Overview
What it does
Audits and restructures content so LLMs and AI answer engines surface and quote it — quotable structure, entity clarity, factual grounding.
When to use it
When you want Uplifted (or a customer's brand) to be found and cited by LLMs — buyers now ask ChatGPT and Claude to recommend and compare tools, and several of Uplifted's own customers arrived that way. This audits and structures content so AI answer engines surface and quote it.
Pairs with MCP
The model can pull Uplifted's real capability and benchmark data to produce the kind of structured, factual, quotable content LLMs cite — and customers can run the same skill against their own brand.
Best for
SEO/content marketers, growth teams, founders.
Install
Drop this entire block into a SKILL.md file inside your Claude project’s .claude/skills/llm-discovery-optimizer/ folder. Claude auto-invokes it for AI-search / answer-engine optimization.
---
name: llm-discovery-optimizer
description: Use this skill to make a brand or product discoverable and citable by LLMs and AI answer engines (ChatGPT, Claude, Perplexity, Google AI Overviews). Audits content for quotable structure, entity clarity, and factual grounding, and outputs the fixes.
---
# LLM-Discovery Optimizer
You make content the kind of thing an LLM reaches for and quotes when answering a buyer's question.
## Data you need
- The page(s) or content to optimize, and the target questions buyers ask an LLM (e.g., "best creative analytics tool for DTC", "[competitor] alternative")
- The brand's verified facts (capabilities, benchmark stats, claims) from Uplifted to ground assertions
- Current schema/structured data on the page, if any
## How to analyze
1. ANSWERABILITY — for each target question, does the content contain a clean, standalone, quotable answer near the top? Flag where it's buried or missing.
2. ENTITY CLARITY — is it unambiguous what the product is, who it's for, and how it relates to named alternatives? LLMs need clean entities.
3. QUOTABLE FACTS — are key claims stated as self-contained facts with numbers/sources (the units LLMs lift), or as fluffy prose?
4. STRUCTURE — headings, FAQ, comparison tables, and schema that machines parse cleanly.
5. GROUNDING — every factual claim traceable to a verified source (no hallucination bait).
## Output format
LLM-DISCOVERY AUDIT — [page/brand]
QUERY COVERAGE — per target question: is there a quotable answer? (yes / buried / missing) + the fix
QUOTABLE REWRITES — specific passages rewritten as standalone, citable facts
ENTITY & STRUCTURE FIXES — headings, FAQ, comparison table, and schema to add (with the JSON-LD types)
GROUNDING CHECK — claims lacking a verifiable source, flagged
PRIORITY LIST — the 5 highest-leverage changes for AI visibility
## Guidelines
- Optimize for being quoted, not just ranked — standalone, factual passages win citations.
- Ground every claim; fabricated specifics get a brand cited wrongly or not at all.
- Recommend concrete schema types and FAQ entries, not "add structured data" in the abstract.Prompt
Audits and restructures content so LLMs and AI answer engines surface and quote it — quotable structure, entity clarity, factual grounding. Use it when you want to be the source an AI cites, not the page it skips.
Audit this content so LLMs and AI answer engines surface and cite it.
Content: {{paste page(s)}}. Target questions buyers ask an LLM: {{list}}. Verified brand facts from Uplifted to ground claims: {{paste or confirm MCP}}. Existing schema: {{paste or none}}.
1. ANSWERABILITY — per question, is there a clean, standalone, quotable answer near the top? (yes / buried / missing)
2. ENTITY CLARITY — is the product, audience, and relation to alternatives unambiguous?
3. QUOTABLE FACTS — are key claims self-contained facts with numbers/sources?
4. STRUCTURE — headings, FAQ, tables, schema that parse cleanly.
5. GROUNDING — every claim traceable to a source.
Output: query coverage (per question + fix), quotable rewrites of specific passages, entity & structure fixes (incl. JSON-LD types), a grounding check, and the 5 highest-leverage changes.
Optimize for being quoted, not just ranked. Ground every claim. Recommend concrete schema/FAQ, not abstractions.
EXPECTED OUTPUT:
- A per-question read on whether your content can be quoted, with fixes
- Specific passages rewritten as standalone, citable facts
- A prioritized list of structure and schema changes for AI visibility