Sponsored by

Find leads on Reddit on auto pilot

Agent skills
self-evolving

Topic: self-evolving

161 skills in this topic.

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
meta-analysis Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
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
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
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
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
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
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
pytorch-training Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
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
systematic-review Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
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
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-classification Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
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
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
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
skill-creator Autonomously create, test, and optimize skills by detecting reusable patterns in your own work. Use when you notice repeated tool sequences, recurring code patterns across attempts, or insights that should be captured as a packaged skill. Also use to benchmark and iterate on existing skills.
Human-Agent-Society/CORAL 440
bernstein-quality Show quality metrics for Bernstein runs — success rates per model, lint/test pass rates, completion time distributions. Use when the user asks about quality, reliability, which model performs best, or pass rates.
chernistry/bernstein 104
bernstein-status Show Bernstein orchestrator status — active agents, task progress, costs, and alerts. Use when the user asks about orchestrator status, what agents are doing, task progress, how much has been spent, or what's happening with the build.
chernistry/bernstein 104
bernstein-create-task Create a new task in the Bernstein orchestrator. Use when the user wants to add a task, delegate work to an agent, file a bug fix, or queue up work for the orchestrator to handle.
chernistry/bernstein 104
bernstein-cost Show detailed cost breakdown and budget status for the Bernstein orchestrator. Use when the user asks about spending, budget, cost per model, cost per agent, or wants a cost projection.
chernistry/bernstein 104
bernstein-plan Create and manage multi-step execution plans in Bernstein. Plans decompose complex goals into stages with dependencies. Use when the user wants to plan a complex feature, break down a large task, or review an execution plan before agents start working.
chernistry/bernstein 104