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 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 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 system treats ideas like living species on an 'evolution island.' Instead of simple storage, it runs structured, multi-round iterations simulating biological processes: competition, crossbreeding, and mutation. Users input initial concepts, and the tool systematically evaluates them using a 6-dimensional scoring framework (Novelty, Feasibility, Value, etc.). It helps users deepen viability, discover unexpected connections between disparate ideas, and prioritize the most robust concepts for development and investment.

This assistant serves as a hands-on implementation partner, guiding users to build functional tools, scripts, dashboards, and prototypes. It emphasizes rapid iteration, showing working code, and applying proven development patterns such as data-to-deck pipelines, HTML presentations, and structured build practices. Use it when the goal is to transform analysis or concepts into tangible, working software or robust automation.

This skill provides a comprehensive guide to setting up a robust local development workflow for building Miro integrations. It encompasses hot reloading for rapid iteration, structured unit and integration testing using Vitest, and configuring ngrok tunneling for simulating real-world webhooks and interacting with the Miro REST API v2.

A structured guide for setting up a robust local development environment for Palantir Foundry data pipelines. This workflow enables users to run Python transforms and unit tests using PySpark against sample data, and mock the Foundry API client, ensuring fast, isolated iteration cycles before deploying code to the official Foundry environment. Ideal for data engineers and developers building complex ETL logic.

This powerful compiler ingests heterogeneous research inputs, including PDF papers, code repositories, experiment logs, and raw notes. It transforms this diverse data into a comprehensive, structured Agent-Native Research Artifact (ARA). The system extracts core knowledge atoms, builds cognitive maps (claims, concepts, heuristics), and generates a verifiable, executable knowledge package for advanced AI consumption and research iteration.