Orchestra Research

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize a paper and its code into a research package
• Link research claims to tables, figures, and verification plans
• Preserve failed approaches and research decisions

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improve an existing agent against benchmark tasks
• Build evaluations for domain-specific tasks
• Refine skills and memory from failure feedback

Build image conversations with LLaVA, a model that connects image recognition with language. This skill guides setup, image descriptions, visual questions, and follow-up conversations, with examples for Python, command-line use, and a Gradio interface.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chatbot that answers questions about images
• Write detailed descriptions of photos
• Ask follow-up questions about the same image

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning sections and page budgets for a systems paper
• Explaining design choices and alternatives
• Organizing evaluation experiments and results

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting research, writing, and editing agents
• Coordinating specialist agents through a manager
• Branching workflows based on analysis results

Track machine learning experiments with SwanLab by recording training settings, metrics, and media such as images and audio. Learn to compare runs, inspect training charts, and connect existing training code to local, cloud, or self-hosted dashboards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing experiments with different training settings
• Viewing training metrics in a local dashboard
• Logging sample images and audio alongside metrics

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Guides LLM compression through pruning, which removes selected model weights. Covers Wanda and SparseGPT methods without retraining, accuracy comparisons, and structured weight patterns that can accelerate inference on compatible hardware.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Trying model compression without retraining
• Comparing accuracy before and after pruning
• Choosing between Wanda and SparseGPT

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Record the reasons behind research decisions
• Preserve failed experiments and their lessons
• Distinguish user decisions from AI suggestions

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tune OpenPI models on custom robot datasets
• Convert JAX checkpoints to PyTorch
• Serve policies for ALOHA, DROID, or LIBERO

Turn research code and experimental results into ML and AI paper drafts. Clarify the main contribution, connect claims to evidence, find and verify references, and prepare conference submissions using LaTeX templates and review checklists.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting a paper from research code and results
• Revising the abstract and introduction around a clear contribution
• Finding related work and verifying references

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting objects with transparent backgrounds
• Building annotation tools with point and box selection
• Generating object masks automatically

Create conference slides from a research paper in Beamer PDF and editable PPTX formats. Adapt the outline to the talk length, select key figures, and add speaker notes with timing and transitions, plus an optional talk script.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a paper into slides for an oral presentation
• Preparing a short spotlight or poster talk
• Creating speaker notes and a timed talk script

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Review Agent-Native Research Artifacts, structured research packages for agents, across six dimensions of research quality. Produce a JSON report with scores, strengths, severity-ranked findings, practical improvements, and an overall acceptance or rejection recommendation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an ARA before publication
• Checking whether evidence supports research claims
• Assessing experimental methods and research records

Train Mixture of Experts models, which activate selected specialist networks for each input. The skill guides architecture, input routing, workload balancing, and GPU distribution using DeepSpeed or HuggingFace, with examples for training configuration and inference optimization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a model that activates only selected expert networks
• Balancing workloads across expert networks
• Distributing experts across multiple GPUs

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording experiment settings and evaluation results
• Comparing model performance across experiments
• Managing model versions for production use

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combine math, coding, and conversation capabilities
• Compare model variants with different merge weights
• Choose a method for merging several models

Guides building AI applications that search documents before answering questions, an approach called RAG. Covers importing files and web data, creating searchable indexes, adding conversational interfaces, and evaluating whether answers match the retrieved material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a question-answering system for internal documents
• Create a chatbot that remembers conversation context
• Search information across files, web pages, and databases