Skill UI

Helps teams treat prompts like production code by providing registry, eval, and governance workflows for versioning, testing, and promoting prompts safely across environments.

A governance skill designed to secure AI agent tool calls using advanced authorization policies. It implements Cedar policies to control which tools agents can access and under what conditions. Key features include running in 'Shadow mode' for safe observation, 'Enforce mode' for strict blocking, and generating cryptographically signed Ed25519 receipts to provide an immutable, tamper-evident audit trail for every policy decision.

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

Deploy and govern MCP declarative agents within the Microsoft 365 admin center, managing their lifecycle, assignments, access controls, and compliance so Copilot delivers organizational capabilities securely.

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

A rigorous, Chief AI Officer-level interrogation tool designed to pressure-test any plan involving artificial intelligence. Before deployment, it forces comprehensive review across crucial dimensions: evaluation metrics, hallucination risk, EU AI Act compliance, model architecture selection (API vs. build), cost economics, and required organizational roles. Essential for maintaining AI product safety and compliance.

Polis Protocol is an advanced, vendor-agnostic framework designed to coordinate multiple AI agents as a self-improving team. Unlike passive task lists, it utilizes a learning router (bandit) to assign tasks based on an agent's proven track record for specific tags. It includes a robust governance layer, allowing agents to propose and vote on amendments to the protocol itself, ensuring the system evolves with its work.