Skill UI

Provides authentication, authorization, encryption, secrets, and hardening patterns for designing secure auth flows, managing tokens, configuring CORS, and implementing best practices around API keys, JWT, OAuth, and rate limiting.

A specialized methodology for reviewing APIs, configuration schemas, and interfaces to identify 'footguns'—design choices where the path of least resistance leads to insecurity. This analysis focuses on making secure usage the default, covering topics like dangerous defaults, primitive vs. semantic type safety, and insecure algorithm selection. It is crucial for architects and reviewers to prevent developer misuse at the design level.

A comprehensive tool designed to audit the quality, relevance, and redundancy of all Claude skills and commands. It supports two modes: a rapid incremental scan for recently changed skills, and a deep full stocktake using AI judgment and expert checklists to assess viability (Keep, Improve, Update, Retire, Merge).

Automates guidance for sorting parameter handlers in API development, covering REST, GraphQL, OpenAPI, authentication, and design patterns, with step-by-step best practices and production-ready validation.

A comprehensive diagnostic bundle designed for developers integrating with the Speak API. It systematically collects critical information, including authentication status, audio format validation (checking codecs, sample rates, and file size using ffprobe), and network connectivity tests for both Speak and OpenAI endpoints. Use this tool when troubleshooting complex language learning integration issues or verifying API setup.

A comprehensive checklist designed for developers to ensure a language learning integration (Speak) is robust and ready for production deployment. It covers critical technical areas including API authentication, audio processing pipelines, rate limiting strategies, monitoring and alerting setup, and adherence to global data compliance standards (e.g., GDPR, FERPA). Use this when implementing or troubleshooting production-grade features.

Guides Java engineers through Spring Boot backend architecture, REST API design, service layering, repository patterns, caching, async handlers, logging, middleware, and resilience practices for scalable production services.

A comprehensive guide to building functional and scalable Telegram bots. Covers bot architecture design, mastering the Telegram Bot API, optimizing user experience (UX), implementing monetization strategies (Freemium, Subscriptions), and deploying production-ready bots using webhooks. Learn to transform complex ideas into engaging, real-world automated assistants.

Document outlines testing strategies such as contract, snapshot, mutation, property-based, integration with containers, and disciplined test organization to help teams design reliable suites that cover behavior, API contracts, and invariants.

This comprehensive skill provides advanced architectural patterns for implementing robust API throttling and rate limiting policies. It covers critical concepts like concurrency limiting, priority queuing, circuit breakers, and adaptive throttling that adjust limits based on real-time backend health metrics (p95/p99 latency). Use this when designing resilient, scalable backend services that must protect themselves from overload and maintain stability under high load.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

torchforge is Meta's advanced PyTorch-native library designed for agentic Reinforcement Learning (RL). It fundamentally separates core RL algorithms from complex distributed infrastructure concerns. This allows researchers to rapidly prototype and experiment with novel RL methods (like GRPO) using clean, PyTorch-native abstractions, while automatically handling large-scale tasks like distributed training across thousands of GPUs using Monarch and TorchTitan.