Data & Analysis Skills Hub

A-share finance skills with generous daily usage after registration.

AI-powered dynamic investment research terminal for US stocks.

Pine strategies, AI trend diagnostics, and Chan theory auto-annotation.

Natural-language access to Tiger SDK for market data, trading, and account management.

One-click access to tools, data, and automation skills for crypto tasks.

Open skill ecosystem for trading plans, signal collection, quantitative strategies, and risk management.

Natural-language crypto access for CEX trading, DEX swaps, wallet tracking, and DeFi interactions.

Guotai Haitong official skills with relatively generous quota.

Guosen Securities official finance skills with a 50-call daily limit.

Official and community finance skills from iWenCai, compatible with OpenClaw and related platforms.

Real-time market data, intelligent trading, and push notifications. Account required.

Popular Python financial data API platform for China markets.

Open-source stock market data API.

Longbridge official skill supporting OpenClaw, Claude Code, Cursor, and related agents.

Eastmoney official skill for news, market data, stock screening, watchlists, and portfolio management.

AI-assisted US stock market product.

Gives agents reliable web scraping, search, and browser automation.

AI-ready PageSpeed Insights and Lighthouse CLI for Core Web Vitals audits, performance fixes, SEO optimization, and before/after verification.

One-click AI automation to diagnose, optimize, and fix Google Search Console indexing issues.

Inference-native tokenmaxxing agent harness for loop engineering, with slash-command loops, verification evidence, memory, context control, and routing visibility.

Generate architecture, workflow, sequence, data-flow, and lifecycle diagrams as self-contained HTML with SVG graphics, theme switching, and PNG, JPEG, WebP, or SVG export.

Create editable Draw.io diagrams for architecture, UML, SysML, BPMN, networks, infrastructure, machine-learning models, and mind maps, with local PNG, SVG, PDF, and JPG export.

Source-backed investment-research workflow for finding supply-chain bottlenecks, mapping technology value chains, screening A-share, Hong Kong, and US stocks, and stress-testing theses without executing trades.

Developing Gemini-powered apps on Google Cloud Vertex AI using the Gen AI SDK

Tinybird project guidelines for datasources, pipes, endpoints, and SQL

Tinybird CLI usage guidelines and commands

Tinybird Python SDK usage guidelines

Tinybird TypeScript SDK usage guidelines

Implement Terraform Provider resources and data sources using the Plugin Framework

Discover existing cloud resources and bulk import them into Terraform state

Manage infrastructure across multiple environments, regions, and cloud accounts

Best practices for Neon Serverless Postgres

Claimable Postgres database provisioning with Neon

Optimize Neon Postgres egress and data transfer

Best practices for working with ClickHouse

Drop-in pandas replacement with ClickHouse performance across 16+ data sources

In-process ClickHouse SQL engine for Python — query files, databases, and cloud storage without a server

Design ClickHouse architectures and translate best practices into workload-specific decisions

Deploy to ClickHouse Cloud and migrate from local setups with clickhousectl

Spin up a local ClickHouse development environment from zero with clickhousectl

Balance, usage, and billing analytics endpoints

Build stateful AI agents with scheduling, RPC, and MCP servers

Comprehensive Cloudflare platform skill covering Workers, Pages, storage, AI, networking, security, and IaC

Send transactional email and route inbound mail with Cloudflare Email Sending and Email Routing

Stateful coordination with RPC, SQLite, and WebSockets

Build sandboxed applications for secure, isolated code execution on Workers

Audit Core Web Vitals and render-blocking resources

Review and author Workers code against production best practices and wrangler.jsonc conventions

Deploy and manage Workers, KV, R2, D1, Vectorize, Queues, Workflows

Key-value object storage for files and data

Network requests, API calls, caching, and offline support

Browse and query HF datasets with the Dataset Viewer API

Create and manage datasets with configs and SQL querying

Publish papers on HF Hub with model/dataset links

Search and extract data from Burp Suite project files

Full Sentry SDK setup for Cloudflare Workers, Pages, Durable Objects, Queues, and Workflows

Design well-architected Azure cloud systems

Document text, table, and data extraction

MongoDB Atlas as ARM resources

Azure Cache for Redis provisioning

Full-text, vector, and hybrid search

NoSQL key-value table storage

NLP: sentiment, entities, key phrases

NoSQL key-value table storage

Full-text, vector, and hybrid search

Hierarchical data lake storage

Vector/hybrid search with semantic ranking

Frontend interactivity with data-wp-* directives and stores

Profiling, caching, database optimization, Server-Timing

Deploy apps to Cloudflare using Workers, Pages, and platform services

Deploy applications to Render's cloud platform using Git-backed services

Inspect Sentry issues, summarize production errors, and pull health data

Set up and audit analytics tracking and measurement pipelines

Add and optimize schema markup and structured data for better SEO

Monitor on-chain Smart Money buy/sell signals with price, max gain, and exit rate data on Solana and BSC

Search Brave's news index with article metadata

Deploy browser automation scripts as serverless cloud functions

Query decoded onchain data (events, tx, blocks) on Base

Query Datadog APM data directly from your editor

Analyze production LLM traces and generate evaluators

Root-cause LLM app failures using eval traces

Analyze single or comparative LLM experiment results

Search, filter, and archive Datadog logs through pup CLI

Manage Datadog monitors through the pup CLI

Rust-based CLI (pup) for talking to the Datadog API

Build Firebase Data Connect backends backed by Cloud SQL

Complete guide for Cloud Firestore Standard Edition

Implement offline-first caching strategies

Build a structured data layer using SQLite

Project market size with real-world data and citations

Analyze A/B test results with statistical significance and recommendations

Cohort retention curves, feature adoption, and segment insights

Generate SQL queries from natural language across major dialects

Generate realistic dummy datasets in CSV, JSON, or SQL

Segment users by behavior, JTBD, and needs from feedback data

Backend architecture with REST API design, auth flows, real-time features, and database integration

Attach a DuckDB database file for interactive querying with automatic schema exploration

Run SQL queries against attached databases or ad-hoc against files using Friendly SQL dialect

Read any data file (CSV, JSON, Parquet, Avro, Excel, spatial) locally or from remote storage

Search DuckDB and DuckLake documentation using full-text search over HTTPS

Search past Claude Code session logs to recover context from previous conversations

Install or update DuckDB CLI and extensions with version management

Eng Manager review: lock in architecture, data flow, diagrams, edge cases, and tests

Systematic root-cause debugging: no fixes without investigation, traces data flow, tests hypotheses

Search engine optimization, crawlability, and structured data

Set up the MongoDB MCP server with authentication and connection configuration

Optimize MongoDB client connection pools, timeouts, and serverless patterns

Design efficient document schemas with validation and indexing patterns

Build, operate, and debug Atlas Stream Processing pipelines with Kafka, S3, and Lambda integrations

Translate natural language into MongoDB queries and aggregation pipelines

Analyze and optimize query performance using Atlas Performance Advisor

Implement Atlas Search and AI-powered recommendations with vector search

Redis development best practices — data structures, query engine, vector search, caching, and performance optimization.

CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.

Use when writing DALI data loading or preprocessing code with `nvidia.dali.experimental.dynamic` (ndd), or when converting DALI pipeline-mode code to dynamic mode, or when the user...

Guide for adding support for new LLM or VLM models in Megatron-Bridge.

Dev environment setup for Megatron Bridge — container-based development, uv package management, lockfile regeneration, adding dependencies, Slurm container usage, and common build...

Bump a pinned dependency (TransformerEngine, Megatron-LM, NRX, etc.), regenerate the lockfile, open a PR, and drive it to green by attaching a watchdog to the "CICD NeMo" workflow...

CI/CD reference for Megatron Bridge — pipeline structure, commit and PR workflow, CI failure investigation, and common failure patterns.

Code style and quality rules for Megatron Bridge — ruff configuration, naming conventions, type hints, mypy rules, docstrings, copyright headers, logging, and the code review check...

Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data.

Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures.

External NeMo-RL end-to-end validation workflow for Megatron-Bridge model/provider changes, including downstream compatibility checks, external RL lifecycle behavior, Megatron poli...

Structured framework for verifying numerical parity of HFMCore weight conversions.

Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute.

Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.

Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as...

Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.

MoE expert-parallel communication overlap in Megatron Bridge.

Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage.

Representative MoE training playbooks by hardware platform and model family.

Long-context MoE training guidance for Megatron Bridge.

Systematic workflow for MoE training optimization in Megatron Bridge, based on the Megatron-Core MoE paper.

Practical guidance for training MoE VLMs in Megatron Bridge.

Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.

Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP...

Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Recommend and customize Megatron Bridge recipes for a user's model, GPU count, and training goal.

Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

Testing reference for Megatron Bridge — unit and functional test layout, tier semantics (L0/L1/L2/flaky), script conventions, running tests locally, adding/moving/disabling tests,...

External verl end-to-end validation workflow for Megatron-Bridge model/provider changes.

Container-based dev environment setup and dependency management for Megatron-LM.

Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:-py3`) used by Megatron-LM CI.

CI/CD reference for Megatron-LM.

Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.

Linting and formatting for Megatron-LM.

Domain knowledge for the nightly main-to-dev sync workflow.

Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.

Research and draft a response to a GitHub issue or question from an external contributor.

How to launch distributed Megatron-LM training jobs on a SLURM cluster.

Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.

Test system for Megatron-LM.

Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), score the change with average normalized relative differences, and produce a PR-ready summary.

Query and browse evaluation results stored in MLflow.

Run commands inside a remote Docker container via the file-based command relay (tools/debugger).

Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM.

Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL).

Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher.

Monitor submitted jobs (PTQ, evaluation, deployment) on SLURM clusters.

This skill should be used when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AW...

Cherry-pick merged PRs labeled for a release branch into that branch, then open a PR and apply the cherry-pick-done label.

Create custom LLM evaluation benchmarks using the BYOB decorator framework.

Query and browse evaluation results stored in MLflow.

Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher.

Interactive config wizard for NeMo Evaluator Launcher (NEL).

> Guide for adding a new benchmark or training environment to NeMo-Gym.

>- Use when debugging a Nemo Gym run or reward profiling job.

> Maintain the NeMo Gym Fern docs site — add, update, move, or remove pages under fern/.

>- Use when creating, validating, or documenting Nemo Gym pivot datasets from rollout, trajectory, chat-completion, Responses API, or tool-call artifacts.

>- Use to help users get started with Nemo Gym reward profiling.

Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery.

Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets.

Build and dependency management for NeMo-RL.

CI/CD reference for NeMo-RL.

Configuration conventions for NeMo-RL.

Contribution conventions for NeMo-RL.

NVIDIA copyright header requirements for NeMo-RL.

Documentation conventions for NeMo-RL.

Error handling guidelines for NeMo-RL.

Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI.

Code style guidelines for NeMo-RL (Python and shell).

Interactive code review for NVIDIA-NeMo/RL pull requests.

Manage durable working-session memory for coding agents.

Testing conventions for NeMo-RL.

Create GitHub pull requests that follow the NemoClaw PR template.

Scan recent git commits for changes that affect user-facing behavior, then draft or update the corresponding documentation pages and refresh generated user skills for release prep.

Scans other open issues to find ones a given PR may also fix or accidentally break.

Cut a new semver release — bump all version strings via bump-version.ts, open a release PR, and after merge tag main and push.

Runs the daytime maintainer loop for NemoClaw, prioritizing items labeled with the current version target.

Runs the end-of-day maintainer handoff for NemoClaw.

Finds open GitHub PRs with security and priority-high labels, links each to its issue, detects duplicates (multiple PRs fixing the same issue), and presents a table of review candi...

Runs the morning maintainer standup for NemoClaw.

Normalizes GitHub issue and PR titles by removing any bracketed [NemoClaw] tag case-insensitively, even when the tag appears later in the title.

Compares competing PRs that target the same issue and recommends which one to merge.

Performs a comprehensive security review of code changes in a GitHub PR or issue.

AI-assisted label triage for NVIDIA/NemoClaw issues and PRs.

Start here.

Describes the agent skills shipped with NemoClaw and how to access them by cloning the repository.

Connects NemoClaw to a local inference server.

Presents a risk framework for every configurable security control in NemoClaw.

Explains how to run NemoClaw on a remote GPU instance, including the deprecated Brev compatibility path and the preferred installer plus onboard flow.

Installs NemoClaw, launches a sandbox, and runs the first agent prompt.

Adds, removes, or modifies allowed endpoints in the sandbox policy.

Explains operational tasks after the quickstart: listing sandboxes, status and health checks, logs, diagnostics, port forwards, multiple sandboxes, credential reset, rebuilds, netw...

Inspects sandbox health, traces agent behavior, and diagnoses problems.

Explains how OpenClaw, OpenShell, and NemoClaw form the ecosystem, NemoClaw's position in the stack, what NemoClaw adds beyond the community sandbox, and when to prefer NemoClaw ve...

Describes the NemoClaw plugin and blueprint architecture and how they orchestrate the OpenClaw sandbox.

> Debug AutoDeploy accuracy regressions vs a reference score (PyTorch backend or published baseline).

> Claude Code skill (trtllm-agent-toolkit): implement or extend TensorRT-LLM AutoDeploy fusion transforms under transform/library/ in a TensorRT-LLM checkout.

> Check whether AutoDeploy YAML configs were actually applied by analyzing server logs and optionally graph dumps (AD_DUMP_GRAPHS_DIR).

> Enable and interpret TensorRT-LLM AutoDeploy FX graph text dumps via AD_DUMP_GRAPHS_DIR.

> Visualize a specific transformer decoder layer from an AutoDeploy FX graph text dump as a hierarchical DOT/PNG diagram.

> Translates a HuggingFace model into a prefill-only AutoDeploy custom model using reference custom ops, validates with hierarchical equivalence tests.

Compile TensorRT-LLM on a compute node inside a Docker container.

Compile TensorRT-LLM on a SLURM cluster.

> Write and implement GPU kernels using NVIDIA CuTe DSL (CUTLASS 4.x Python API) — NOT for Triton, CUDA C++, or conceptual explanations.

> Optimize existing Triton kernels for NVIDIA TileIR backend on Blackwell GPUs (sm_100+).

> ONLY for OpenAI Triton (@triton.jit) kernel development.

> Performance analysis coordination workflow.

> Analyze host/CPU overhead in TensorRT-LLM inference from nsys traces.

Profiles and optimizes TensorRT-LLM host/CPU overhead using line_profiler (with nsys support planned).

> Analyze ncu (NVIDIA Nsight Compute) profiling output: SOL% bottleneck classification, roofline analysis, occupancy diagnosis, memory hierarchy analysis, warp stall analysis, metr...

>- Nsight Systems (nsys) CLI for system-level timeline profiling.

> Performance optimization coordination playbook.

>- Apply CUDA Graphs to PyTorch workloads — API selection (torch.compile, PyTorch make_graphed_callables, TE make_graphed_callables, MCore CudaGraphManager, FullCudaGraphWrapper, m...

>- Identify and eliminate host-device synchronizations in PyTorch code.

> Code instrumentation for timing workloads.

> Best practices for contributing code to TensorRT-LLM.

> Systematic approach to exploring the TensorRT-LLM codebase before implementing new features or optimizations.

>- Upgrade flashinfer-python version in TensorRT-LLM.

>- Review, design, and refactor TensorRT-LLM PyTorch MoE code for architecture fit, clean code, maintainability, and testability.

Generate a source-backed starting `trtllm-serve --config` YAML for basic aggregate single-node PyTorch serving, aligned with checked-in TensorRT-LLM configs and deployment docs.

Add a new cuTile GPU kernel operator to TileGym.

Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents.

Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit).

Use when adding, modifying, optimizing, or debugging CuTile autotuning code.

Expert cuTile programming assistant.

Iteratively optimize cuTile kernel performance through systematic profiling, bottleneck analysis, IR comparison, and targeted tuning.

Integrate TileGym kernels into Hugging Face `transformers` models by replacing the library's submodule(s) and certain class(es)' implementations, and patching certain class(es)' in...

Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI).

Install cuOpt for Python, C, or as a server (pip, conda, Docker) — system requirements, install commands, and verification.

LP, MILP, and QP (beta) with cuOpt — C API only.

LP, MILP, and QP (beta) with cuOpt — CLI only (MPS files, cuopt_cli).

Solve Linear Programming (LP), Mixed-Integer Linear Programming (MILP), and Quadratic Programming (QP, beta) with the Python API.

Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only.

cuOpt REST server — start server, endpoints, Python/curl client examples.

cuOpt REST server — what it does and how requests flow.

Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server).

Numerical optimization (LP, MILP, QP) — concepts, problem-text parsing, and formulation patterns.

Vehicle routing (VRP, TSP, PDP) — problem types and data requirements.

After solving a non-trivial problem, detect generalizable learnings and propose skill updates so future interactions benefit automatically.

NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API.

> Use this skill to bring any vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engi...

Deploy Nemotron Voice Agent on Workstation (x86), Jetson Thor, or Cloud NIMs.

"NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage.

Manage and monitor VSS alerts after the alerts profile is deployed.

Deploy, debug, or tear down any VSS profile using a compose-centric workflow — config (dry-run) with env overrides, review resolved compose, then compose up.

Produce video analysis reports by discovering the deployed VSS agent, querying POST /generate for a timestamped captioned summary of the clip, then formatting the agent reply as th...

> Use this skill when working with the RTVI VLM or RT-VLM microservice API on VSS 3.1.

Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901).

Search video archives using natural language — find events, objects, actions, and people across recorded video using fusion search (Cosmos Embed1 semantic search + CV attribute sea...

Summarize a video by calling the VLM NIM or the Long Video Summarization (LVS) microservice directly.

Call the vss agent to run video understanding on video to answer a text question.

Query VIOS REST APIs: sensor list, recording timelines, video clip extraction, snapshot capture, add/delete sensors and streams

Generate video summary reports using the VSS video_search_frag extension with Long Video Summarization (LVS), Enterprise RAG knowledge retrieval, and human-in-the-loop parameter co...

Interact with the Gemini Enterprise Agent Platform Skill Registry to create and search for available skills.

Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB model context protocol (MCP) tools for automated database operations.

Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights.

Manages Cloud Run services, jobs, and worker pools.

This file generates or explains Cloud SQL resources.

Use this skill whenever you are working on a project that uses Firebase products or services, especially for mobile or web apps.

Manages custom Agent resources on Gemini Enterprise Agent Platform.

Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK.

Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform.

Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration.

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics.

Provides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and b...

Guidance for a developer's first steps on Google Cloud, covering account creation, billing setup, project management, and deploying a first resource.

Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF).

Generates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Ar...

Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well...

Generates reliability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework.

Generates security-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF).

Generates sustainability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF).

Agent skills for Qdrant vector search, covering scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, and Java

Extract text, tables, and metadata from 62+ document formats

AWS development with infrastructure automation and cloud architecture patterns

Execute safe read-only SQL queries against PostgreSQL databases

Vector-powered CLI for semantic file search with a Claude/Codex skill

Three.js skills for creating 3D elements and interactive experiences

Create diverse synthetic test inputs for LLM evals

753 cybersecurity skills across 38 domains: cloud security, pentesting, red teaming, DFIR, malware analysis, threat intel, and more (MITRE ATT&CK mapped)

Human-like TTS workflows with local/cloud APIs and app delivery

Diagnose and optimize Agent Skills (SKILL.md) with real session data and research-backed static analysis. Works with Claude Code, Codex, and any Agent Skills-compatible agent

Graph-based long-term memory skill for AI (LLM) coding agents — faster context, fewer tokens, safer refactors

11 skills for the Honeydew semantic layer over Snowflake, Databricks, and BigQuery: model exploration, entity/relation/attribute/metric/context/domain creation, validation, query, filtering, and workspace branching

Genealogy research agent with OCR, FamilySearch, YAML data, and human-in-the-loop

AI-powered VMware vCenter/ESXi monitoring and operations: inventory queries, health/alarms, VM lifecycle (create, delete, snapshot, clone, migrate), vSAN management, Aria Operations analytics, and scheduled log scanning. Supports Claude Code, Gemini CLI, Codex, Aider, Trae, Kimi, and MCP.

JavaScript in n8n Code nodes with data access patterns

Workflow patterns for webhook, HTTP, database, and AI tasks