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Agent skills
ai-agents

Topic: ai-agents

18,135 skills in this topic.

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
nanoresearch-writing Draft a LaTeX research paper from all previous stage outputs
OpenRaiser/NanoResearch 689
nanoresearch-planning Produce an experiment blueprint from a research hypothesis
OpenRaiser/NanoResearch 689
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
nanoresearch-experiment Generate a Python code skeleton from an experiment blueprint
OpenRaiser/NanoResearch 689
nanoresearch-ideation Search academic literature and generate research hypotheses
OpenRaiser/NanoResearch 689
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
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
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
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
unsloth Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
OpenRaiser/NanoResearch 689
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
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
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
happier-testing Repo-specific TDD and test-validation workflow for Happier changes, with lane selection, fixture policy, and anti-flake guardrails.
happier-dev/happier 649
happier-session-control Manage Happier sessions (list/status/send/wait/history/stop + execution runs) via the happier CLI JSON contract.
happier-dev/happier 649
happier-github-ops Run GitHub CLI commands as the Happier bot account via `yarn ghops` (forced PAT auth + non-interactive).
happier-dev/happier 649
happier-session-control Manage Happier sessions (list/status/send/wait/history/stop + execution runs) via the happier CLI JSON contract.
happier-dev/happier 649
advanced-evaluation This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
guanyang/antigravity-skills 617
brand-guidelines Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
guanyang/antigravity-skills 617
canvas-design Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
guanyang/antigravity-skills 617
claude-api
guanyang/antigravity-skills 617
vercel-composition-patterns React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
guanyang/antigravity-skills 617
context-degradation This skill should be used when the user asks to "diagnose context problems", "fix lost-in-middle issues", "debug agent failures", "understand context poisoning", or mentions context degradation, attention patterns, context clash, context confusion, or agent performance degradation. Provides patterns for recognizing and mitigating context failures.
guanyang/antigravity-skills 617