Skill UI

This skill provides a complete local development loop for Ideogram integrations. It includes a strongly typed client wrapper, a mock server for offline testing (preventing API credit usage), and comprehensive Vitest unit tests. Ideal for setting up robust, fast-iterating development environments and ensuring API reliability.

This comprehensive guide enables developers to set up a robust local development environment for Instantly API integrations. It utilizes a dedicated mock server to allow for safe testing of API calls, writing comprehensive integration tests, and simulating webhook events without consuming real credentials or exceeding rate limits. Ideal for building reliable SaaS connectors.

A comprehensive guide covering 33+ essential JavaScript concepts, from fundamental types and type coercion to advanced topics like closures, event loop mechanics, hoisting, and asynchronous programming (Promises). Ideal for developers who need to deepen their understanding of JavaScript's internal workings, debug complex behavior, or teach robust JS fundamentals.

This guide provides a comprehensive workflow for setting up a robust local development environment for PostHog integrations. It covers enabling debug mode for real-time event inspection, implementing mocking strategies for stable unit testing, and configuring integration tests using a dedicated development PostHog project. Use this when building or testing analytics tracking features with posthog-js or posthog-node to ensure fast, isolated development cycles.

This guide provides a comprehensive, environment-aware setup for Sentry in local development workflows. It details how to correctly route development events to separate DSNs, enable verbose debugging, and maximize data capture rates (e.g., setting sample rates to 1.0). Users learn to use `startSpan` for performance profiling and ensure that sensitive credentials are safely managed outside of source control.

This protocol defines the structured communication rules for multiple specialized AI agents simulating C-suite roles (e.g., CFO, CTO, CRO). It dictates invocation syntax, response formatting, and enforces strict constraints (such as loop prevention, call depth limits, and isolation rules) to ensure coordinated, non-contradictory, and reliable cross-functional analysis for executive decision-making.

A comprehensive guide detailing best practices for building, reviewing, and deploying Salesforce Flows. This skill covers selecting the correct Flow type (e.g., Screen, After-Save), ensuring bulk safety by preventing DML/SOQL in loops, implementing robust fault handling paths, and performing automation density checks to ensure scalable and resilient automation.

This guide sets up a professional, cost-effective local development workflow for ElevenLabs TTS/voice projects. It includes SDK mocking to prevent character quota overruns during testing, hot-reloading capabilities, and quota-aware scripts. Ideal for developers building robust AI voice applications.

This guide teaches how to build robust, production-grade ReAct agents using LangGraph 1.0's `create_react_agent`. It specifically addresses common agent failure modes, such as preventing infinite loops (recursion caps) and ensuring proper error propagation. Learn to define typed tools using Pydantic, manage state with checkpointers, and set precise recursion limits. Perfect for users migrating from legacy AgentExecutor or building reliable, multi-step tool-calling workflows.

This CLI alias functions as a command for gracefully canceling an active PUA loop, mirroring the functionality of the Claude Code `/pua:cancel-loop` command. It cleans up loop state and worktree references, ensuring the cancellation event is recorded. It also safely manages configuration files, preserving unknown fields in `~/.pua/config.json`.

This skill enforces an algorithm-first discipline, requiring the user to state the time and space complexity (Big-O), the intended data structure, and the core algorithm family before writing any complex code (loops, queries, recursion). It serves as a critical gatekeeper to prevent common performance pitfalls like O(n²) complexity, N+1 database queries, and brute-force implementation errors, significantly improving code robustness and efficiency.

Arbor executes an Autonomous Optimization (AO) loop, allowing users to iteratively improve complex artifacts—such as models, agents, or data pipelines—against a measurable objective. It implements Hypothesis Tree Refinement (HTR), structuring research into a persistent tree to track hypotheses, insights, and evidence. This process is crucial for long-horizon optimization tasks (e.g., Kaggle-style benchmarking) and utilizes a held-out test set to prevent overfitting and ensure cumulative, auditable progress.