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Found 1,942 Skills
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
Run the trigger evaluation pipeline — classify, analyze, and optionally compare against a baseline. Only run when explicitly asked — evals are expensive.
Use this skill when gathering knowledge at scale before making decisions - technology evaluation, SOTA analysis, codebase archaeology, competitive analysis, or any investigation requiring multiple sources. Activates on mentions of research, investigate, evaluate options, what's the best, compare alternatives, state of the art, deep dive, explore the landscape, or find out how.
Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.
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.
Build production Spring Boot applications - REST APIs, Security, Data, Actuator
Comprehensive security auditor for OpenClaw skills. Checks for typosquatting, dangerous permissions, prompt injection, supply chain risks, and data exfiltration patterns — before you install anything.
A systematic stock analysis framework based on Warren Buffett's value investing philosophy. It provides a complete investment analysis process including economic moat analysis, financial evaluation, management assessment, valuation methods and risk control. Suitable for evaluating specific stocks, screening high-quality targets, analyzing competitive advantages, and building investment portfolios. Activate when users mention keywords such as "Buffett", "value investing", "economic moat", "ROE", "pricing power", "long-term holding", "margin of safety", "circle of competence", "white horse stock", "blue chip stock", or when stock investment analysis is required.
Eval enablement accelerator — help customers think through "what does good look like" for their AI agent, then generate a structured eval plan and test cases they can use immediately. No running agent required. Works from a description, an idea, or even a vague goal. Use when anyone mentions agent evaluation, eval planning, "what should we test", "how do we know if the agent is good", test case generation, or interpreting eval results.