the market is overlooking. Use when the user asks to evaluate tech stock valuations, find overvalued or undervalued tech companies, assess whether a tech stock's growth justifies its multiple, compare tech company fundamentals, analyze revenue growth vs. valuation, or identify mispriced technology stocks.
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Triage GitHub bug reports for actionability. Use when evaluating whether a bug issue has sufficient detail and identifying missing information from the reporter.
Conducts comprehensive frontend design reviews covering UI/UX design quality, design system validation, accessibility compliance, responsive design patterns, component library architecture, and visual design consistency. Evaluates design specifications, Figma/Sketch files, design tokens, interaction patterns, and user experience flows. Identifies usability issues, accessibility violations, design system deviations, and provides actionable recommendations for improvement. Produces detailed design review reports with severity-rated findings, visual examples, and implementation guidelines. Use when reviewing frontend designs, validating design systems, ensuring accessibility compliance, evaluating component libraries, assessing responsive designs, or when users mention design review, UI/UX review, Figma review, design system validation, accessibility audit, or frontend design quality.
Evaluate solutions through multi-round debate between independent judges until consensus
Evaluate text completeness based on criteria.
Use this skill to work with Microsoft Foundry (Azure AI Foundry): deploy AI models from catalog, build RAG applications with knowledge indexes, create and evaluate AI agents. USE FOR: Microsoft Foundry, AI Foundry, deploy model, model catalog, RAG, knowledge index, create agent, evaluate agent, agent monitoring. DO NOT USE FOR: Azure Functions (use azure-functions), App Service (use azure-create-app).
Evaluate designs for usability, visual hierarchy, consistency, and adherence to design principles. Trigger with "what do you think of this design", "give me feedback on", "critique this", "review this mockup", or when the user shares a design and asks for opinions.
Evaluate output, identify lessons, decide accept/rework. Use after implementation.
Production-ready financial analyst skill with ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. 4 Python tools (all stdlib-only). Works with Claude Code, Codex CLI, and OpenClaw.