KeyAPI Twitter Content Analytics

What problem it addresses

When analyzing Twitter / X data, the hard part is not writing code but mapping a business question to the right API surface. A request like “check topic heat” may span search, profiles, replies, retweets, trends, lists, or communities. Listing endpoints and parameters directly can lead to wrong fields, missed pagination, or mistaking a docs URL for a /v1/... route. This skill turns the request into an executable workflow: confirm the goal, entity, scope, metrics, and output, choose a scenario, verify current KeyAPI docs, then call the REST API.

How it works

• Goal-to-scenario routing: route by tweet, profile, search, trend, community, list, or Spaces workflows instead of asking users to pick endpoints first.
• Docs-first execution: use https://docs.keyapi.ai/llms.txt and endpoint pages as the source of truth for method, path, parameters, pagination, and response shape; if official docs conflict, follow official docs.
• Script-backed calls: use scripts/configure-keyapi-auth.mjs --status for auth checks, scripts/search-keyapi-docs.mjs for doc resolution, and scripts/keyapi-api.mjs for live requests; fall back to equivalent REST when scripts are unavailable.
• Input compression: collect only Goal, Entity, Scope, Metric or sort, Pagination depth, and Output format to avoid parameter overload.

Boundaries and notes

It is for KeyAPI REST data lookup, not platform web scraping. If credentials are missing, complete local KEYAPI_TOKEN setup before calling the API, and never print or restate the token. Prefer --body-file or --image-file for large request bodies, use --output-file for large responses, then extract, sort, and aggregate from data.data. For multi-endpoint reports, confirm sections and maximum scope first to avoid unbounded batch calls.