Skill UI

A specialized audit skill designed to identify and quantify resource leakage, excessive costs, and billing vulnerabilities within complex development tool workflows (e.g., GitHub Apps). It traces webhook events, queue processing, and model calls to pinpoint where token burn or unauthorized resource usage is occurring, ensuring cost efficiency and clean billing practices.

A comprehensive resource providing codified expertise for managing complex energy portfolios for large commercial and industrial (C&I) consumers. Covers electricity and gas procurement, tariff structure analysis (TOU, RTP), demand charge mitigation, PPA evaluation, and multi-site energy strategy development. Ideal for optimizing utility spending, understanding complex utility bills, and building resilient, sustainable energy supply chains.

This skill provides comprehensive operational controls for long-lived, cloud-hosted agent workloads. It manages the entire lifecycle—from deployment and scaling to observability and security. Features include detailed monitoring (logs, metrics, traces), safety controls (kill switches, least privilege), and robust change management processes (rollout, rollback, auditing). Ideal for mission-critical microservices and background agents.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

This tool provides a comprehensive overview of the federation's operational status. It allows users to check overall health, list connected peers along with their trust levels and scores, and summarize crucial metrics like active sessions, message exchanges, PII redactions, and detected threats. Use it to inspect cross-installation agent connectivity and ensure the stability of the federated system.

Used to verify the actual billing reality, revenue, and pricing mechanics—such as team billing, quotas, and refunds—based on underlying product code, rather than sales documents or simple customer fixes. This skill separates billing facts, customer impact, and the true product implementation logic.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

A comprehensive guide to building highly accessible user interfaces using React and Next.js. Covers best practices for semantic HTML, proper ARIA attribute usage, managing form labels and error states, and ensuring full keyboard navigation support. Essential when developing any interactive component or form.

This skill provides a detailed, side-by-side comparison of advanced LLM agent evaluation frameworks, such as ruflo and HAL. It helps researchers and developers identify capability gaps—including real code execution, full web browsing, and structured file parsing—and prioritize architectural improvements for robust agent benchmarking and performance tracking.

Guides users through the entire end-to-end process of running the GAIA benchmark. It covers environment validation, cost estimation, model execution, and generating a signed, HAL-compatible submission package for leaderboards. This ensures high integrity and reproducibility for model performance evaluation.

GateGuard is a mandatory pre-tool-use hook designed to significantly enhance the reliability and safety of LLM-generated code and actions. Instead of relying on self-evaluation, it forces the model to perform concrete, verifiable investigations—such as listing all imported files, checking data schemas, or detailing rollback procedures—before any write or destructive command execution. It establishes a three-stage gate (Deny, Force, Allow) to prevent hallucination and ensure context awareness in complex coding environments.

This expert skill guides the creation of commercial-grade, highly optimized, and professional Windows installers for Python applications. It leverages Nuitka for extreme compilation (LTO, slimming, 32/64-bit selection) and Inno Setup for robust packaging. The goal is to deliver the smallest size, fastest startup time, and a residue-free user experience, strictly avoiding basic single-file builds. Ideal for professional software deployment.