skills/seo-intel/references/ai-platform-profiles.md

# AI Platform Profiles

Reference for querying AI platforms and optimizing content for AI visibility.

---

## Nimble AI Platform Agent Discovery

Never hardcode agent template names. The catalog changes — new agents appear,
old ones get renamed or deprecated. Discover and validate at runtime using the
three-layer pattern from `nimble-playbook.md`.

### Layer 1: Category Discovery

Run broad searches to discover all available AI and SERP agents:

```bash
nimble extract:templates list --limit 100  # then filter items for "ai"
nimble extract:templates list --limit 100  # then filter items for "chatgpt"
nimble extract:templates list --limit 100  # then filter items for "perplexity"
nimble extract:templates list --limit 100  # then filter items for "google ai"
nimble extract:templates list --limit 100  # then filter items for "gemini"
nimble extract:templates list --limit 100  # then filter items for "grok"
nimble extract:templates list --limit 100  # then filter items for "google serp"
nimble extract:templates list --limit 100  # then filter items for "search engine"
```

### Layer 2: Session-Specific Narrowing

From the results, identify agents that match the needed surfaces. For AI
visibility workflows, look for agents whose description mentions:
- Direct platform querying (ChatGPT, Perplexity, Gemini, Grok, Google AI)
- Structured `answer` + `sources` output
- SERP entity parsing (for Google Search enrichment)

### Layer 3: Validation

Validate each candidate before use:

```bash
nimble extract:templates get --extract-template-name {discovered-name}
```

Confirm:
- **Input param**: typically `prompt` (conversational) or `keyword`/`query` (search)
- **Output fields**: look for `answer`, `sources`, `links` in the schema
- **Entity structure**: SERP agents return `data.parsing.entities` as a dict keyed
  by entity type name (e.g., `AIOverview`, `OrganicResult`, `RelatedQuestion`).
  Entity types are **dynamic** — iterate all keys to detect present features.

Cache discovered template names as variables (`{chatgpt_agent}`,
`{perplexity_agent}`, `{serp_agent}`, etc.) for the duration of the run.

### Execution Patterns

After discovery, use the cached template names:

```bash
# AI platform query — substitute discovered name
nimble extract:templates run --template "{chatgpt_agent}" --params '{"prompt": "...", "skip_sources": false}'

# SERP enrichment — substitute discovered name
nimble extract:templates run --template "{serp_agent}" --params '{"query": "...", "num_results": 20, "country": "US"}'

# Batch queries (6+ per platform)
nimble extract:templates batch \
  --template "{chatgpt_agent}" \
  --input '{"params": {"prompt": "query 1", "skip_sources": false}}' \
  --input '{"params": {"prompt": "query 2", "skip_sources": false}}'
```

### What to Expect from AI Platform Agents

AI platform agents send a real prompt to the platform and return structured data:
- `data.parsing.answer` — the AI-generated answer text
- `data.parsing.sources` — array of cited sources (URL, title, snippet)
- `data.parsing.links` — extracted links from the response

Source arrays vary by platform: some include position fields (`startPosition`,
`endPosition`) for citation placement; some include `source_domain` for direct
domain matching. Check the validated schema from `nimble extract:templates get`.

### What to Expect from SERP Agents

SERP agents return `data.parsing.entities` — a **dict keyed by entity type name**.
Each value is an array of records. Common entity types: `AIOverview`,
`OrganicResult`, `RelatedQuestion`, `RelatedSearch`, `Ad`. Other types may appear
(news, images, shopping, local, knowledge panels, featured snippets). Always
iterate all keys — do not check only known types.

Common SERP agent params: `query`, `country`, `locale`, `location`, `num_results`,
`start` (pagination), `time` (time range).

**When to use SERP agents:** Feature enrichment on 3-5 priority keywords per run.
**When NOT to use:** Bulk position checks — use `nimble search --search-depth lite`
for all-keyword sweeps (cheaper, faster).

### Agent Tips

- Set `skip_sources: false` on agents that support it to get source citations.
- Sources with `startPosition`/`endPosition` show where in the answer the source
  was cited — earlier position = stronger visibility signal.
- Sources with `source_domain` simplify domain matching without URL parsing.
- All agent response fields live under `data.parsing.{field}` in the JSON.
- If an agent fails validation or returns empty, drop that platform for the run
  and note reduced coverage. Do not fabricate data for unreachable platforms.

---

## Per-Platform Ranking Factor Profiles

Based on Princeton/KDD 2024 GEO study (arXiv:2311.09735), SE Ranking (400K page
study), and ZipTie research.

### ChatGPT

- Content-answer fit accounts for ~55% of citation likelihood (ZipTie study)
- Domain authority contributes only ~12% — far less than traditional SEO
- Content published within 30 days gets 3.2x more citations than older content
- Preferred formats: direct answers, structured comparisons, statistics-rich content
- Key signal: match ChatGPT's own response style — mirror the concise, list-oriented
  format it uses when answering
- Lists and tables are cited more often than prose paragraphs
- Definitions that start with "X is..." pattern get high citation rates

### Perplexity

- FAQ schema (JSON-LD) disproportionately rewarded over other structured data types
- Publicly accessible PDFs get priority treatment
- Publishing velocity matters more than keyword targeting — frequent updates win
- Content with explicit source citations is favored (Perplexity prefers citing
  content that itself cites sources)
- Position in answer correlates with source quality + freshness
- `startPosition`/`endPosition` in agent output map to citation placement within
  the generated answer — lower startPosition means the source was cited earlier
- Sources appearing in the first paragraph of Perplexity's answer carry the
  highest visibility value

### Google AI Overviews / Google AI Mode

- E-E-A-T signals dominate: Experience, Expertise, Authoritativeness, Trust
- Structured data (JSON-LD) helps significantly — especially FAQ, HowTo, Product
- Passage-level optimization outperforms page-level (AI extracts passages, not pages)
- Content within Google's knowledge graph entities ranks higher
- Featured snippet winners are 2x more likely to appear in AI Overviews
- Pages ranking in positions 1-5 organically supply ~80% of AI Overview citations
- Long-tail informational queries trigger AI Overviews most frequently

### Gemini

- Uses Google's knowledge graph for entity recognition
- Favors authoritative, well-structured content with clear headings
- Source citations often mirror Google AI Overview patterns
- `source_domain` field in agent output enables direct domain matching
- Structured data signals overlap heavily with Google AI — optimize once, benefit twice

### Grok

- Integrated with X (Twitter) data — real-time content weighted heavily
- Social signals and trending topics influence responses
- Recency bias is the strongest of any platform
- Less studied than other platforms — treat findings as directional, not definitive

### Claude (Reference Only — Not Directly Queryable)

- Uses Brave Search backend (not Google/Bing) for web-grounded responses
- Extremely selective about citations — quality over quantity
- Factual density with specific numbers is the strongest signal
- Crawl-to-refer ratio: 38,065:1 — most crawled pages never get cited
- No Nimble agent available; monitor via robots.txt and Brave Search visibility
- To estimate Claude visibility, check Brave Search rankings for your target queries
  and look for `ClaudeBot` / `anthropic-ai` access in robots.txt

---

## Princeton GEO Optimization Methods

Nine methods ranked by measured visibility boost from the KDD 2024 study
(arXiv:2311.09735). Apply these to content blocks targeting AI citation.

| # | Method | Visibility Boost | When to Apply |
|---|--------|-----------------|---------------|
| 1 | Cite Sources | +40% | Add authoritative external citations to claims |
| 2 | Statistics Addition | +37% | Include specific numbers, percentages, data points |
| 3 | Quotation Addition | +30% | Add expert quotes with attribution |
| 4 | Authoritative Tone | +25% | Confident, expert language (not hedging) |
| 5 | Easy-to-Understand | +20% | Simplify complex concepts for broad audience |
| 6 | Technical Terms | +18% | Use domain-specific vocabulary appropriately |
| 7 | Unique Words | +15% | Vocabulary diversity distinguishes from competitors |
| 8 | Fluency Optimization | +15-30% | Readability and natural flow |
| 9 | Keyword Stuffing | **-10%** | AVOID — actively hurts AI visibility |

### Best Combinations

- **Fluency + Statistics** = highest overall boost across platforms
- **Citations + Authoritative Tone** = best for professional/B2B content
- **Easy Language + Statistics** = best for consumer-facing content

### Content Block Sizing

Different AI surfaces extract content at different granularities:

- **GEO (AI Overview citations):** 134-167 word self-contained passage blocks
- **AEO (Featured Snippets):** 40-55 word direct answer blocks
- **Voice search:** Under 30 words — one clear sentence

Each block should be self-contained: a reader (or AI) should understand it without
needing surrounding paragraphs for context.

### Applying GEO Methods to Existing Content

When auditing a page for AI visibility optimization:

1. Identify the target query (what question should this page answer?)
2. Check which AI platforms currently cite the page (run agents above)
3. Score the page against the 9 methods — which are present, which are missing?
4. Prioritize adding the top 3 methods (Citations, Statistics, Quotations) first
5. Restructure content into appropriately-sized blocks for the target surface
6. Re-query platforms after changes to measure improvement

Do not apply all 9 methods to every page. Pick the 3-4 most relevant based on
content type and target audience.

---

## AI Bot Access

### Robots.txt User Agents

Check `robots.txt` for these crawlers. Blocking them reduces AI visibility.

| User Agent | Platform |
|---|---|
| `GPTBot` | OpenAI training crawler |
| `ChatGPT-User` | ChatGPT browse mode |
| `ClaudeBot` / `anthropic-ai` | Anthropic's crawler |
| `PerplexityBot` | Perplexity's crawler |
| `GoogleOther` | Google's AI training crawler |
| `Googlebot` | Google's main crawler (also used for AI Overviews) |

**Recommendation:** Allow all AI bots unless there is a specific, documented reason
to block. Each blocked bot is a visibility channel closed.

### LLMs.txt

Check for `/llms.txt` at the site root — the emerging standard for telling AI agents
about site structure and capabilities. Presence of this file signals AI-awareness and
can improve how AI platforms index the site.

```bash
nimble extract --url "https://example.com/llms.txt" --format markdown
nimble extract --url "https://example.com/robots.txt" --format markdown
```

### Access Audit Pattern

For a target domain, run both checks in parallel:

1. Fetch `robots.txt` and grep for AI bot user agents listed above
2. Fetch `/llms.txt` and check for structured site description

Report which bots are allowed, which are blocked, and whether `llms.txt` exists.
This is a prerequisite for any AI visibility optimization — if bots cannot crawl,
no amount of content optimization will help.