Skill UI

This skill runs a read-only audit to assess production deployment readiness. It reviews release artifacts, migration changes, configuration gaps, and potential risks (including rollback feasibility and launch blockers) before actual deployment or merging. It helps reduce coordination failures and ensures a safe release cycle.

This skill guides developers through the complex process of integrating an iOS App Clip target into an existing Expo project. It covers setting up the necessary bundle identifiers, configuring associated domains (AASA file), and deploying the required metadata to allow the lightweight clip to be invoked directly from a URL alongside the parent application.

This skill mints tamper-evident, post-quantum-signed receipts for consequential agent actions (e.g., deployment, payment, access grant). It records who, what, and under which policy the action occurred, ensuring the evidence cannot be altered later. By providing verifiable proof from the certificate alone, it is crucial for meeting regulatory compliance requirements, such as the EU AI Act, and building auditable, trustworthy systems.

The Application Insights JavaScript SDK is designed to instrument client-side web applications for Real User Monitoring (RUM). It automatically collects crucial telemetry, including page views, user clicks, AJAX dependencies, unhandled exceptions, and custom events. This SDK supports correlating browser-side activity with backend services and includes specialized support for GenAI agent traces following OpenTelemetry standards.

This skill provides building blocks and workflows to programmatically design, test, and deploy automated triage agents for Monte Carlo alerts. Users can fetch, score, and troubleshoot alerts interactively or set up scheduled, reusable workflows, moving from manual review to fully automated incident response.

A comprehensive skill for managing AWS operational excellence, cost governance, and observability. It provides guidance on pre-deployment cost estimation (TCO), detailed spending analysis using Cost Explorer, setup of CloudWatch alarms, auditing activity via CloudTrail, and overall security posture assessment. Essential for optimizing cloud spending and ensuring reliable infrastructure.

A comprehensive guide for managing AWS infrastructure as code using the SST v4 (Ion) framework, built on top of Pulumi. This skill covers the entire lifecycle, including writing and editing sst.config.ts, defining high-level components (Function, Bucket, Dynamo), wiring resource links, writing unit tests for infrastructure, and troubleshooting deployments across complex, multi-stack AWS environments.

This comprehensive tool performs an architectural audit of a codebase, mapping module dependencies, checking layering integrity, and flagging structural decay. Drawing on classic engineering principles (like Conway's Law and Testability Seams), it provides a deep assessment of the project's health and structure. Ideal for large-scale code reviews or onboarding new developers.

A comprehensive guide on creating developer-centric changelogs and release notes. Learn how to structure updates using Semantic Versioning and the Keep a Changelog format. This skill helps communicate new features, breaking changes, deprecations, and performance improvements in a way that developers genuinely care about, building trust and demonstrating product momentum.

This skill guides users through an in-depth interview process to achieve maximum project context completeness (100% "Gold Code") for their .faf file. By systematically asking questions about project goals, architecture, and technology stack, it ensures all critical context slots are filled, making the project fully structured and highly readable by AI systems.

Create highly aesthetic, animation-rich HTML presentations from scratch or convert existing PowerPoint files (PPT/PPTX). This tool focuses on generating distinctive, professional web decks with fixed 16:9 ratios. It supports low-density for keynote talks or high-density for detailed reports, ensuring visual impact and zero dependencies.

A comprehensive guide and skill for deploying large language models (LLMs) locally using the llama.cpp framework and GGUF quantization format. It covers searching the Hugging Face Hub, selecting optimal quantizations (e.g., Q4_K_M), running local servers, and achieving OpenAI-compatible API endpoints. Supports various hardware backends including CPU, Mac Metal, CUDA, and ROCm. Ideal for developers needing offline, private LLM inference.