ReddRadar
Topic: autonomous-research
124 skills in this topic.
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hypothesis-formulation
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
aiming-lab/AutoResearchClaw 11,027
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scientific-visualization
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
aiming-lab/AutoResearchClaw 11,027
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mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
aiming-lab/AutoResearchClaw 11,027
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systematic-review
Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
aiming-lab/AutoResearchClaw 11,027
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skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
OpenRaiser/NanoResearch 689
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unsloth
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
OpenRaiser/NanoResearch 689
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creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
OpenRaiser/NanoResearch 689
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ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
OpenRaiser/NanoResearch 689
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brainstorming-research-ideas
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
OpenRaiser/NanoResearch 689
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evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
OpenRaiser/NanoResearch 689
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nanoresearch-writing
Draft a LaTeX research paper from all previous stage outputs
OpenRaiser/NanoResearch 689
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peft-fine-tuning
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
OpenRaiser/NanoResearch 689
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ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
OpenRaiser/NanoResearch 689
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ml-paper-writing
Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
OpenRaiser/NanoResearch 689
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nanoresearch-experiment
Generate a Python code skeleton from an experiment blueprint
OpenRaiser/NanoResearch 689
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huggingface-accelerate
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
OpenRaiser/NanoResearch 689
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academic-plotting
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
OpenRaiser/NanoResearch 689
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autoresearch
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
OpenRaiser/NanoResearch 689
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nanoresearch-ideation
Search academic literature and generate research hypotheses
OpenRaiser/NanoResearch 689
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nanoresearch-planning
Produce an experiment blueprint from a research hypothesis
OpenRaiser/NanoResearch 689
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sibyl-empiricist
Sibyl 实验主义者 agent - 关注可复现性、数据质量和实验设计严谨性
Sibyl-Research-Team/AutoResearch-SibylSystem 225
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sibyl-rebuttal-advocate
Sibyl Rebuttal 辩护者 agent - 穷尽角度为论文辩护
Sibyl-Research-Team/AutoResearch-SibylSystem 225
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sibyl-rebuttal-writer
Sibyl Rebuttal 写手 agent - 撰写正式 rebuttal 回复
Sibyl-Research-Team/AutoResearch-SibylSystem 225
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sibyl-server-experimenter
Sibyl 服务器端实验 agent - 在远程服务器上使用 Codex/Claude 本地执行实验
Sibyl-Research-Team/AutoResearch-SibylSystem 225