Skill UI

Turns messy workflows, docs, transcripts, or existing scripts into production-ready cross-platform agent skills by surfacing hidden requirements, generating specs, and validating exports for Claude Code, Copilot CLI, Cursor, and similar runtimes.

A comprehensive guide and set of scripts to bootstrap, build, package, sign, and notarize SwiftPM-based macOS applications without requiring Xcode projects. It provides a complete end-to-end workflow for managing app releases, including generating AppCasts and handling complex code signing and notarization steps.

Review Swift 6.2+ codebases for actor isolation, Sendable violations, and async migration issues, then apply minimal-impact fixes so concurrency diagnostics disappear while guiding developers through main-actor contexts and safe background work.

Diagnose SwiftUI performance issues by reviewing code first, classifying symptoms, and guiding users through profiling when runtime evidence is needed, then summarizing causes, fixes, and validation steps.

Keeps project-specific memory for Claude Code by auto-loading context on each session, tracking git activity, and running deterministic Node.js scripts so instructions remain consistent across models.

A library-agnostic Flutter/Dart code review checklist that walks through widget best practices, state management patterns, Dart idioms, performance, accessibility, security, and clean architecture, helping reviewers spot regressions in any app stack.

Transforms Claude Code into a continuous autonomous agent with persistent memory, scheduled operations, remote dispatch, computer automation, and task queuing for repeatable workflows and monitoring.

Turns your existing Kanban board into an autonomous AI-driven development pipeline, coordinating Claude Code workers, deterministic gates, cost tracking, and crash-proof orchestration for full lifecycle work.

Adds samber/oops structured error builders to Go services, giving stack traces, codes, user-friendly messages, panic recovery, and exporter-friendly attributes for observability and diagnostics.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).

Copilot Spaces allow users to consolidate and manage project-specific knowledge, including documentation, code snippets, architectural standards, and operational instructions. By loading a Space, Copilot grounds its responses not just on general knowledge, but on the curated, authoritative context of the entire project, enabling accurate Q&A, code generation, and workflow execution based on team standards. Ideal for onboarding, large-scale projects, and maintaining consistency.

This guide provides a detailed template and directive for generating machine-readable, deterministic implementation plans. It is designed for complex software development tasks, such as adding new features, refactoring existing code, or designing system architecture. The output ensures that all steps are atomic, measurable, and structured for autonomous execution by AI agents or human teams, minimizing ambiguity and dependency.