Skill UI

This script provides a lightweight, standalone Python integration for Google Sheets, offering full read and write access without needing a dedicated server. Users can retrieve spreadsheet data in multiple formats (plain text, CSV, JSON), extract specific ranges, find sheets via search queries, and perform advanced write operations like updating, appending, or clearing cell values. It is highly suitable for automating data reporting, extraction, and workflow integrations involving Google Workspace.

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

Phoenix Evals lets teams build evaluators for AI/LLM apps by wiring deterministic code with LLM nuance, running Python or TypeScript workloads, validating against humans, and iterating through error analysis, RAG, and production workflows.

A comprehensive reference for diagnosing and fixing silent breaking changes in React 18 class components. This guide covers how React 18 automatically batches state updates in asynchronous contexts (e.g., setTimeout, Promises, async/await), providing patterns for handling state reads after awaiting, and offering guidance on when to correctly use `flushSync` versus refactoring.

Reads and summarizes local text-based files such as Markdown, JSON, CSV, and source code, including quick type checks and smart tailing for big logs while skipping PDFs, images, and binaries.

This guide maps user questions and topics (e.g., configuration, memory, skills, CLI) to specific, relevant documentation paths and source code entry points within the QwenPaw ecosystem. It is designed for built-in QA Agents to eliminate aimless searching, ensuring the agent reads the most accurate files to provide comprehensive and precise answers about the system's features.

This advanced research technique is designed for deep dives into football topics. Instead of relying solely on the original query, it mandates tracking every discovered entity—such as player names, club names, or coaches—from search results. By treating these entities as new leads, the user must formulate follow-up searches using the discovered names and remaining constraints, ensuring nothing relevant is overlooked and building a comprehensive knowledge chain.

This technique guides users to find obscure bands by utilizing unique, non-musical personal experiences of the band members (e.g., major surgery, relocation, unique side careers). Instead of searching directly for band names or discography details, the user should identify the most distinctive life event mentioned in the query. Searching for this unusual personal anchor point first is highly effective, as it leads to the member's identity, which can then be used to pinpoint the correct band.

Provides programmatic access to massive, versioned public single-cell and spatial transcriptomics datasets from CZ CELLxGENE. This skill allows users to efficiently query specific subsets of cell metadata, gene expression matrices, embeddings, and summary statistics across various organisms, tissues, and diseases. It is ideal for large-scale, memory-efficient biological research and model training without full dataset downloads.

Modal is a powerful serverless cloud platform designed specifically for running AI and Machine Learning workloads. It allows users to deploy, serve, and scale complex models (like LLMs) without managing infrastructure. Key features include on-demand access to high-end GPUs (A100, H100), serverless function scaling, persistent storage, and the ability to define entire infrastructure using pure Python code, eliminating the need for separate YAML or Dockerfiles.

A comprehensive guide to creating, managing, and processing ClickUp webhooks. Learn how to set up real-time HTTP POST notifications for resource changes (tasks, lists, spaces). This is essential for implementing two-way synchronization, building external automation workflows, and processing detailed event payloads for seamless system integration.