Skill UI

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

This comprehensive guide provides minimal working examples for key Adobe APIs: Firefly AI image generation, PDF content extraction, and Photoshop background removal. It serves as a practical quickstart for developers starting new Adobe integrations, testing API connectivity, or learning basic API calling patterns using TypeScript and Node.js.

This guide assists developers in upgrading the Anima SDK versions and managing associated API changes. It covers migrating from manual Figma plugin workflows to automated SDK-based code generation, ensuring seamless integration of the latest design system features into development projects.

A comprehensive guide for developers on upgrading the Anthropic SDK versions and migrating between different Claude model generations. It covers best practices for version checking, reviewing breaking changes, updating model IDs across the codebase, running output comparison tests, and implementing gradual rollouts using environment variables to ensure stable deployment.

A comprehensive setup for creating a fast and reproducible local development loop for building Attio REST API integrations. This guide includes project structure setup, typed client generation, a mock server implementation (using MSW) for offline development, and a full test harness for unit and integration testing. Ideal for developers building complex SaaS integrations.

This skill automatically converts structured data specifications (such as CSV, JSON schemas, SQL DDL, or Protobuf definitions) into functional, production-ready code. It generates complete models, validation logic, full CRUD API endpoints, and unit tests in multiple languages (Python, TypeScript, Go, etc.), significantly reducing boilerplate coding time and ensuring type safety.

This skill automates the validation of Navan API integrations within CI/CD pipelines. It generates robust GitHub Actions workflows to perform comprehensive tests, including token health checks, booking data schema validation, and automated compliance report generation. By integrating these checks into your push/pull request process, it ensures the stability and adherence of your Navan connection without requiring a dedicated SDK.

This is a detailed offensive checklist for identifying and exploiting insecure deserialization vulnerabilities across multiple programming languages (Java, Python, PHP, .NET, Node.js, etc.). It covers techniques like gadget chain generation, magic method abuse, type confusion, and modern attack vectors, providing a deep methodology for penetration testing and vulnerability research.

This skill provides a comprehensive checklist and methodology for identifying Server-Side Template Injection (SSTI) vulnerabilities. It covers template engine identification (e.g., Jinja2, Twig), polyglot payload generation, systematic detection methods, and executing engine-specific Remote Code Execution (RCE) payloads. Ideal for security professionals testing web application input sanitization.

This tool provides a comprehensive security assessment framework for Retrieval-Augmented Generation (RAG) systems and vector databases. It tests critical vulnerabilities such as embedding inversion, cross-tenant data leakage, knowledge-base poisoning, and retrieval manipulation, aligning with OWASP LLM08:2025 standards. It is essential for validating the security posture of multi-tenant and vector-search applications.

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.