Skill UI

This skill provides a minimal working example for integrating with the Apollo.io API. It demonstrates the three core operations: searching for people, enriching individual contacts (to get emails/phones), and enriching entire organizations. It is ideal for developers starting a new Apollo integration, testing API setup, or learning basic data enrichment patterns.

This meta-skill is designed to guide and execute structured developmental workflows, enabling continuous skill improvement and knowledge transfer. It is applicable when a task requires following a defined developmental process. Users must ensure all necessary inputs, permissions, and success criteria are provided, and must treat the output as a guide, supplementing professional validation.

This skill acts as a code education expert, specializing in breaking down complex code, algorithms, or system architecture. It transforms difficult technical concepts into understandable explanations using clear narratives, visual diagrams, and step-by-step breakdowns. Ideal for creating learning materials, onboarding guides, or detailed technical reviews for developers of all levels.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

Personalized reports that analyze recent Claude Code chats to surface recurring challenges, highlight learning gaps, curate HackerNews resources, and deliver actionable development insights directly to your Slack DMs for targeted growth.

A comprehensive guide containing minimal working examples for all core Exa search operations. It demonstrates basic metadata search, deep content extraction (text, highlights, summary), finding semantically similar pages, and retrieving content for specified URLs. Ideal for developers starting a new Exa integration or learning advanced RAG patterns.

Provides minimal working examples and comprehensive instructions for querying the Fireflies.ai GraphQL API. Users can learn how to list workspace users, fetch recent meeting transcripts, and retrieve detailed summaries, including action items and keywords, using code examples in TypeScript, Python, and Bash. Ideal for initial API setup, integration testing, or learning GraphQL patterns for meeting data.

This skill provides foundational examples for interacting with Guidewire's core cloud APIs, including PolicyCenter, ClaimCenter, and BillingCenter. It demonstrates how to execute initial RESTful API calls, query resources (like accounts and claims), and understand common API response structures, pagination, and error handling patterns. Ideal for connectivity testing, learning Guidewire's REST structure, and prototyping basic insurance integrations.

A comprehensive guide and expert resource for mastering Julia 1.10+ development. Covers advanced topics including multiple dispatch, performance optimization, type stability, and modern tooling. Ideal for building production-ready applications in scientific computing, machine learning, data science, and web development.

Guides deliberate skill development during AI-assisted coding by offering optional 10–15 minute exercises after architecture work, schema changes, or “why” questions so developers stay engaged and understand decisions.

This guide demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.