Skill UI

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.

A comprehensive Python SDK for interacting with the Azure AI Translator service. It provides robust features including real-time text translation, language detection, script transliteration, and detailed dictionary lookups. Ideal for developers building multilingual applications that require accurate cross-cultural text processing.

A comprehensive Python SDK for interacting with the Azure AI Translator service. It provides robust features including real-time text translation, language detection, script transliteration, and detailed dictionary lookups. Ideal for developers building multilingual applications that require accurate cross-cultural text processing.

A comprehensive TypeScript SDK for integrating Azure AI Translator services. It provides robust clients for both real-time text translation (including language detection, transliteration) and large-scale document translation. Users can translate single documents or manage complex batch translation jobs using REST-style API calls, making multilingual support easy to integrate into applications.

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

A comprehensive guide to setting up a fast, reproducible local development workflow for Groq applications. This setup includes structured project scaffolding, implementing singleton clients, defining model constants, and providing isolated unit tests with mocking, alongside live integration tests. Ideal for developers building low-latency LLM applications and ensuring rapid iteration cycles.

A comprehensive guide and implementation structure for configuring Instantly.ai across Development, Staging, and Production environments. This skill ensures strict workspace isolation by utilizing environment variables and dedicated workspaces, preventing accidental data leakage or incorrect live deployments during testing and iteration cycles. It covers secure key management and safe promotion workflows.

This sophisticated skill automates the entire Pull Request (PR) quality assurance lifecycle. It systematically fetches and categorizes CI check failures and detailed review feedback using the LOGAF priority scale. The workflow guides users through root cause analysis, proposing minimal, targeted fixes, replying to reviewers, and committing updates iteratively until all checks pass, ensuring high code quality before merging.

This setup configures a comprehensive local development workflow for integrating with the Juicebox API. It provides a fast feedback loop by allowing developers to mock profile searches and candidate enrichment data. Users can easily toggle between mock mode for rapid iteration and sandbox mode for validating against the real API, ensuring efficient development without consuming live API credits.

A comprehensive guide to configuring a structured, local development environment for LangChain applications. It covers setting up project architecture, managing dev dependencies, and implementing a two-tiered testing strategy: isolated unit tests using mocked LLMs (for fast, API-free testing) and robust integration tests using real API providers. Ideal for establishing a reliable, rapid iteration workflow.

This guide provides a comprehensive local development setup for LLM applications using Langfuse. It enables fast iteration cycles, real-time trace debugging, and hot reloading, making it ideal for developers building and testing complex AI workflows locally. Instructions cover environment setup, advanced SDK usage, and optional self-hosted Docker deployment.

This guide establishes a comprehensive and rapid development loop for Obsidian plugins. It covers the entire workflow, from setup (cloning templates, symlinking) to advanced features, including running esbuild in watch mode, achieving hot-reloading (via Ctrl+R or automatic methods), deep debugging with Chrome DevTools, and running unit tests with vitest. Essential for developers seeking fast feedback and reliable plugin iteration.