Skill UI

This skill provides comprehensive management for multiple local Command Line Interface (CLI) agents. It allows users to start, stop, monitor, and assign tasks to these agents, running them in separate tmux sessions for parallel execution. It is ideal for complex, scheduled workflows requiring multi-agent coordination, task monitoring, and log tailing, making it invaluable for advanced automation setups.

This skill defines the coordination protocol for multi-agent execution during development sprints. It governs how project architects must spawn specialized agents (e.g., backend, frontend, QA), manage dependencies via shared contracts, and collect comprehensive, structured reports. It ensures systematic, parallel, and controlled project iteration development.

An expert framework for creating realistic API mock services. It simulates real API behavior and contracts, enabling developers to build and test frontend components or integration workflows in parallel, even before the actual backend service is complete. Ideal for testing, demo environments, and validating API contracts.

An advanced guide for optimizing API interaction with Apollo.io. This skill covers critical performance enhancements including implementing connection pooling, setting per-endpoint time-to-live (TTL) caching, utilizing bulk enrichment endpoints for efficiency, and controlling concurrency during parallel searches. It also demonstrates how to slim down large API response payloads to reduce memory and bandwidth usage.

A comprehensive utility designed to manage and automate the safe migration of applications using the Apollo.io API. It guides developers through auditing current API usage, identifying deprecated endpoints and authentication methods, and implementing feature-flagged changes. Key features include generating migration maps and running shadow tests for parallel comparison between old and new API versions, ensuring zero downtime during major system upgrades.

This comprehensive guide details how to migrate from multiple standalone data enrichment providers (like ZoomInfo, Apollo, Clearbit) or complex custom scripts into a unified Clay pipeline. It covers auditing current data stacks, mapping disparate fields, running parallel validation tests, and configuring Clay to consolidate data sources, significantly reducing complexity and operational costs.

A comprehensive, phased guide detailing how to migrate your code review process to CodeRabbit. It covers the entire journey, from assessing current tools (like SonarCloud, DeepSource, or manual reviews) and mapping existing rules, through to running a parallel pilot phase and finally achieving full adoption. Ideal for engineering managers or DevOps teams planning a major CI/CD tool transition.

This skill is designed to configure a Rails project workspace to function correctly within Conductor, a platform for parallel coding agents. It sets up the necessary scaffolding, including `conductor.json`, executable setup scripts, and server scripts. Additionally, it updates critical Rails configuration files (such as Sidekiq, Cable, and development environment settings) to ensure proper environment variable handling, Redis isolation, and port management for reliable parallel agent execution.

This skill unlocks advanced multi-step code generation and architectural refactoring capabilities using autonomous agents. It supports parallel agent workflows, complex multi-file migrations, and multi-codebase orchestration by autonomously calling tools (Read, Write, Bash) in controlled, checkpointed cycles, drastically improving large-scale development efficiency.

This skill provides advanced engineering patterns to dramatically improve the performance and reliability of Customer.io API integrations. It implements critical optimizations such as HTTP connection pooling, LRU deduplication caching, parallel batch processing, and fire-and-forget async tracking. Use this when integrating Customer.io into high-throughput, mission-critical systems to reduce latency from seconds to milliseconds.

This guide provides advanced techniques to optimize Deepgram API performance. Learn how to preprocess audio using ffmpeg (resampling, noise reduction), select optimal transcription models (speed vs. accuracy), and implement efficient streaming or parallel batch processing to achieve high throughput and low latency.

Guides engineers through DeepSpeed distributed training and NVMe I/O optimizations, covering ZeRO, pipeline parallelism, mixed precision, and DeepNVMe handles for synchronous and asynchronous tensor storage.