Skill UI

Automatically scans modern API frameworks (Next.js, Express, FastAPI, Django) to generate comprehensive, ready-to-run test suites. It ensures complete coverage for critical areas including authentication, input validation (boundary/injection), error code matrices, pagination, file uploads, and rate limiting. Ideal for TDD, contract testing, and security audits.

A powerful tool to scaffold and ship production-ready Model Context Protocol (MCP) servers directly from OpenAPI specifications. It supports Python and TypeScript, ensuring schema validation, safe evolution, and robust tool definition generation, eliminating the need for manual wrapper coding. Use it to expose existing REST APIs to LLM agents or build shared tool integrations.

A comprehensive technical reference for implementing advanced Lightning Network channel factories. It covers complex topics such as multi-party channels, Layer 2 scaling on Bitcoin, and advanced protocols including Decker-Wattenhofer invalidation trees, MuSig2 key aggregation, and encrypted Noise NK transport. Essential for developers building highly scalable and secure Bitcoin infrastructure.

A comprehensive guide and skill for building robust, production-ready REST API endpoints. This skill covers full lifecycle development, including input validation, centralized error handling, authentication/authorization checks, CRUD operations, pagination, and generating proper API documentation. It ensures that back-end services are secure, scalable, and adhere to industry best practices.

A structured methodology for de-risking product ideas and validating market opportunities. This framework guides teams through defining measurable outcomes, building Opportunity Solution Trees (OST), mapping critical assumptions (Desirability, Viability, Feasibility), and planning rigorous experiments (prototyping, interviews, usability tests) before committing to full development resources. Ideal for discovery sprints and pre-build validation.

This comprehensive tool validates Claude Code plugins against official Anthropic specifications and internal enterprise standards. It performs two main types of checks: structural validation (checking `plugin.json` fields, file references, and permissions) and content validation (conducting deep grading using `SKILL.md`). Use this when building, auditing, or preparing plugins for marketplace submission to ensure compliance and quality.

This skill provides a comprehensive workflow for building vendor-agnostic detection rules using the Sigma format. It empowers security operations center (SOC) engineers to translate raw threat intelligence and MITRE ATT&CK techniques into standardized, portable detection logic. The process guides users through rule validation and conversion into specific queries for major SIEM platforms, including Splunk, Elastic, and Microsoft Sentinel, ensuring robust detection coverage.

This skill outlines a comprehensive, structured workflow for managing Microsoft Patch Tuesday security updates. It covers the entire lifecycle, from initial vulnerability triage and risk assessment to testing, staged deployment (Pilot, Production Rings), and final validation, ensuring compliance with risk-based remediation SLAs.

This comprehensive guide provides essential security protocols for building robust Laravel applications. It covers best practices for authentication (Sanctum, Passport), authorization (Policies), input validation, and protecting against common vulnerabilities like CSRF, XSS, and SQL injection. Key topics include secure file uploads, rate limiting, environment variable management, and implementing robust middleware.

Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers. It normalizes various threat intelligence objects (indicators, actors) into platform-native schemas, enabling automated ingestion, validation, and routing to SIEM, TIP, and EDR systems. Ideal for integrating new ISAC feeds or building bi-directional intelligence pipelines.

A comprehensive, NIST/CISA-aligned workflow for structured recovery following a ransomware incident. It covers critical steps including environment isolation, forensic evidence preservation, Active Directory restoration (DSRM, krbtgt reset), backup integrity validation, and phased, dependency-based system rebuild. Essential for post-incident disaster recovery planning.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.