Agent Skills: datacommons-client
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
ID: obinopaul/agents-backend/datacommons-client
21
Install this agent skill to your local
Skill Files
The files that make up datacommons-client.
More agent skills
- x-scraperX (Twitter) 抓取 skill - 通过 agent-browser (CDP) 抓取指定用户推文或首页推荐流，支持关键词过滤、Tab 切换、多格式输出。使用场景：按用户/关键词抓取时间线、查看首页推荐流、生成 RSS/JSON/Markdown。
- native-parametersTemplate tags for native SQL questions written through question_write's `native` — tag kinds, field-filter vs raw-variable, the template_tags shape, widget types, [[ ]] optional blocks, wiring tags to dashboards. Read before first passing template_tags. Triggers — "add a filter widget to my SQL", "parameterize this query", "field filter", "why does my variable return no rows", "wire a dashboard filter to a SQL card".
- ef-coreGet best practices for Entity Framework Core
- markdown-converterMarkdown conversion: PDF, Office, HTML, data, OCR, audio, ZIP, YouTube.
- ai-multimodalProcess and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
- huggingface-community-evalsRun evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.