Skill UI

A powerful tool for executing complex, multi-step, and multi-agent workflows defined in YAML format directly within the AI session. It acts as an orchestration engine, simulating various roles (agents) to complete tasks sequentially or in parallel, without requiring external API keys or configuration. Ideal for complex business process automation and multi-role content generation.

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.

A systematic auditor designed to evaluate the resilience and quality of complex AI workflows. It scans across five critical dimensions—Prompt Quality, Context Efficiency, Tool Health, Architecture Fitness, and Safety—providing a scored report and prioritized, actionable remediation steps. Use this tool to find hidden flaws and ensure robustness before deploying any AI agent or system.

This CLI alias serves as the primary interface for the Codex /pua:pro protocol, designed for advanced code operations within the Claude Code environment. It manages complex workflows related to self-evolution, platform telemetry monitoring, key performance indicators (KPIs), and system configuration updates, ensuring robust and consistent protocol adherence.

This skill acts as a comprehensive hybrid router for any research request. It first deterministically classifies the user's intent, delegating the query to specialized modules like Pulse (trends/sentiment), Grants (NIH funding), or LitReview (academic papers). If no specialist matches, it falls back to a robust general search and synthesis workflow. The system ensures routing transparency, provides comprehensive reports, and includes full citations and an audit log.

This skill functions as a governance and coordination layer for multi-vendor AI agents. Instead of static task assignment, it utilizes a learning router that assigns work based on an agent's proven track record and accumulated 'lessons' from completed tasks. Agents function as 'citizens' who can not only contribute but also propose and vote on amendments to the protocol's rules, ensuring continuous, measurable, and adaptive improvement in collaborative workflows.

This advanced AI skill converts raw study materials—such as syllabi, past exam papers, or personal notes—into a structured, prioritized, and difficulty-ranked High Score Roadmap. It covers all key question types (Theory, Numerical, MCQ, Coding, Lab) and optimizes study flow by ordering topics from Easy to Hard. Use it for targeted revision, gap analysis, and predicting high-yield exam questions.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

This skill facilitates professional multilingual dubbing for local audio and video assets. Users can submit source media for translation and dubbing into various target languages and locales. It manages the entire workflow—from job submission and status polling to final artifact download—while enforcing critical checks for source rights, speaker consent, and appropriate regional accents.

Generates a highly structured, machine-parseable shooting script for short videos based on a specified topic, style, and duration. It outputs a detailed shot list including image prompts, video prompts, voiceovers, and on-screen text, making it ideal for automated AI video generation workflows.

This skill provides an offline self-optimization cycle for local AI agents. When triggered, it harvests past interaction sessions, mines recurring tasks and patterns, and re-runs these tasks offline. It then proposes bounded edits to the agent's memory (CLAUDE.md) and skills (SKILL.md). Crucially, all changes pass through a held-out validation gate, ensuring that only improvements are adopted, thereby continuously enhancing the agent's performance for the user's specific workflow without requiring model retraining.

A generic entry point for delegating self-contained, multi-step tasks to specialized LLM agents (Codex, Claude Code, Pi). Utilizes background processing tools to manage the full lifecycle of AI-driven sub-tasks, such as policy review, complex report synthesis, document generation, or large-scale code refactoring. Ideal for meta-skill workflows requiring full agent agency.