Skill UI

This comprehensive tool provides programmatic control over Lark/Feishu spreadsheets. It supports creating, reading, updating, and managing worksheets, handling complex data types (formulas, mentions, dropdowns), and setting advanced filters. Use it for automated data processing, bulk data entry, and integrating spreadsheet logic into complex workflows.

A comprehensive module for managing knowledge bases (Wiki) and associated resources within the Feishu platform. It covers the full lifecycle of knowledge spaces, nodes, and members. Provides advanced procedures for creating, retrieving, moving content, and managing user/group/department access, while enforcing critical best practices for identity resolution and API usage.

Query authoritative Microsoft documentation to explain Azure, .NET, M365, Windows, and Power Platform concepts, tutorials, limits, configurations, or best practices whenever a user wants to understand how something works.

Example skill that walks through the Anthropic SKILL.md structure, showcasing when to load it, how to author steps, and how to reference bundled resources for skill creation.

officecli is a single-binary CLI for creating, inspecting, validating, and updating Word/Excel/PowerPoint files without Office; ideal for scripting flows that add slides, edit tables, or check formatting across docs.

Master the art of creating, maintaining, and running robust end-to-end (E2E) tests for Flutter applications using the Patrol framework. This skill covers validating complex user journeys, testing interactions with native system features (like permissions and deep links), capturing UI regressions, and ensuring consistent cross-platform behavior across Android, iOS, and Web. Best practice guidelines for deterministic testing are included.

Provides a measurement-driven workflow for improving web application performance, covering when to optimize, how to profile front-end and back-end bottlenecks, and how to monitor Core Web Vitals before and after fixes.

Guides Flutter/Dart developers through Riverpod setup and best practices for defining providers, using refs, handling lifecycles, combining async data, performing side effects, observing changes, and testing so app state stays consistent and leak-free.

Guides teams through a final launch-readiness checklist covering code quality, security, performance, documentation, feature flags, staged rollouts, and monitoring to ensure confident production deployments.

A comprehensive workflow for the full lifecycle management of AI skills. This tool guides users through creating skills from scratch, modifying existing ones, and rigorously benchmarking performance. It supports iterative refinement, quantitative evaluation, and optimizing skill descriptions for maximum triggering accuracy.

Guides multi-axis reviews before merging, checking correctness, readability, architecture, security, and performance while enforcing concise change sizing and descriptive commit messaging to keep PR history clear.

Streamlines the process of providing code review feedback by enforcing a highly structured, ultra-compressed format. It guides users to cut through noise and deliver actionable, single-line comments (Location: Problem. Fix.) while maintaining critical context like severity and technical rationale. Ideal for team settings prioritizing efficiency and high code quality.