What problem does it solve? AI-generated answers and reports often contain hallucinated or unverifiable citations because URLs are reconstructed from memory. This Skill eliminates that risk by assigning each retrieved source a stable numeric id in a persistent ledger, so the model only ever emits integers it was handed and the URL mapping is rendered mechanically. ## Core Features & Use Cases - Citation Ledger: Register URLs at retrieval time with sources.py add or ingest; ids are idempotent, URL-normalized, and stable across multi-turn and multi-agent work. - Inline Numbered Citations: Write Perplexity-style [n] markers per sentence, then render the Sources block in markdown, plain, footnotes, bibtex, or evidence styles. - Fact-Checking Mode: Attach verbatim quotes validated against fetched page text, flag unsourced claims with [unverified], and gate drafts with verify --evidence --min-coverage. - Use Case: While writing a market research report from web searches, Reddit threads, and RSS feeds, register every URL as it arrives, cite each claim inline, then run verify before delivery to catch unknown ids or thinly cited prose. ## Quick Start Ask the agent to research a topic across the web and deliver an answer where every factual claim carries an inline numbered citation and a rendered Sources list verified against the citation ledger.
