Sponsored by

Find leads on Reddit on auto pilot

Topic: metaclaw

64 skills in this topic.

pytorch-training Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
aiming-lab/AutoResearchClaw 11,027
scientific-writing Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
aiming-lab/AutoResearchClaw 11,027
experimental-design Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
aiming-lab/AutoResearchClaw 11,027
biology-biopython Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
aiming-lab/AutoResearchClaw 11,027
chemistry-rdkit Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.
aiming-lab/AutoResearchClaw 11,027
chemistry-rdkit Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.
aiming-lab/AutoResearchClaw 11,027
scientific-writing Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
aiming-lab/AutoResearchClaw 11,027
cv-detection Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
aiming-lab/AutoResearchClaw 11,027
hypothesis-formulation Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
aiming-lab/AutoResearchClaw 11,027
statistical-reporting Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
aiming-lab/AutoResearchClaw 11,027
mixed-precision Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
aiming-lab/AutoResearchClaw 11,027
rl-policy-optimization Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
aiming-lab/AutoResearchClaw 11,027
researchclaw Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
aiming-lab/AutoResearchClaw 11,027
data-loading Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
aiming-lab/AutoResearchClaw 11,027
statistical-reporting Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
aiming-lab/AutoResearchClaw 11,027
nlp-alignment Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
aiming-lab/AutoResearchClaw 11,027
literature-search Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
aiming-lab/AutoResearchClaw 11,027
systematic-review Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
aiming-lab/AutoResearchClaw 11,027
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
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
a-evolve Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on: "evolve", "self-improve", "diagnose failures", "generate skills from errors", "what went wrong and how to fix it", or any mention of A-Evolve.
aiming-lab/AutoResearchClaw 11,027
literature-search Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
aiming-lab/AutoResearchClaw 11,027
biology-biopython Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
aiming-lab/AutoResearchClaw 11,027
nlp-pretraining Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
aiming-lab/AutoResearchClaw 11,027