AI &amp; ML skills

vaex

k-dense-ai/scientific-agent-skills

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

prompt-engineering

neolabhq/context-engineering-kit

Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.

zarr-python

k-dense-ai/scientific-agent-skills

Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

build-agents

vercel/vercel-plugin

Default guidance for building AI agents. Use for generic requests to build, create, scaffold, design, architect, or implement an AI agent, agent app, tool-calling agent, durable agent, multi-agent system, or scheduled agent.

pysam

k-dense-ai/scientific-agent-skills

Python/HTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.

gtars

k-dense-ai/scientific-agent-skills

Use Gtars for local genomic interval models and set algebra, overlaps and counts, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and the CLI.

lamindb

k-dense-ai/scientific-agent-skills

Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.

polars-bio

k-dense-ai/scientific-agent-skills

High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.

llm-council

am-will/codex-skills

Agent Browser

am-will/codex-skills

A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.

agentic-jujutsu

ruvnet/ruflo

Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

ai-engineer

sickn33/agentic-awesome-skills

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

planning-with-files

zhaono1/agent-playbook

Uses persistent markdown files for general planning, progress tracking, and knowledge storage (Manus-style workflow). Use for multi-step tasks, research projects, or general organization WITHOUT mentioning PRD. For PRD-specific work, use prd-planner skill instead.

earnings-analysis

anthropics/financial-services-plugins

Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.

jetson-diagnostic

nvidia/skills

Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

msgraph

merill/msgraph

Up-to-date Microsoft Graph API knowledge for AI agents. Search 27,700+ Graph APIs, endpoint docs, resource schemas, and community samples — all locally, no network calls. Use when the agent needs to find, understand, or call Microsoft Graph endpoints.

bankr

bankrbot/skills

AI-powered crypto trading agent, wallet API, and LLM gateway via natural language. Use when the user wants to trade crypto, trade tokenized stocks and ETFs (spot or leveraged), check portfolio balances (with PnL and NFTs), view token prices, search tokens, research token holders, transfer crypto, manage NFTs, use leverage (Hyperliquid or Avantis), bet on Polymarket, deploy tokens, set up automated trading, sign and submit raw transactions, call or deploy x402 paid API endpoints, browse the web, store and query files on their wallet's filesystem, or access LLM models through the Bankr LLM gateway funded by your Bankr wallet — including zero-data-retention and TEE-private inference tiers. Supports Base, Ethereum, Polygon, Solana, Unichain, World Chain, Arbitrum, BNB Chain, and Robinhood Chain.

AgentDB Vector Search

ruvnet/ruflo

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

ReasoningBank with AgentDB

ruvnet/ruflo

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

quant-analyst

404kidwiz/claude-supercode-skills

Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.

AgentDB Memory Patterns

ruvnet/ruflo

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

nemoclaw-user-guide

nvidia/skills

Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.

jetson-llm-serve

nvidia/skills

Stand up vLLM or SGLang serving on Jetson, using upstream vLLM on Thor and Orin JetPack 7.2+, and NVIDIA-AI-IOT vLLM on older Orin.

keyword-research

aaron-he-zhu/aaron-marketing-skills

Use when the user asks to "find keywords", "挖词", or "搜什么词"; prioritizes search volume, keyword difficulty, intent, and topic clusters from provided or connected data. Not for competitor-relative coverage gaps — use content-gap-analysis. 关键词研究/内容选题

jetson-inference-mem-tune

nvidia/skills

Pick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.

storage-format

tursodatabase/turso

SQLite file format, B-trees, pages, cells, overflow, freelist that is used in tursodb

n8n-agents

czlonkowski/n8n-skills

Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.

jetson-llm-benchmark

nvidia/skills

Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.

jetson-package

nvidia/skills

Pick Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes; maps Orin SM 8.7 vs Thor SM 11.0 and JetPack-specific package choices.

finetuning-method-selection

wshobson/agents

Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.

generative-engine-optimization

kostja94/marketing-skills

When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For third-party publishing strategy (which platforms to use), use parasite-seo. For GitHub repos, README, and Awesome lists, use github. For Medium.com only, use medium-posts. For Grokipedia edits, use grokipedia-recommendations. For traditional Google SERP strategy, use seo-strategy.

n8n-agents-official

n8n-io/skills

Use when building or editing any AI feature in n8n: AI Agents, Text Classifier, Information Extractor, Sentiment Analysis, Summarization Chain, Basic LLM Chain, embeddings, vector stores, single one-shot LLM calls, or AI media generation (image / audio / video) via the native LangChain provider nodes. Triggers on any `@n8n/n8n-nodes-langchain.*` node, "agent", "chat assistant", "LLM with tools", "tool calling", "fromAi", "system prompt", "memory window", "structured output", "outputParser", "function calling", "RAG", "vector store", "embeddings", "classify with AI", "extract fields with LLM", "sentiment analysis", "summarize with LLM", "single LLM call", chat triggers with files, AI image / video / audio generation, or any multi-turn or one-shot LLM behavior.

phoenix-evals

github/awesome-copilot

Build and run evaluators for AI/LLM applications using Phoenix.

jetson-speculative-decoding

nvidia/skills

Add EAGLE-3 or draft-model speculative decoding to a Jetson vLLM server when TPOT is the bottleneck.

mapbox-mcp-runtime-patterns

mapbox/mapbox-agent-skills

Integration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities.

n8n-binary-and-data

czlonkowski/n8n-skills

Handle files and binary data in n8n correctly. Use when working with files, images, PDFs, attachments, uploads or downloads, base64, vision/multimodal input, or when an AI agent needs a file as tool input or output — and whenever the user mentions $binary, binaryPropertyName, "read the PDF", "attach the file", "send the image", Merge losing binary, or a CDN for chat images. Covers the $binary vs $json split, reading/writing binary, keeping binary alive across transforms with Merge, the agent-tool binary boundary, and the CDN/URL requirement for chat surfaces.

implement-task

neolabhq/context-engineering-kit

Implement a task step by step with automated LLM-as-Judge verification at the end of each phase

spark-environment-setup

wshobson/agents

Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.

do-in-steps

neolabhq/context-engineering-kit

Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Use when later steps depend on the results of earlier ones.

do-in-parallel

neolabhq/context-engineering-kit

Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.

write-tests

neolabhq/context-engineering-kit

Add missing test coverage for your local code changes by generating new test files (covers uncommitted and untracked changes, or the latest commit if everything is committed). Use when you want write tests for new logic or increase test coverage.

terragrunt-generator

akin-ozer/cc-devops-skills

Generate/create/scaffold Terragrunt HCL files — root.hcl, terragrunt.hcl, child modules, stacks, multi-env layouts.

do-and-judge

neolabhq/context-engineering-kit

Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop

karpathy-coder

alirezarezvani/claude-skills

Use when writing, reviewing, or committing code to enforce Karpathy's 4 coding principles — surface assumptions before coding, keep it simple, make surgical changes, define verifiable goals. Triggers on "review my diff", "check complexity", "am I overcomplicating this", "karpathy check", "before I commit", or any code quality concern where the LLM might be overcoding.

qiaomu-opencli-browser

joeseesun/qiaomu-opencli-skills

Make websites accessible for AI agents. Navigate, click, type, extract, wait — using Chrome with existing login sessions. No LLM API key needed.

rag-engineer

sickn33/agentic-awesome-skills

Expert in building Retrieval-Augmented Generation systems. Masters

conversation-memory

davila7/claude-code-templates

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

clean-code

davila7/claude-code-templates

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments