What problem does it solve? Documents in a Fux search index can only be found by words they literally contain, so a postmortem titled "checkout unavailable for 47 minutes" will never match a search for "what happened during the checkout outage". This Skill writes the questions a searcher would type, attaching new searchable vocabulary to each document so it can be found by words it does not contain. ## Core Features & Use Cases - Question generation (doc2query): Writes five to ten natural-language questions per document into .fux/enrich/<sha>.md files that are committed to git and indexed as searchable context. - Plan-driven workflow: Uses fux enrich --plan to get the exact worklist, sha, and chunk count, then re-verifies the sha immediately before writing to avoid stale enrichment. - Retrieval validation: Every question is checked with fux enrich --check, which runs each question as a real search and refuses the file if the question does not place its own document in the top 3. - Use Case: A user asks to "enrich the ranking SR". The Skill runs the plan, reads records/0111_ranking.md, writes questions like "How does fux decide which result comes first?", validates them, and the document becomes findable by searcher vocabulary. ## Quick Start Enrich the document records/0111_ranking.md in my Fux corpus so it can be found by questions it does not literally answer.