Skill UI

This utility conducts a thorough audit of project documentation against the current codebase to ensure complete consistency. It supports 'delta' (detecting changes since merge-base) or 'full' auditing modes. By utilizing Impact Mapping and verifying cross-reference pairs, it identifies outdated specs, missing documentation, or inconsistent references across the entire project ecosystem.

This guide provides the reference architecture and instructions for automating TechSmith workflows. It details using the Snagit COM API and Camtasia Producer CLI for screen capture, video editing, and batch rendering processes. The architecture emphasizes using PowerShell and Python interop (pywin32) to manage complex automation tasks effectively.

A comprehensive reference guide for common errors encountered when interacting with the Together AI API. This includes error codes and solutions for inference, fine-tuning, and model deployment, covering authentication, rate limits, validation failures, and billing issues. Essential for developers integrating OpenAI-compatible LLM services.

A comprehensive reference architecture for building scalable, cost-effective, and robust AI services. This system manages the full AI lifecycle, including intelligent model routing (for quality/cost trade-offs), response caching (using Redis), asynchronous batch processing, and fine-tuning pipeline management via Together AI's OpenAI-compatible API. Ideal for enterprise applications requiring high availability across multiple open-source models.

This technique is used to locate a specific TV series by leveraging the known filmography of its creator (director, writer, or showrunner). When the question provides clues that the creator also worked on a distinct, identifiable project (e.g., a trilogy or an award-winning miniseries), use that secondary work as a powerful search anchor. Identify the person first using their other works, then trace back through their complete credits to pinpoint the target series.

This skill provides a structured framework for capturing, organizing, and maintaining persistent facts about the user. By segmenting facts into topic-specific files (e.g., coding preferences, tech stack, goals), it creates a reliable, objective knowledge base. This structure is crucial for AI agents and applications to achieve highly personalized interactions and provide accurate context.

This guide provides comprehensive reference architecture for integrating with Veeva Vault, covering REST APIs, VQL query language, and VAPIL Java SDK. It details key concepts like authentication, common data patterns, and error handling, making it essential for developers working on life sciences or CRM solutions built on the Vault platform.

This guide provides a comprehensive reference for diagnosing and fixing common Webflow Data API v2 HTTP errors (400, 401, 403, 404, 409, 429, 500). It covers the root causes and offers concrete code solutions in TypeScript and Bash for issues like invalid tokens, insufficient scope, or rate limits, helping developers effectively troubleshoot failed Webflow integrations and ensure data flow stability.

Provides a comprehensive, layered reference architecture for building production-grade integrations with Webflow's Data API v2. This guide separates concerns into distinct layers: API access, business logic (CMS, e-commerce), webhook handling, and caching, ensuring scalability and maintainability for complex web applications.

This guide provides the reference architecture and best practices for integrating Wispr Flow's voice-to-text API. It details how to utilize both WebSocket (for low-latency streaming) and REST APIs, covering essential concepts like audio format requirements, context awareness (especially for programming code), and comprehensive error handling strategies.

This guide provides the reference architecture for integrating Workhuman's Social Recognition and rewards APIs. It details key concepts like OAuth 2.0, peer-to-peer recognition, and configurable awards. Use this skill when building complex employee recognition workflows, syncing data bi-directionally with HRIS systems (e.g., Workday, SAP), or integrating with collaboration tools like Slack and Microsoft Teams.

This skill provides a comprehensive, structured process for creating and documenting new AI agent skills. It guides the user through gathering requirements, drafting structured files (like SKILL.md, REFERENCE.md), and optimizing descriptions to ensure the agent can correctly identify and utilize the new capability. Use when you need to build, write, or refine a complex, functional skill module for an AI agent.