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Topic: ai

10,359 skills in this topic.

test-skill Use when testing per-client skill rules
agent-sh/agnix 169
deploy-prod Use when deploying to production environment
agent-sh/agnix 169
agnix Use when user asks to 'lint agent configs', 'validate skills', 'check CLAUDE.md', 'validate hooks', 'lint MCP'. Validates agent configuration files against 385 rules across 10+ AI tools.
agent-sh/agnix 169
agnix Use when user asks to 'lint agent configs', 'validate skills', 'check CLAUDE.md', 'validate hooks', 'lint MCP'. Validates agent configuration files against 385 rules.
agent-sh/agnix 169
agent-without-context-skill Use when testing agent without context validation
agent-sh/agnix 169
grid-ctf-ops Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.
greyhaven-ai/autocontext 729
autocontext Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, or discover available scenarios. Provides LLM-based judging with rubric-driven scoring.
greyhaven-ai/autocontext 729
agentfield-multi-reasoner-builder Architect and ship a complete multi-agent backend system on AgentField from a one-line user request. Use when the user asks to build, scaffold, design, or ship an agent system, multi-agent pipeline, reasoner network, AgentField project, financial reviewer, research agent, compliance agent, or any LLM composition that should outperform LangChain/CrewAI/AutoGen — especially when they want a runnable Docker-compose stack and a working curl smoke test.
Agent-Field/agentfield 1,413
agentfield-multi-reasoner-builder Architect and ship a complete multi-agent backend system on AgentField from a one-line user request. Use when the user asks to build, scaffold, design, or ship an agent system, multi-agent pipeline, reasoner network, AgentField project, financial reviewer, research agent, compliance agent, or any LLM composition that should outperform LangChain/CrewAI/AutoGen — especially when they want a runnable Docker-compose stack and a working curl smoke test.
Agent-Field/agentfield 1,413
nanoresearch-experiment Generate a Python code skeleton from an experiment blueprint
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
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
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
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
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
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
unsloth Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
OpenRaiser/NanoResearch 689