What problem does it solve? When an AI repeatedly consults the same documentation site, it wastes effort rediscovering which page to open every session. This Skill captures that one-time exploration cost as a local routing table mapping tasks ("I want to...") to exact URLs with anchors, so future lookups hit the right page with a single fetch. ## Core Features & Use Cases - Site map extraction: Fetches candidate links via a four-level priority chain (llms.txt > sitemap.xml with recursive index support > manual sitemap > navigation fallback) using a zero-dependency Python script. - Task-oriented routing tables: Produces index.md plus topics/ partition files under .web-index/{site}/, where every entry maps a user task to a URL, anchor, and keywords—never mirrors page content. - Dual-mode operation: Consumption mode queries existing indexes and fetches directly; build mode runs the full six-stage pipeline (admission check, scope confirmation, map fetch, purpose annotation, persistence, delivery) only for unindexed sites. - Use Case: You tell the AI "I'll be working from the MinIO docs repeatedly—index this site." It crawls the sitemap, annotates 87 links with task descriptions, and writes .web-index/minio/. Two weeks later, a question about TLS certificates is answered by reading one topics file and fetching one anchored URL. ## Quick Start Ask the AI to build a web index for a documentation site you will reference repeatedly, for example: "Create a web index for https://min.io/docs/minio/linux since I'll consult it throughout this project."