Building a Weekly
Style Signal Report

Style discourse moves quickly across platforms, editorial media, retail copy, and brand campaigns. Signals are often flattened into trends without source context, historical memory, or incentive analysis.

Build a recurring report that tracks what style culture is saying without turning it into shopping advice or brand forecasting.

Covers the crawler, MCP workflow, report schema and validation, confidence derivation, manual social sampling, archive and timeline interfaces, transparency disclosures, and editorial/product direction.

A crawler collects public source material. MCP tools expose extracted data. Language models summarize and structure recurring signals. Reports are validated against a controlled schema, saved as dated JSON files with a revision history for corrections, and rendered across an archive, a per-date report view, a reverse-chronological timeline, and a per-signal longitudinal view in a Next.js frontend. A CI workflow re-validates the archive on every push.

The interface uses an editorial/reporting tone, restrained typography, clear hierarchy, source transparency, and index-like structure. Report pages carry structured data, a stable citation line, and heading markup built for accessibility rather than visual-only hierarchy.

Signals are evaluated by recurrence, source diversity, source type, incentive context, visual coherence, volatility, and historical continuity. Confidence can additionally be derived deterministically from corroboration count and source-sector diversity, tracked separately from editorially assigned confidence so the two are never conflated.

AI is used for extraction and organization, not taste authority. Human interpretation and source transparency remain central. Corrections, editorial-independence, and AI-involvement disclosures are published on the methodology and about pages, and a genuinely low-signal reporting period is disclosed as such rather than padded with manufactured signals.

TikTok and Pinterest signals are added through a manual sampling workflow sourced from official platform trend reports or APIs, each carrying a required editor note, rather than through scraping. The workflow has been exercised against real reports, not only designed.

Every report in the archive is hand-authored or research-assembled rather than produced by a live crawl merged into the archive; a real crawl-and-summarize run has succeeded once but its output was not merged, pending a deliberate resolution of a same-date collision. Migration off the legacy cache-file pipeline is nearly, not fully, complete.

Scheduled live crawls merged into the archive, source-sector-aware crawling, source-sector comparison views, and completion of the legacy pipeline migration.