Skill UI

A comprehensive guide for implementing phishing-resistant multi-factor authentication using FIDO2/WebAuthn protocols. Learn to build a complete relying party server using Python, supporting both roaming hardware keys (e.g., YubiKey) and platform biometrics. Covers registration ceremonies, credential storage, and migrating to passwordless authentication.

This critical skill serves as a gate between completed experiments and final research conclusions. It systematically evaluates collected experimental results (e.g., W&B metrics, logs) against predefined research claims. It performs deterministic evidence pre-checks to verify if cited data points exist, and utilizes an advanced AI model (Codex) to judge whether the results support, contradict, or lack evidence for the intended claims, guiding the next action (pivot, supplement, or confirm).

This skill provides interactive, periodic monitoring of deep learning training runs. It continuously checks critical metrics—such as loss trends, evaluation scores, and gradient norms—against defined baselines to detect signs of divergence, NaN values, plateaus, or general instability. It delivers a structured report and recommends whether to continue, wait, or stop the training process, ensuring computational resources are not wasted on failing models.

Assesses AI/ML systems for prompt injection, jailbreak, model inversion, data poisoning, and agent tool abuse using MITRE ATLAS mapping, signature detection, and scoring metrics before deployment.

This skill provides programmatic access to comprehensive global financial market data via the Alpha Vantage API. Users can retrieve time-series data, including historical prices (OHLCV) for equities, options, forex, commodities, and crypto. It also provides access to fundamental metrics (e.g., P/E ratio, market capitalization), economic indicators (e.g., GDP, CPI), and over 40 technical indicators (e.g., RSI, MACD), making it ideal for quantitative analysis and financial modeling.

This skill monitors for regressions on deployed URLs following deployments, merges, or dependency upgrades. It performs comprehensive checks including HTTP status, console errors, network failures, performance metrics (LCP/CLS/INP), and crucial API endpoint health, ensuring service stability in production or staging environments. Supports quick checks, continuous monitoring, and differential comparison against baselines.

Crafts polished internal communications by matching request types (3P updates, newsletters, FAQs, etc.) to reference templates, gathering context, confirming scope, and producing concise, audience-aware drafts with metrics and links.

This skill provides a comprehensive, dual-engine audit for content, covering both Search Engine Optimization (SEO) and Answer Engine Optimization (AEO), along with readability metrics. It generates detailed scores (out of 100) and provides a severity-ranked list of issues (Critical, Important, Polish) with specific, actionable fix instructions. Use it before publishing to ensure content achieves optimal search performance and high engagement.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.

Guides multi-round investigation of any GitHub repository, combining API queries, discovery searches, and deep dives to produce structured markdown reports with summaries, timelines, metrics tables, and Mermaid diagrams for executive-ready briefs.

This skill provides comprehensive observability tooling for Go services, covering the five essential signals: structured logging (slog), Prometheus metrics, OpenTelemetry tracing, continuous profiling, and RUM. Use it for proactive production monitoring, correlating logs with traces, setting up SLOs, and ensuring all new features are fully instrumented for robust diagnosis.

A comprehensive, type-safe in-memory caching library for Go, designed for advanced system architecture. It supports nine sophisticated eviction algorithms (like W-TinyLFU, LRU, LFU, ARC) to optimize cache hit rates based on specific access patterns. Key features include TTL management, loader chains for read-through caching (with singleflight deduplication), sharding, and Prometheus metrics. Ideal for high-frequency services needing to reduce database load and latency.