Skill UI

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

Provides Apple Human Interface Guidelines interaction and UX patterns, covering onboarding, feedback, modality, notifications, search, settings, drag and drop, and more for designing intuitive Apple platform experiences.

Automates the fix-push-wait cycle for pull requests. Fetches CI check status and categorized review feedback, fixes failures and blockers, then commits, pushes and re-checks until all checks are green.

Classify and prioritize user-defined inboxes, support tickets, feedback, or records using custom criteria. Supports bulk parallel judgments with smoke_test pilot, review queues, and labels without automatic replies or mailbox changes.

A human-in-the-loop studio for iteratively tuning AI outputs or reviewing complex media/prose. It allows users to directly manipulate visual parameters (color, typography, layout) or annotate documents in real-time, moving beyond static comparison grids or vague prompts. Perfect for designers and content creators who need hands-on, visual feedback.

Discover, find, compare, audit, repair, adapt, craft, run, debrief, and prepare repeatable AI-agent loops for publication. A comprehensive tool for managing bounded feedback loops in AI agent workflows.

Expert skill for Makepad animation development. Generates animation code, explains states and transitions, handles hover effects, animator timelines, easing functions, and keyframe animations. Used for building interactive UI feedback in Makepad Rust applications. Supports Forward, Snap, Reverse, Loop, and BounceLoop timeline types with various easing functions.

Discover, inspect, and safely invoke 2,000+ AI models and APIs through SandBase's local MCP bridge, with schema, price, and checksum verification before any paid call.

This skill provides comprehensive testing approaches for Temporal workflows using pytest. It covers unit testing with time-skipping, integration testing with mocked activities, replay testing for determinism, and local development setup. It helps achieve high test coverage and fast feedback loops for Python-based Temporal applications.

Build a measured exposure and grading path in Three.js with a 64x36 luminance meter, async readback, weighted log-average exposure, asymmetric adaptation, single tone-map ownership, and a generated 32-cube post-tone-map LUT.