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Found 199 Skills
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.
Comprehensive skill for Microsoft GraphRAG - modular graph-based RAG system for reasoning over private datasets
Django Unfold admin theme - build, configure, and enhance modern Django admin interfaces with Unfold. Use when working with: (1) Django admin UI customisation or theming, (2) Unfold ModelAdmin, inlines, actions, filters, widgets, or decorators, (3) Admin dashboard components and KPI cards, (4) Sidebar navigation, tabs, or conditional fields, (5) Any mention of 'unfold', 'django-unfold', or 'unfold admin'. Covers the full Unfold feature set: site configuration, actions system, display decorators, filter types, widget overrides, inline variants, dashboard components, datasets, sections, theming, and third-party integrations.
Super Ralph Wiggum - autonomous iteration loops with templates, PRD support, progress tracking, and browser testing. This skill should be used when running Claude Code in autonomous loops for test coverage improvement, PRD-based feature development, documentation generation, dataset creation, lint fixing, code cleanup, or framework migrations. Combines the plugin's in-session loop mechanism with specialized templates and best practices from Geoffrey Huntley, Ryan Carson, and AI Hero.
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates.
Master Node.js streams for memory-efficient processing of large datasets, real-time data handling, and building data pipelines
Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Use this for exploratory data analysis (EDA), generating visualizations, finding trends, and deriving insights from datasets using Python (Pandas/Seaborn/Plotly) or SQL.
Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.
Complete Development Guide for Tables, Search, and Pagination Features in React/Next.js Projects. Covers core technologies such as race condition handling, search system implementation, pagination systems, infinite scrolling, CRUD synchronization, Intersection Observer API, and state management selection. Key Features: - Handle race condition issues in asynchronous requests - Implement high-performance search and autocomplete features - Build professional-grade pagination systems and caching strategies - Develop smooth infinite scrolling experiences - Ensure data consistency for CRUD operations - Select the most suitable state management solution Applicable Scenarios: - React/Next.js applications requiring search and pagination features - List display and CRUD operations for large datasets - Need for high-performance infinite scrolling or virtualized lists - Facing complex data management issues such as race conditions and state synchronization - Projects needing to select an appropriate state management solution
Verify that claims and direct quotes in research manuscripts are present in source materials. Systematically checks interview transcripts, datasets, or cited literature using fast search with haiku agent fallback for intensive reading.
Use this skill for ANY question about creating test or evaluation datasets for LangChain agents. Covers generating datasets from traces (final_response, single_step, trajectory, RAG types), uploading to LangSmith, and managing evaluation data.