Google KeyAPI Data Analysis

Concrete Problem

When you need data from Google web, image, video, news, shopping, Scholar, patents, or local places, manual browsing and ad-hoc API calls are fragile. Endpoint paths, parameter enums, pagination, and response shapes can change with KeyAPI docs, and memory-based calls often produce 404s. The skill turns natural-language goals into a KeyAPI REST workflow grounded in official documentation, for search, ranking, comparison, analysis, and reporting, rather than making users inspect endpoints one by one.

How It Works

• Semantic routing first: identify the goal, entity, scope, metric, pagination depth, and output format, then map the request to web, image, video, places, news, shopping, scholar, or patents scenarios.
• Contract verification before calls: use https://docs.keyapi.ai/llms.txt and the selected endpoint page as the source of truth for method, /v1/... path, request body, and response shape.
• Local execution: prefer scripts/keyapi-api.mjs for live REST calls, with --query-param, --body-file, --image-file, and --output-file for structured queries, large payloads, and analysis-ready results.
• Layered output: separate observed API facts from inference and present analytical language instead of dumping raw endpoint lists.

Boundaries and Caveats

This skill covers Google data surfaces supported by KeyAPI and does not replace manual web browsing. If KEYAPI_TOKEN is unavailable, live execution should pause and local credential setup should follow the KeyAPI docs. For broad multi-endpoint reports, confirm the sections and maximum scope first to avoid unapproved expansion.