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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 guide for threat hunting and detection engineering focused on identifying SSO and OAuth token abuse. It detects 'pass-the-cookie' and session replay attacks where attackers use stolen tokens (like cookies or refresh tokens) to move laterally across federated SaaS platforms (Entra ID, Okta) without needing credentials. Essential for post-incident scope analysis and validating T1550.001 coverage.

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.

This skill guides the verification of software build provenance and signatures using industry standards like SLSA and Sigstore. It leverages tools like cosign and slsa-verifier to enforce keyless OIDC identity and confirm that artifacts were built by trusted sources without tampering. It is essential for hardening CI/CD pipelines and securing the entire software supply chain against advanced attacks.

This official Rust client library provides comprehensive functionality for interacting with Azure Queue Storage. It allows developers to reliably send, receive, manage, and peek messages within an Azure cloud environment. Use it in Rust applications that require asynchronous message queuing, ensuring robust communication and data synchronization between services. Supports full lifecycle management including deletion and peeking.

This advanced skill performs a full-sweep analysis, providing a unified assessment of code quality across multiple dimensions: code decay, architecture integrity, technical debt, and test coverage. It automatically applies safe fixes directly to the codebase, while requiring confirmation for risky changes. It is ideal for comprehensive pre-release code health audits and automated refactoring.

This skill provides a comprehensive test quality review, drawing on established principles from classic engineering texts like xUnit Test Patterns and How Google Tests Software. It diagnoses structural flaws—such as brittleness, decay, and poor coverage—in existing test suites, helping ensure that the codebase and its tests are robust, maintainable, and adhere to high industry standards.

Neon Object Storage provides S3-compatible, branch-aware object storage directly integrated with your Neon Postgres project. It ensures that files (images, documents, user uploads) remain perfectly synchronized with the database schema across all development branches (dev, preview, staging, production). Use this skill when you need a single backend for both database rows and file assets, eliminating the need for separate cloud storage providers.

This skill generates comprehensive, production-grade unit and integration tests using the Python pytest framework. It covers advanced patterns such as fixtures (with scope and teardown), parametrization, custom markers, mocking external dependencies, and best practices for writing robust and maintainable test suites. Use this when implementing detailed test coverage for Python code.

A comprehensive guide for developers working with Supabase. Covers best practices for database interaction, robust authorization using Row Level Security (RLS), secure authentication (Auth), and integrating various Supabase products (Storage, Realtime, Edge Functions). Essential for preventing common security vulnerabilities like BOLA/IDOR and ensuring data integrity across your application stack.

This skill automatically rewrites raw commit messages to adhere to professional engineering standards. It strips out common 'AI/marketing slop' phrases (like 'robust implementation' or 'leverage') while strictly maintaining the Conventional Commits format. It ensures subjects are concise, bodies provide necessary context (why, breaking changes), and the tone is direct and actionable, making your project history clean and readable.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.