Evaluate NVIDIA Cosmos Policy for robot manipulation in LIBERO and RoboCasa simulations. Follow GPU environment setup, configure rendering without a display, run small checks before larger benchmarks, inspect success rates, and profile inference latency.
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Explore computer science and AI research directions using eight creativity frameworks. Combine ideas across fields, question hidden assumptions, and transfer mechanisms from other disciplines to develop research questions with testable predictions.
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Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.
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Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.
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Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.
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Combine specialized language models without retraining using mergekit. The skill explains how to choose merging methods, configure each model’s contribution, compare resulting variants, and prepare a merged model for evaluation and deployment.
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Document how a research or coding session evolved after the work is complete. The skill extracts decisions, experiments, failed approaches, and claims into ara/ files, distinguishing user statements from AI suggestions and actions.
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Turn recorded audio and video into text or timestamped subtitles with Whisper. This skill covers transcription across 99 languages, language detection, translation into English, batch processing, and model selection for available computing resources.
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Plan systems conference papers with section page budgets, paragraph outlines, and writing patterns. Organize design choices, experimental evidence, and related work into a clear argument, then review the draft with submission checklists.
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Turn papers, code, experiment logs, and notes into an ARA, a structured research package for AI agents. Organizes claims, evidence, verification plans, code stubs, and research decisions, then checks coverage and consistency across the generated files.
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Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
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Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.
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Manage machine learning experiments with MLflow by recording settings, metrics, files, and trained models. Covers experiment comparison, model version management, reproducible project configurations, and deployment workflows across local and cloud environments.
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Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.
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Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.
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Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.
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Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Guides logging for PyTorch and TensorFlow, including weight distributions and performance profiling to investigate training problems and processing bottlenecks.
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Build AI agents by connecting workflow blocks in AutoGPT's visual editor. This skill covers multi-step automation, scheduled runs, webhooks that start work after external events, execution monitoring, and custom agent development.
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Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.
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Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.
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Guides adapting OpenPI robot models to custom datasets and serving them to produce actions from observations. Covers JAX and PyTorch training, checkpoint conversion, and troubleshooting for normalization statistics and GPU memory.
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Create masks that identify object regions in images using points, boxes, or existing masks. The skill guides automatic mask generation, transparent-background extraction, annotation workflows, and deployment, without requiring training for each specific task.
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Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.
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Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill guides model selection, batch processing, and memory optimization, with examples for understanding images without training for each specific task.
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