Interactive Hamilton DAG development via MCP tools. Validate, visualize, scaffold, and execute Hamilton pipelines without leaving the conversation. Use when building or debugging Hamilton dataflows interactively.
Performance and parallelization patterns for Hamilton including async I/O, Spark, Ray, Dask, caching, and multithreading. Use for scaling Hamilton workflows.
Research, compare, and update AI model configurations. Covers text model tiers, image generation models, image tool models, pricing data sourcing, and the budget/rate-limit system. Use when bumping model versions, adding new models, updating pricing, or auditing model specs against provider documentation.
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).
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 AWQ", "quantize LLM", "quantize MoE", "quantize VLM", or needs to produce a quantized HuggingFace or TensorRT-LLM checkpoint from a pretrained model using ModelOpt.
Implements Figma designs 1:1 using OneKey component library (还原设计稿).
Transform text content into professional Mermaid diagrams for presentations and documentation. Use when users ask to visualize concepts, create flowcharts, or make diagrams from text. Supports process flows, system architectures, comparisons, mindmaps, and more with built-in syntax error prevention.
This skill should be used when the user asks to "add a parameter", "define parameters", "create an enum parameter", "add a gain control", "add a frequency parameter", "use parameter groups", "randomize parameters", "reset to defaults", "smooth a parameter", "use LogParamSmooth", "create presets", "serialize state", "handle OnParamChange", "copy parameter values", "use InitDouble", "use InitEnum", "use InitBool", "use parameter shapes", "use ShapePowCurve", "parameter flags", or discusses parameter definition, grouping, batch operations, smoothing, presets, or state serialization in an iPlug2 plugin.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
A skill that uses GLM-V native grounding capabilities for coordinate conversion, bounding-box visualization, and more. GLM-V native grounding can locate any target specified by the prompt in an image and output relative coordinates normalized to 0-1000 based on image size. Coordinate formats include 2D bounding box (default), 2D points, and 3D bounding box. GLM-V also supports spatiotemporal localization and tracking of multiple prompt-specified targets in videos, outputting 2D bounding boxes per second.
Query Etherscan V2 API for gas prices, account transactions, balances, token transfers, contract source code, and compilation metadata via Nethereum.DataServices
Stream real-time blockchain data with Nethereum. Use when the user asks about WebSocket subscriptions, new block headers, pending transactions, event log streaming, Rx observables, DEX monitoring, or live token transfer tracking.
Transforms research findings into executive-ready briefings. Automatically activated when user mentions 'executive', 'briefing', 'C-suite', 'board', 'leadership', or 'presentation'.
Claw Compactor — 6-layer token compression skill for OpenClaw agents. Cuts workspace token spend by 50–97% using deterministic rule-engines plus Engram: a real-time, LLM-driven Observational Memory system. Run at session start for automatic savings reporting.
When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,' 'conversion rate,' 'CAC,' 'LTV,' 'NRR,' 'GTM dashboard,' 'magic number,' 'pipeline velocity,' or 'funnel metrics.' This skill covers GTM measurement from metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences. Do NOT use for technical implementation, code review, or software architecture.
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. Triggers: "data lake", "DataLakeServiceClient", "FileSystemClient", "ADLS Gen2", "hierarchical namespace".
Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing. Triggers: "queue storage", "QueueServiceClient", "QueueClient", "message queue", "dequeue".
Add a PostgreSQL database with Drizzle ORM to a Scaffold-ETH 2 project. Use when the user wants to: add a database, use Drizzle ORM, integrate Neon PostgreSQL, store off-chain data, build a backend with database, or add persistent storage to their dApp.
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.
