Skill UI

This command inspects the native code execution sandbox environment. It reports on platform compatibility (e.g., bubblewrap, Seatbelt), active security configurations (auto-allow status, filesystem policies, network allowlists), and details recent security violations. Use it to audit the execution environment before running autonomous workflows.

This skill enables the execution of OpenSquilla agents against complex, real-world software engineering benchmarks (SWE-bench). It runs the entire process—from pulling the necessary Docker image to collecting the patch and running official evaluations—in a containerized environment. Use it to test an agent's ability to solve specific coding issues, benchmark performance across datasets (e.g., verified, multilingual), or verify if a generated patch successfully resolves an instance.

This comprehensive culinary assistant guides you through meal planning, recipe execution, and advanced cooking techniques. It integrates principles of food science and flavor architecture, helping users define goals, troubleshoot flavor issues, and execute dishes with precision. Use it to master everything from basic knife cuts to complex plating.

Automates the deployment process for services by generating and executing necessary shell commands. This skill is ideal for CI/CD pipelines or manual staging deployments, allowing users to target specific services and environments while ensuring safety by requiring script review before execution. Note that deployment will restart running services, causing brief downtime.

A systematic methodology for resolving discrepancies between conceptual models (documentation, tests, diagrams, metrics) and actual observed behavior (live code execution or real data). This process mandates that observed reality (the 'territory') always overrules the theoretical map, ensuring system integrity and accurate knowledge updates.

A systematic planning technique that assumes a project or plan has already failed. By reasoning backward from a catastrophic outcome (in past tense), this method identifies the necessary conditions for failure. These necessary conditions are then inverted into concrete, verifiable preventive requirements, gates, and mitigation strategies, ensuring proactive risk management before execution begins.

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.