Skill UI

This skill outlines a systematic workflow for addressing review comments and issue feedback on GitHub Pull Requests (PRs) using the GitHub CLI (`gh`). It guides users through inspecting comments, planning fixes, applying code changes, and finally responding to the threads as resolved, ensuring a streamlined code review process.

A specialized skill for architecting, building, and productionizing complex Agent Development Kit (ADK) agents and multi-agent systems. This process emphasizes best practices, including clean module structure, robust tool integration, comprehensive regression testing, and deployment automation for reliable, shippable AI solutions. Use this when requiring the creation of structured, enterprise-grade agent code.

A systematic workflow designed to significantly improve the reliability and performance of existing AI agents. It covers detailed performance analysis, including gathering metrics (e.g., task success rate, latency), classifying failure modes, and advanced prompt engineering techniques (like Chain-of-Thought and Constitutional AI). The process culminates in structured A/B testing and controlled deployment, ensuring measurable, robust improvements.

A comprehensive guide to building production-ready Apache Airflow Directed Acyclic Graphs (DAGs). This skill covers best practices for operators, sensors, testing, deployment, and designing robust data pipelines. Use this when orchestrating complex workflows, managing ETL processes, or ensuring reliable batch job execution in a production environment.

Guides creation of algorithmic art philosophies and p5.js implementations that emphasize emergent behavior, seeded randomness, and a meticulously crafted algorithmic process, producing philosophy .md plus interactive .html/.js viewers for flow fields, particles, and controlled chaos.

Guides creation of algorithmic art philosophies and p5.js implementations that emphasize emergent behavior, seeded randomness, and a meticulously crafted algorithmic process, producing philosophy .md plus interactive .html/.js viewers for flow fields, particles, and controlled chaos.

Analyzes text to determine emotional tone and polarity, classifying passages as positive, negative, or neutral; ideal for processing reviews, social media posts, surveys, or any feedback that requires opinion mining or emotion detection.

A comprehensive workflow designed for creating, maintaining, and deploying high-quality API documentation. This process guides users through several phases, including API discovery, generating OpenAPI specifications (Swagger), writing detailed developer guides, adding functional code examples, setting up interactive testing interfaces, and structuring the final documentation site.

A comprehensive workflow designed for creating, maintaining, and deploying high-quality API documentation. This process guides users through several phases, including API discovery, generating OpenAPI specifications (Swagger), writing detailed developer guides, adding functional code examples, setting up interactive testing interfaces, and structuring the final documentation site.

A comprehensive, multi-step guide for migrating contact and company data into Apollo.io. Use this skill when switching from other CRMs (like Salesforce or HubSpot), consolidating multiple data sources, or executing large-scale data imports. It covers defining field mappings, pre-migration data assessment, and performing high-throughput batch processing using the Apollo Contacts API.

A comprehensive, production-grade reference architecture for integrating with Apollo.io. It details a layered design encompassing API services, business logic, background job processing (e.g., lead enrichment), and data modeling, enabling the building of robust sales intelligence and CRM synchronization systems.

This module provides production-ready TypeScript patterns for interacting with the Apollo.io REST API. It implements critical best practices including type-safe client setup (using Zod), custom error handling, exponential backoff retry logic, asynchronous pagination, and rate-aware bulk processing. Use this when building reliable, scalable, and robust integrations with Apollo data.