Classifying Media Relevance

Release pages contain two kinds of media: editorial content that belongs in the release (screenshots of the feature, demo videos, diagrams explaining a change) and site chrome that doesn't (author avatars, nav logos, tracking pixels, decorative separators). This skill governs which items end up in a release's media[] array.

The goal is precision-over-recall: a dropped editorial image is recoverable (users click through to the source page), but a kept junk image pollutes the UI and wastes storage.

When this runs

• During the parse pipeline, after the AI extracts release content from a fetched page.
• During crawl-mode fetches, when the extractor reads full-page markdown from a linked article and produces a fresh media[].
• Not during feed fetches where the feed already scoped media to per-entry content (trust the feed).

Cheap pre-checks (keep in code, don't spend AI tokens)

These checks are deterministic, free, and catch the overwhelming majority of obvious junk. Always run them before invoking this skill. If a pre-check drops an item, no AI call is needed.