# orchestra-research/ai-research-skills

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3,641 stars · 293 forks · TeX · mit

## Links

- GitHub: https://github.com/Orchestra-Research/AI-Research-SKILLs
- Homepage: http://orchestra-research.com
- awesome-repositories: https://awesome-repositories.com/repository/orchestra-research-ai-research-skills.md

## Topics

`ai` `ai-research` `claude` `claude-code` `claude-skills` `codex` `gemini` `gpt-5` `grpo` `huggingface` `machine-leanring` `megatron` `skills` `vllm`

## Description

This project is an LLM research orchestrator and autonomous AI agent framework designed to automate the scientific lifecycle. It functions as an end-to-end research pipeline and model training toolkit, managing everything from initial literature reviews and hypothesis testing to the final drafting of academic papers.

The system is distinguished by its ability to convert unstructured academic PDFs into machine-executable knowledge layers, allowing agents to reproduce and extend research findings. It employs a two-loop orchestration architecture and a specialized research engineering skill library to guide autonomous agents through complex scientific workflows.

The platform covers a broad set of capabilities including distributed model training, parameter-efficient fine-tuning, and automated GPU infrastructure provisioning. It provides tools for mechanistic interpretability, research rigor review, and the generation of publication-ready visualizations and analysis reports.

The project is implemented primarily using TeX for academic document generation.

## Tags

### Artificial Intelligence & ML

- [Automated Research Lifecycles](https://awesome-repositories.com/f/artificial-intelligence-ml/automated-research-lifecycles.md) — The project automates the research lifecycle from literature review to manuscript writing to move from question to publication. ([source](http://orchestra-research.com))
- [Autonomous Agent Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-agent-orchestration.md) — Provides a framework for deploying modular agents with persistent memory to automate complex research workflows. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Agent Orchestration Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-orchestration-loops.md) — Employs a two-loop architecture with high-level strategic planning and low-level tactical execution for autonomous research.
- [Role-Based Agent Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/integration-deployment/agent-frameworks/agent-orchestrators/role-based-agent-orchestration.md) — Coordinates specialized AI agents with distinct personas and data connectors to execute complex research workflows.
- [Autonomous AI Agent Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-ai-agent-frameworks.md) — Implements a framework for building self-directed multi-agent systems that execute complex scientific research tasks.
- [Autonomous Research Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-research-frameworks.md) — Manages the full AI research lifecycle from literature survey to paper writing using a two-loop architecture. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Language Model Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-architectures.md) — Deploys diverse language model architectures including transformers and state-space models. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-fine-tuning.md) — Implements memory-efficient training methods and reinforcement learning to optimize large language model weights.
- [Model Training Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-toolkits.md) — Ships a comprehensive toolkit for pretraining, fine-tuning, and aligning large-scale neural network models.
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Optimizes model weights using parameter-efficient fine-tuning methods and no-code interfaces. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Research Artifact Compilation](https://awesome-repositories.com/f/artificial-intelligence-ml/research-artifact-compilation.md) — Converts unstructured academic papers into structured data formats containing formal claims and reproducibility specifications.
- [Research Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/research-orchestration.md) — Coordinates a multi-stage pipeline for literature review, hypothesis generation, experimentation, and academic drafting.
- [Automated Research Paper Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/research-papers/automated-research-paper-analysis.md) — Transforms narrative PDF research papers into structured knowledge packages for analysis and reproduction. ([source](http://orchestra-research.com/publications))
- [Executable Knowledge Conversion](https://awesome-repositories.com/f/artificial-intelligence-ml/research-papers/automated-research-paper-analysis/executable-knowledge-conversion.md) — Restructures academic papers into formats that allow agents to understand, reproduce, and extend findings. ([source](http://orchestra-research.com/perspectives))
- [Academic Paper Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/academic-paper-generators.md) — Automatically synthesizes experimental data and literature to produce formal academic manuscripts and figures. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [AI Safety Guardrails](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-safety-guardrails.md) — Implements safety classifiers, programmable guardrails, and prompt injection detectors to align model outputs. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Scientific Experiment Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-task-execution/scientific-experiment-execution.md) — Executes scientific code autonomously within isolated environments to validate research hypotheses. ([source](http://orchestra-research.com/perspectives/introducing-new-orchestra))
- [Text Dataset Curators](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-management/evaluation-datasets/dataset-curation/text-dataset-curators.md) — Curates large-scale text datasets using distributed streaming execution and GPU-accelerated deduplication. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Experiment Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/experiment-tracking.md) — Offers tools for logging, versioning, and visualizing training and evaluation workflows to validate research findings.
- [Experiment Tracking Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/experiment-tracking-systems.md) — Provides a system for logging metrics, monitoring training progress, and managing distributed GPU configurations.
- [Data Analysis Reports](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/generative-text-inference/iterative-refinement-generation/research-report-drafting/structured-research-reports/data-analysis-reports.md) — Produces comprehensive reports featuring statistical analysis and comparison tables from experimental data. ([source](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra))
- [Multi-Node Training Scaling](https://awesome-repositories.com/f/artificial-intelligence-ml/gpu-model-deployments/multi-node-training-scaling.md) — Coordinates large-scale model training across multiple GPU clusters using sharded data parallelism.
- [High-Throughput Text Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/inference-runtimes/high-performance-ai-inference/high-throughput-text-inference.md) — Implements optimized model execution for high-volume text generation with low latency. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-training.md) — Scales model training across multiple compute nodes using sharded data parallelism. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Model Interpretability](https://awesome-repositories.com/f/artificial-intelligence-ml/model-interpretability.md) — Performs mechanistic interpretability using activation caching and causal interventions to discover model features. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Model Performance Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/profiling-and-benchmarking/model-performance-optimization.md) — Reduces memory usage and increases inference speed via quantization, attention optimizations, and weight compression. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Model Training Monitoring](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-monitoring.md) — Tracks training metrics in real time to detect performance plateaus and predict success. ([source](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra))
- [Output Constraint Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-engineering/output-constraint-engines.md) — Provides tools to constrain model outputs using schemas, grammars, and validation rules. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Reinforcement Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-training.md) — Executes post-training pipelines using preference optimization and hybrid flow frameworks. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Ideation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/research-agent-frameworks/ideation-frameworks.md) — Uses structured ideation and cognitive science frameworks to discover novel research goals. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Rigor Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/research-papers/automated-research-paper-analysis/rigor-analysis.md) — Evaluates research claims through formal coherence and falsifiability scoring to ensure epistemic integrity. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Research Reproductions](https://awesome-repositories.com/f/artificial-intelligence-ml/research-papers/research-reproductions.md) — Validates existing research by provisioning GPUs and comparing results across ML tasks. ([source](http://orchestra-research.com/perspectives/ai-research-skills))
- [Publication-Ready Comparison Charts](https://awesome-repositories.com/f/artificial-intelligence-ml/side-by-side-preference-ranking/publication-ready-comparison-charts.md) — Generates publication-ready comparison charts, loss curves, and correctness trends. ([source](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra))
- [Training Logic Debuggers](https://awesome-repositories.com/f/artificial-intelligence-ml/training-logic-debuggers.md) — Executes small-scale GPU test runs to identify bugs and reward function failures before committing to full-scale experiments. ([source](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra))
- [Training Script Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/training-script-generators.md) — Produces complete training scripts featuring experiment tracking, checkpointing, and evaluation loops for various model configurations. ([source](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra))

### Part of an Awesome List

- [Scientific Research Agents](https://awesome-repositories.com/f/awesome-lists/ai/scientific-research-agents.md) — Provides a framework for specialized agents that automate the entire scientific research cycle, including experimentation and academic writing. ([source](http://orchestra-research.com/publications))
- [Model Evaluation and Benchmarking](https://awesome-repositories.com/f/awesome-lists/ai/model-evaluation-and-benchmarking.md) — Assesses model performance using standardized task harnesses and code-specific evaluation metrics. ([source](https://cdn.jsdelivr.net/gh/orchestra-research/ai-research-skills@main/README.md))
- [Autonomous Research Agents](https://awesome-repositories.com/f/awesome-lists/ai/autonomous-research-agents.md) — Library of skills for managing two-loop autonomous research architectures.

### Education & Learning Resources

- [Full-Lifecycle Research Pipelines](https://awesome-repositories.com/f/education-learning-resources/research-workflow-automation/full-lifecycle-research-pipelines.md) — Provides an end-to-end pipeline that automates the full academic lifecycle from idea generation to final paper production.
- [Research Recipe Libraries](https://awesome-repositories.com/f/education-learning-resources/research-workflow-automation/research-recipe-libraries.md) — Supplies a library of modularized knowledge and experiment patterns to guide agents through the research lifecycle. ([source](http://orchestra-research.com/perspectives))

### Content Management & Publishing

- [Reproducibility Specification Layers](https://awesome-repositories.com/f/content-management-publishing/documentation-knowledge-management/knowledge-bases/execution-knowledge-stores/reproducibility-specification-layers.md) — Restructures research papers into machine-native layers containing claims and reproducibility specifications. ([source](http://orchestra-research.com/ara))
- [Research Presentation Generation](https://awesome-repositories.com/f/content-management-publishing/media-management/media-automation-tools/document-generation/research-presentation-generation.md) — Generates comprehensive analysis presentations to communicate experimental findings. ([source](https://www.orchestra-research.com/perspectives/ai-research-skills))

### Scientific & Mathematical Computing

- [Literature Querying Systems](https://awesome-repositories.com/f/scientific-mathematical-computing/automated-literature-reviewers/gap-identifiers/literature-querying-systems.md) — Processes dense academic papers to answer probing questions and identify knowledge gaps within a field. ([source](http://orchestra-research.com/perspectives/introducing-new-orchestra))
- [Research and Analysis Tools](https://awesome-repositories.com/f/scientific-mathematical-computing/research-analysis-workflows/research-and-data-analysis-tools/research-and-analysis-tools.md) — Creates publication-ready plots and automated presentations to communicate scientific findings. ([source](http://orchestra-research.com/perspectives/ai-research-skills))

### Development Tools & Productivity

- [GPU Environment Automation](https://awesome-repositories.com/f/development-tools-productivity/development-environment-setup/infrastructure-service-automation/gpu-environment-automation.md) — Automates the setup of GPU resources, including containers and dependency installation for research environments. ([source](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra))

### DevOps & Infrastructure

- [GPU Provisioning Services](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure/cloud-computing-serverless/gpu-provisioning-services.md) — Automates the deployment of containerized compute resources and GPUs across multiple cloud providers with spot recovery.

### Software Engineering & Architecture

- [Parallel LLM Execution](https://awesome-repositories.com/f/software-engineering-architecture/concurrent-task-execution/parallel-llm-execution.md) — Runs multiple training configurations simultaneously and logs real-time metrics through a dedicated SDK. ([source](https://www.orchestra-research.com/perspectives/LLM-with-Orchestra))