Skill UI

This tool leverages an LLM-as-a-Judge agent to thoroughly evaluate staged Git changes before they are committed. It checks for correctness, completeness, and safety issues, providing a detailed verdict (APPROVE, NEEDS_REVIEW, REJECT) and actionable feedback. It is essential for maintaining high code quality, especially in complex or security-sensitive code sections.

This framework provides a rigorous pre-execution review mechanism for proposed actions by AI agents or systems. It functions as a critical safety boundary, auditing the entire decision path—from initial request to proposed tool execution—without authorizing or executing anything. It verifies action conformity, evidence integrity, temporal validity, and reviewer independence to ensure system compliance and security.

This skill provides an active penetration testing toolchain covering information gathering, port scanning, vulnerability scanning, web attacks (SQL injection, XSS), directory brute-forcing, and password cracking. It exposes 20+ security tools to AI agents via MCP servers for authorized security assessments.

This advanced skill performs server-enforced security and privacy reviews for applications, including those rapidly developed with AI agents. It implements comprehensive models like STRIDE (security) and LINDDUN (privacy), screens dependencies against live CVE/CISA data, and evaluates applicability of major EU regulations (GDPR, AI Act, NIS2). The output is a formal, auditable compliance report, never a simulated chat response or legal verdict.

A comprehensive security auditing tool for AI agent skills. It combines deterministic static scanning (SkillSpector) with deep semantic analysis to assess safety, trustworthiness, and potential risks. Determines if a skill is safe to install by checking for over-permissions, malicious behavior, and hidden dependencies.

This skill enforces strict communication protocols for AI agents interacting in shared channels with external parties. It ensures agents speak only when explicitly addressed, prevents internal context leakage, and routes sensitive content to draft-only approval. Ideal for group chats, DMs, and business partnerships where privacy and security are critical.

Performs static security analysis on GitHub Actions workflows that incorporate AI coding agents (e.g., Claude, Gemini, OpenAI). This skill helps identify critical data flow vulnerabilities, such as where attacker-controlled input can reach AI agent prompts or internal systems via CI/CD pipelines, going beyond simple expression checks.

This skill detects supply chain compromise risks in AI agents. It identifies vulnerabilities related to compromised dependencies, malicious plugins, tools, models, and untrusted update sources. It is used when agents dynamically load external resources or pull remote configurations. The skill probes for untrusted loading, dependency integrity, and malicious artifact injection to ensure agent security.

This skill provides defense-in-depth security controls for AI agents, covering prompt injection detection, input validation, tool abuse prevention, data exfiltration mitigation, and privilege escalation protection. It includes threat modeling with STRIDE, code examples for input sanitization, and guidance for secure agent deployment.

Secure AI coding agents with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments. Covers Claude Code, Cursor, Copilot, and Codex security implementations including CLAUDE.md configuration, command allowlists, filesystem access controls, and pre-commit hooks for secret scanning.

This toolkit provides six production-ready AI engineering workflows including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior engineering partners with structured methodologies, quantitative scoring, and checklists for consistent results in LLM system optimization and security.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.