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Found 331 Skills
Implement Syncfusion WPF TreeMap (SfTreeMap) control for hierarchical data visualization using nested rectangles. Use this when visualizing large datasets with hierarchical structure, creating heat maps, or displaying proportional data. This skill covers TreeMap configuration, layout algorithms, color mapping, data binding, and interactive features for stock analysis, data categorization, and hierarchical visualization scenarios.
Use when the user needs ML pipelines, statistical analysis, data preprocessing, feature engineering, model selection, experiment tracking, or data visualization. Triggers: dataset exploration, model training, feature engineering, hyperparameter tuning, experiment tracking setup, statistical hypothesis testing, visualization creation.
Cohere integration. Manage Documents, Models, Datasets, Jobs. Use when the user wants to interact with Cohere data.
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
Local execution tools for X/Twitter hosted collection workflows, including actor runs, dataset normalization, tweet ranking, account ranking, audience graph construction, and language clustering.
Local execution tools for Xiaohongshu/Rednote hosted collection workflows, including actor runs, dataset normalization, account and post ranking, comment clustering, product-pool ranking, and topic-map building.
Implement Syncfusion Angular Sparkline component for compact data visualization. Use this skill whenever the user needs to create sparkline charts, visualize small datasets inline, add markers or data labels, implement different sparkline types (Line, Column, Area, Pie, Win-Loss), or handle sparkline customization like tooltips, axis settings, and theme styling. Covers installation, basic rendering, type selection, marker configuration, data label formatting, advanced features, accessibility, and migration from EJ1.
Use this skill when an AI agent needs to manage, audit, report on, create, pause, update, or troubleshoot Meta/Facebook/Instagram ads through Meta's official Ads CLI (`meta ads ...`). It is designed for any shell-capable agent, not just OpenClaw. It focuses on safe command planning, JSON output, confirmation gates, read-before-write behaviour, paused-by-default launches, reporting workflows, datasets/pixels, catalog/product operations, and failure handling.
Write, push, run, publish, and manage Kaggle Benchmark tasks using the kaggle CLI and the kaggle-benchmarks Python SDK. Use when the user wants to create or push a benchmark task (optionally with attached Kaggle datasets), run benchmarks against LLM models, check task/run status, stream or fetch execution logs, download results and source notebooks, publish a task to make it public, or troubleshoot benchmark workflows.
Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning traces, via VLM/LLM distillation. Use when the user wants to "create video training data", "generate video QA datasets", "build CoT reasoning traces from videos", "auto-label videos", or run the video_reasoning_annotation pipeline. Triggers include "video annotation", "video CoT", "video QA", "chain-of-thought", "video captioning pipeline", "video distillation".
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.