Skill UI

A comprehensive checklist and implementation guide for utilizing RunwayML's API for advanced AI video generation and image editing. This skill provides the Python SDK pattern, covering tasks like image-to-video creation, model selection, and waiting for task completion. It also includes critical error handling for common issues (e.g., unauthorized access or insufficient credits), making it essential for developers building production-level creative AI applications.

This guide details how to manage and reduce the high rate-limit costs associated with the SalesLoft API. It explains the cost model, emphasizing that deep pagination significantly increases consumption. Learn advanced strategies like incremental syncing, caching frequently accessed data, implementing webhooks, and request consolidation to maintain efficiency and stay within budget limits.

This guide provides methods for managing API rate limits, specifically addressing complex cost-based throttling models (like SalesLoft's). It teaches how to implement robust rate-limit-aware clients, handle 429 errors using exponential backoff, and calculate the total cost associated with deep pagination, ensuring stable and optimized API throughput for large-scale bulk operations.

A comprehensive tool for searching the Model Context Protocol (MCP) across various GitHub locations, including PRs, issues, and discussions. It allows users to track the full lifecycle of protocol specifications, from initial proposals to final merged changes. Furthermore, it facilitates deep dives into pull request reviews and comments, enabling the extraction of key maintainer rationales and design intent for accurate technical understanding.

Provides comprehensive strategies for minimizing API credit consumption when using SerpApi. Techniques include implementing aggressive caching with Redis, utilizing the free Archive API for historical data retrieval, and selecting the appropriate engine (like Google Light API) to balance cost and speed. Includes cost calculation models and usage monitoring scripts.

This guide details how to use Shopify Metafields and Metaobjects via the GraphQL Admin API. Metafields allow attaching key-value custom data to existing resources (like products or orders), while Metaobjects enable defining entirely new, structured content types. Together, they provide a powerful way for developers to model complex custom data, often eliminating the need for external databases to extend e-commerce functionality.

Automate the Continuous Integration and Continuous Deployment (CI/CD) workflow for applications leveraging Together AI's OpenAI-compatible API. This skill sets up comprehensive GitHub Actions workflows to run unit tests (mocking completions and embeddings) on every Pull Request, and full integration tests (live API calls) upon merging to the main branch. It ensures robust validation of prompt formatting, model selection, and API connectivity for various LLMs.

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 workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.

This tool provides a local development loop for integrating with Together AI's OpenAI-compatible API. It mocks key functionalities like chat completions, embeddings, and model listing, allowing developers to build and test AI applications rapidly without consuming live API credits. Developers can toggle between mock mode for rapid iteration and live mode for final model evaluation, ensuring a seamless workflow.

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.

This tool extracts and formalizes the core business terminology (Ubiquitous Language) from a conversational context. It generates a structured glossary, identifies ambiguities, resolves conflicting terms, and proposes canonical names. It is essential for building consistent and unambiguous domain models, particularly when following Domain-Driven Design (DDD).