Skill UI

Manages complex, iterative background jobs (Ralph loop) for continuous process evolution. It automatically runs multiple `evolve_step` generations until predefined stopping criteria are met (e.g., QA pass, convergence, or max generations). Ideal for complex AI generation pipelines requiring robust, non-stop processing.

This meta-skill automates the entire short-drama production pipeline, starting from a simple topic. The workflow analyzes the request to infer render style, character identity, and shot count. It first drafts a detailed, shot-by-shot shooting script and pauses for user review. After approval, it generates a universal cast reference image, per-shot composition images, and corresponding video clips. Crucially, it maintains character and scene consistency by anchoring every video to both the reference image and the composition image. Finally, it adds title/ending cards, burns subtitles, and de...

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.

Orchestrate AI/ML workflows including data ingestion, model training, batch inference, and RAG indexing using tools like Prefect, Airflow, or Dagster. Essential for managing complex AI systems and automating ML pipelines.

Manage Azure Machine Learning resources including workspaces, data assets, models, compute clusters, jobs, pipelines, and environments using the Azure ML SDK v2 for Python. Ideal for end-to-end ML workflow automation.

A comprehensive toolkit for building production-grade AI agents focused on crypto business development. This skill guides users through the entire token listing lifecycle: autonomously discovering promising assets across multiple chains, applying a weighted 100-point scoring model, verifying safety via advanced wallet forensics, and managing outreach pipelines with necessary human oversight and LLM cascade patterns.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

Generate workflow JSON files for chaining AI models. This skill provides guidance and patterns for creating structured workflows that link multiple AI models in sequence, enabling complex multi-step AI pipelines.

Master the LangChain framework to build sophisticated LLM applications. Learn to implement autonomous agents, complex chains, memory management, and tool integration. Covers document processing pipelines, callbacks, and production-grade architecture patterns like RAG for enterprise AI solutions.

Design and implement complete machine learning pipelines using multi-agent orchestration following modern MLOps best practices. Covers data engineering, feature stores, model training, and production deployment. Ideal for building scalable, monitored, and reproducible ML systems in production environments.

Provides comprehensive guidance for building end-to-end MLOps pipelines, covering data preparation, training, validation, deployment, and monitoring with DAG orchestration tools like Airflow, Dagster, and Kubeflow.