# Geo Gap Finder > Live-data GEO (Generative Engine Optimization) opportunity finder, powered by the Bright Data MCP. Give it your product and it runs deep research to find the AI-search prompts you can actually WIN — the prompts where ChatGPT cites nobody, cites something irrelevant, or cites a page you could easily beat. It profiles your product, validates which topics are in scope, discovers 15-20 real long-tail prompts people are searching, runs live GEO citation diagnosis on each, then ranks them into an opportunity matrix scored by how easy each one is to capture. Every prompt, citation, and score is grounded in something scraped live in this run. Nothing comes from training-data memory. Sources are always cited. Search volume and domain authority are labeled as PROXIES (no such metric exists in this data source) — never invented numbers. Trigger whenever the user wants to: find GEO / AI-search opportunities for their product, figure out "where can I rank in AI search", "which prompts should I target", "what AI-search gap - Skill: `nimish-html/geo-gap-finder` (Agent Skill, multi-file: 8 files) - Install (CLI): `npx skillmds@latest add nimish-html/geo-gap-finder` - Raw SKILL.md: https://api.skillmd.com/api/skills/nimish-html/geo-gap-finder/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: nimish-html (https://skillmd.com/u/nimish-html) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/nimish-html/geo-gap-finder --- # GEO Opportunity Finder A guided, multi-phase deep-research flow whose whole purpose is to find the **AI-search prompts your product can actually win** — and prove each one is winnable with **live data scraped right now** via the **Bright Data MCP** (server name: `brightdata`). It exists to defeat the #1 objection to AI-generated SEO/GEO advice — *"the AI just hallucinates"* — so the prime directive is non-negotiable: > **NO-FABRICATION RULE.** Never state a prompt, ranking, competitor, citation, > metric, or "best practice" unless it came from a live tool call in THIS run. If > a live call fails or returns nothing, write **"⚠️ Live data unavailable for this > step"** and stop that step. Do not fill the gap from memory. Do not invent search > volumes, citation counts, domain-authority scores, or URLs. **Search volume and > domain authority do not exist in this data source — treat any demand/authority > read as a labeled PROXY** (People Also Ask / related presence; organic SERP rank > + recognizability). Every output ends with a **Sources** section listing the > exact URLs/queries it pulled. ## The big idea Most SEO tools tell you how to rank for a keyword you already picked. This skill answers the question that comes *first*: **which battles are even worth fighting?** It scans many prompts about your product and ranks them by how easy they are to capture in AI search. The opportunity matrix is the deliverable. Writing the content to capture a chosen opportunity is an *optional* follow-on. **An opportunity is a prompt where (your hypothesis, made measurable):** - 🟢 **Nothing is cited** — the AI answers from memory; any solid page can become the source. - 🟢 **Something irrelevant is cited** — the cited pages don't really answer the prompt. - 🟡 **A recreatable page is cited** — a blog, listicle, product page, Q&A, or Reddit thread you could out-build. - 🔴 (not an opportunity) **A hard-to-beat source is cited** — official docs, Wikipedia, standards bodies, gov, top-brand domains. - ⚪ (defend, not capture) **Your own domain is already cited.** ## Prerequisites (check first) 1. The `brightdata` MCP server must be connected. Confirm `mcp__brightdata__*` tools are available. If **not**, tell the user: *"The Bright Data MCP isn't loaded. Copy `.mcp.json.example` → `.mcp.json`, paste your API token (raw, not `${VAR}`), restart Claude Code, then re-run."* Do not attempt the phases without it. 2. Skim `references/tool-map.md` for the **real** tool names + which tier (free vs. credits) each call burns. If a tool name there isn't present in-session, list the actual `mcp__brightdata__*` tools and use those — never guess a tool name. 3. Read `references/opportunity-scoring.md` — it holds the scoring rubric and the opportunity-type taxonomy used in Phase 5. ## How to invoke - **Whole flow:** "find GEO opportunities for ``" → runs Phase 1 → 5, pausing for input at Phase 2 and at the Phase 3 credit checkpoint. - **A single phase:** pass `--phase` or just ask ("just profile my product", "regenerate the prompt list", "score the opportunities from the diagnosis"). - Each phase reads any prior phase's saved Markdown from `opportunity-research//` if present, else runs what it needs. ## Output location Write all artifacts as clean Markdown a non-technical founder can read, under the user's current directory: ``` opportunity-research// ├── 01-product-profile.md ├── 02-validated-categories.md ├── 03-candidate-prompts.md ├── 04-geo-diagnosis.md └── 05-opportunity-matrix.md ← the hero deliverable ``` Announce the file path after writing each one. --- ## PHASE 1 — Product Intake **Goal:** understand the product well enough to judge prompt relevance later. 1. Take the user's product URL + one-sentence description. Scrape the page with **`mcp__brightdata__scrape_as_markdown`** (free). 2. Extract a **product profile**: what it does (1-2 lines), core features, primary use cases, ICP / buyer, the category it competes in, any competitors named on the page, and the key entities/specs/numbers it owns. 3. Write `01-product-profile.md` and show it back. Ask the user to correct anything wrong before continuing — a bad profile poisons every later relevance score. **Output ends with Sources:** the URL scraped. --- ## PHASE 2 — Category Validation *(interactive)* **Goal:** prune the search space so paid calls aren't wasted on off-target prompts. 1. From the profile, derive 4-8 candidate **topic categories / angles** the product could plausibly be the answer for (e.g. for a payments-address tool: address validation, sanctions screening, ISO 20022 structured address, STP rates, payment data quality). 2. Use **AskUserQuestion** (multiSelect) to let the user confirm which categories are in scope and cut the irrelevant ones. Let them add categories you missed. 3. Write `02-validated-categories.md` listing the kept categories (and what was cut). --- ## PHASE 3 — Prompt Discovery *(free; ends at the credit checkpoint)* **Goal:** a list of ~15-20 **real** long-tail prompts, grounded in live search data. 1. For each validated category, draft candidate prompts across families: question forms, "best X for Y", "X vs Y", alternatives-to, "how do I…", integration/spec questions. 2. **Ground them in reality** — do NOT ship hallucinated prompts. For category seeds, fire **`mcp__brightdata__search_engine`** (`engine: "google"`, optional `geo_location`) and harvest the live **People Also Ask** + **related searches**. Keep/expand prompts that map to real surfaced queries; this is also where genuine long-tail demand shows up. Pipe collected SERP JSON through the helper if useful: ```bash python3 scripts/cluster_serp.py < serp.json ``` 3. Pre-rank candidates by relevance to the Phase 1 profile, dedupe, and trim to ~15-20. Tag each with its source category and whether it came from PAA/related (real) vs. constructed-but-plausible. 4. Write `03-candidate-prompts.md` and **STOP. Show the list to the user and ask them to cut/approve.** The next phase spends Bright Data credits (one paid call per prompt) — do not proceed until the user approves the final set. **Output ends with Sources:** every SERP query run + PAA/related pulled. --- ## PHASE 4 — GEO Diagnosis at scale *(credits — only after approval)* **Goal:** for each approved prompt, capture what AI search cites today and why. 1. For each approved prompt, fire **`mcp__brightdata__web_data_chatgpt_ai_insights`** (`prompt`) **one at a time (sequentially)** — parallel calls trigger transient `HTTP 400: Customer is not active`; a sequential retry succeeds. ChatGPT is the default engine because it's the only one returning explicit cited URLs. Perplexity (prose, usually no URLs) and Grok (often empty `[{}]`) are optional cross-checks — only add them if the user asks, since each is another paid call per prompt. If a call returns empty, mark "⚠️ Live data unavailable" — never invent. 2. Capture per prompt: the answer text + the **cited source URLs**. Dedupe cited URLs across all prompts, then scrape them with **`mcp__brightdata__scrape_batch`** as Markdown (**≤5 URLs per call** — batch in groups of 5). 3. For each cited page, read the **citation pattern** via `references/geo-citation-patterns.md` and note its **page type** (blog / listicle / product page / docs / Wikipedia / standards body / Reddit / etc.) and a recognizability/authority read (proxy). 4. Write `04-geo-diagnosis.md`: per prompt → who's cited, page types, whether the citations actually answer the prompt, and whether the user's own domain appears. **Output ends with Sources:** every AI prompt run + every cited URL scraped. --- ## PHASE 5 — Opportunity Matrix *(free — the deliverable)* **Goal:** rank the prompts into a clear "go capture these" list. 1. Score every prompt on two axes using `references/opportunity-scoring.md`: - **Capturability** (from what's cited): 🟢 nothing cited → 🟢 irrelevant cited → 🟡 recreatable page cited → 🔴 hard-to-beat cited → ⚪ you're already cited. - **Relevance**: how well the product (Phase 1 profile) actually answers the prompt. The helper turns the Phase-4 evidence JSON into a ranked matrix: ```bash python3 scripts/score_opportunities.py < diagnosis.json ``` (or score inline if it errors — it only organizes scraped evidence, never invents). 2. Classify each prompt's **opportunity type** and the **recommended capture move** (new blog post / product page / comparison page / FAQ / schema add). 3. Write `05-opportunity-matrix.md`: a ranked table — `prompt → capturability → relevance → who's cited now → opportunity type → capture move`. Lead with the **top-right quadrant** (easy *and* relevant) as the priority targets. Label demand/authority reads as proxies. **Output ends with Sources:** the diagnosis + SERP evidence each score rests on. --- ## OPTIONAL — Capture a chosen opportunity If the user picks an opportunity row, generate the content to capture it (this is the old content-engine, now a follow-on, not the core): 1. Build an evidence bundle from Phases 1-4 for the chosen prompt (cited competitors + their winning patterns, shared entities, PAA to answer verbatim, schema seen). 2. Fill `templates/content-brief.md` (helper: `python3 scripts/format_brief.py evidence.json`) → a brief with target prompt + intent, title/H1, H2/H3 outline mapped to PAA + entities, a ≤60-word extractable direct-answer block, FAQ/schema recommendations, entity checklist, internal-link notes — each item citing the scraped competitor/PAA it came from. 3. Draft the article to that brief: direct answer up top under each question, lists/tables where the cited winners used them, skimmable. Mark any spot needing a real stat the user must supply as `[NEEDS SOURCE: …]` — never invent a number. Save as `capture-brief.md` / `capture-draft.md`, each ending with its Sources. --- ## OPTIONAL — Track Re-run Phase 4 on the captured prompts later to see if the user's page is now cited. Output a before/after table (date, prompt, was-cited?, notable changes). Save as `tracking-.md`. --- ## Style for all outputs - Plain language, short sentences, tables over walls of text. - Lead with the takeaway (the winnable prompts), then the evidence. - Always close with **Sources** (real URLs + the live queries used). If a section has no live source, it doesn't belong in the output.