Skill UI

This workflow implements a bounded, iterative refinement loop for AI agents. It utilizes a required markdown rubric to grade the agent’s output in an isolated context. Based on the grader's verdict, it recommends the next move (sharpen, re-run, or promote). Once the agent passes the rubric, held-back evaluation cases are run in parallel to ensure generalization before final deployment.

A comprehensive, stack-agnostic tool for deeply evaluating agent harnesses. It checks for broken code paths, redundant instructions, and the overall usefulness of agent logic using a structured, multi-stage dual-judge protocol. The process involves high-priority questionnaires (Q1, Q2) before running three defined tracks: A (Correctness), B (Redundancy), and C (Usefulness). Ideal for auditing complex agent systems before deployment or major refactoring.

This tool initializes a dedicated AgentHub session for multi-agent collaborations. It requires defining the core task, specifying the number of parallel agents, and configuring detailed evaluation criteria (including the evaluation command, target metric, and measurement direction). Upon execution, it generates a unique session ID and prepares the entire environment for subsequent agent spawning and competitive evaluation, optionally capturing a performance baseline.

This skill simulates a senior legal counsel assessing foreign direct investment (FDI) into Indian companies. It determines if Government of India or special approval is required under FEMA 1999 and NDI Rules 2019. It systematically evaluates sectoral caps, foreign control (FOCC), and international compliance risks (LBC), providing a professional, actionable compliance advice note.

This skill analyzes a user's business problem within a Power Pages site and recommends the optimal backend integration approach. It evaluates options like Web API, AI Web API, Server Logic, and Cloud Flows, guiding the user on where to connect data, implement complex business logic, or add generative AI summaries.

This skill provides a comprehensive audit framework for evaluating AI agents used in legal, compliance, or business operations. It assesses the risks associated with delegated authority—such as sending messages, filing documents, or making payments—by checking for adequate controls, logging, revocation mechanisms, and clear accountability chains. It is essential for firms connecting agents to sensitive systems like CRM, court portals, or financial accounts to ensure safe and compliant deployment.

Utilizes a structured Severity x Likelihood matrix to systematically assess and classify legal risks. This framework helps in-house legal teams evaluate contract exposure, assess potential deal risks, and determine appropriate mitigation actions (e.g., Accept, Mitigate, Escalate). It provides a quantifiable method for risk scoring and classification (Low, Medium, High, Critical).

A comprehensive guide for setting up and debugging the fundamental structure of a LÖVE (Love2D) game in Lua. Covers the essential callback loop (love.load, love.update, love.draw), delta-time movement, handling various input types, and managing screen states.

An advanced, AI-assisted tool for generating high-quality multiple-choice questions (MCQs) suitable for various academic levels. It supports both light, gamified quizzes and deep, evaluative assessments with detailed feedback. The system ensures factual accuracy through rigorous document research and allows export to major platforms like Moodle, Kahoot!, and Wooclap.

This assistant specializes in complex cross-border and comparative legal analysis. It helps research legal questions across multiple global jurisdictions and provides a structured framework for evaluating legal risks. It utilizes the Legal Data Hunter MCP to access 13M+ legal documents covering case law, legislation, and doctrine from over 50 countries. The output is flexible, requiring mandatory inline citations for all legal claims, and emphasizes aggressive data quality reporting to flag gaps.

This skill assists in legal workflows by systematically screening Non-Disclosure Agreements (NDAs). It evaluates structural components, confidentiality definitions, acceptable carveouts, and problematic clauses to classify the risk level (GREEN, YELLOW, RED). It guides legal teams on whether an NDA is standard, needs review, or requires full counsel intervention.

Full reference guide for Ouroboros commands, skills, agents, and workflows. Covers Socratic interviews, seed generation, evaluation, evolutionary loops, and troubleshooting for power users.