---
name: marketing-aeo
description: "Track brand and domain citations in ChatGPT, Perplexity, Google AI Overview, and Claude. Use for AEO/GEO monitoring, AI-search visibility, chatbot mentions, citation queries, or project-level AI answer tracking."
---

# AEO Monitor

Track how often AI chatbots cite your site for queries that matter to your audience. Stores everything locally in SQLite per project; tracks actual cost per provider; works via `/loop` for recurring checks.

## How to use it

This is a Node.js CLI. Invoke via the `aeo` wrapper script at the skill root:

```
<plugin-path>/skills/marketing-aeo/aeo <command>
```

The wrapper auto-installs deps on first run (pnpm preferred, npm fallback). The skill data **lives with the user's project**, not with the plugin — every command is run from inside the project directory (`cwd`), and writes to `<project>/.aeo/`.

For convenience, suggest the user alias it:

```
alias aeo='<plugin-path>/skills/marketing-aeo/aeo'
```

Requirements:
- Node 22+
- pnpm or npm (for the one-time dep install)

## Setup flow — guide the user through this

When the user first asks to set this up for a project:

1. **Verify env vars are set** — run `aeo doctor`. If any of these are missing, give them the get-it links and stop:
   - `FIRECRAWL_API_KEY` (firecrawl.dev) — for ChatGPT + Perplexity
   - `DATAFORSEO_API_KEY` (base64 of `login:password`) — for Google AI Overview
   - `GEMINI_API_KEY` (aistudio.google.com/apikey) — for structured extraction
   - `ANTHROPIC_API_KEY` (optional, console.anthropic.com) — for Claude

2. **`cd` into the user's project**, then `aeo init`. This creates `<project>/.aeo/runs.sqlite`.

3. **Research the project before adding queries** — read `CLAUDE.md`, `README.md`, `src/lib/content-registry.ts` (or equivalent), the site map. Understand:
   - What does this project do?
   - Who is the audience?
   - What questions do they ask AI chatbots?

4. **Propose 10-20 natural-language queries** the audience would actually ask. Examples:
   - "what is X and how does it work" (general)
   - "X vs competitor" (comparison)
   - "X side effects / pricing / when launches" (specific)
   - "how do I do Y" (how-to that the project answers)

   Don't just list keywords. Phrase them like real people type into ChatGPT.

5. **Confirm queries with the user**, then add them one at a time:
   ```
   aeo query add "what is retatrutide and how does it work"
   aeo query add "retatrutide vs mounjaro for weight loss"
   ...
   ```

6. **Add domains to detect.** The user's own domain + close competitors:
   ```
   aeo domain add example.com --label own
   aeo domain add www.example.com --label own
   aeo domain add competitor.com --label competitor
   ```

7. **Dry run** to confirm cost: `aeo run --dry-run`. Then **first real run**: `aeo run`.

8. **(Optional) Set a budget** if they want a warning when costs rise: `aeo budget set 1000` (= $10/mo).

9. **Suggest a /loop or /schedule** for ongoing monitoring:
   - `/loop 24h aeo run` for daily checks
   - Or use the `schedule` skill for cron-based remote runs

## Daily run flow (used by /loop)

```
aeo run               # runs all queries × all configured platforms
aeo report --days 7   # citation rate per query per platform
aeo cost --days 7     # actual spend last 7 days
```

The user can also restrict scope:
- `aeo run --platform chatgpt`
- `aeo run --query "specific question"`
- `aeo run --dry-run` (cost estimate without executing)

## Reading results

After a run, the structured data is in SQLite. Useful commands:

| Question | Command |
|---|---|
| Are we cited at all? | `aeo report --days 7` |
| For which queries? | `aeo history --query "..."` |
| Who are we losing to? | `aeo competitors --days 30` |
| What did we spend? | `aeo cost --by provider` |
| Which queries cost most? | `aeo cost --by query` |

## Cost reference

Per query × platform (rough):
- ChatGPT (Firecrawl + Gemini extract): ~$0.012
- Perplexity (Firecrawl + Gemini extract): ~$0.012
- Google AI Overview (DataForSEO + Gemini extract): ~$0.003
- Claude (Anthropic Haiku + Gemini extract): ~$0.010

A full daily run with 10 queries × 4 platforms ≈ $0.40/day or ~$12/month per project.

## When user wants new queries

Don't just guess. Sources of real questions people ask:

1. **GA4 referrer data** — what queries from ChatGPT/Perplexity drove existing traffic? (Look at landing pages with `utm_source=chatgpt.com`.)
2. **GSC top impressions** — what Google queries do they already get impressions on? Convert to natural-language form for chatbots.
3. **Reddit/YouTube comments** — pull questions from `comment-mine` skill if available.
4. **The site's content map** — every major article suggests 2-3 corresponding queries.

Propose, confirm, add. Don't bulk-add without review.

## Gotchas

- **Responses vary run to run.** ChatGPT may cite you 1 in 5 times for the same query — that's normal. Citation rates are statistical, not deterministic. Run daily, look at rates over time.
- **Mobile app traffic strips referrers.** GA4 won't show all AI traffic — many ChatGPT users on mobile land as Direct.
- **First run is slowest.** ChatGPT/Perplexity scrapes wait 15-18s for streaming responses.
- **Concurrency is capped (default 3).** ChatGPT/Perplexity are Firecrawl scrapes; firing every job at once saturates the Firecrawl **hobby** plan's concurrency limit and trips server-side `SCRAPE_TIMEOUT` (408) — a single scrape succeeds in ~15s, so a wall of timeouts means too much parallelism, not a broken URL. Tune with `AEO_CONCURRENCY` (raise it on a paid Firecrawl plan: `aeo config set firecrawl_plan standard`), or `--serial`.
- **Firecrawl → DataForSEO fallback.** If a ChatGPT/Perplexity scrape still errors or returns empty, the run falls back to the DataForSEO Google-AI-Overview citation set for that query (tagged `operation: ai_overview_fallback`, `metadata.fallback_provider`) so a run never zeroes out. It's a *different surface* (Google's AI answer, not the native chatbot) — treat fallback rows as a proxy. Disable with `AEO_DISABLE_DATAFORSEO_FALLBACK=1`.
- **`.aeo/` should be gitignored.** It's local data, not source. The skill auto-creates `.aeo/raw/` for debugging — same applies.

## Architecture notes (for debugging)

- `aeo` — shell wrapper that runs `tsx src/cli.ts` from the skill's own `node_modules`
- `src/cli.ts` — commander entrypoint
- `src/scrapers.ts` — one function per platform (Firecrawl, DataForSEO, Anthropic)
- `src/extract.ts` — Gemini 2.5 Flash structured extraction (regex fallback if no `GEMINI_API_KEY`)
- `src/db.ts` — SQLite (better-sqlite3) schema + query helpers
- `src/costs.ts` — provider rate tables (update when prices change)

Every API call records to `cost_events`. Every scrape writes raw output to `.aeo/raw/` for debugging when extraction is weird.