# 不应被屏蔽（允许 AI 索引）：
GPTBot         # OpenAI / ChatGPT
PerplexityBot  # Perplexity
ClaudeBot      # Anthropic / Claude
Google-Extended # Google AI Overviews
anthropic-ai   # Anthropic（备用标识）
Applebot-Extended # Apple Intelligence
cohere-ai      # Cohere

User-agent: GPTBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: Google-Extended
Allow: /

If any AI bot is blocked, flag it. That's an immediate visibility killer for that platform.

**robots.txt to allow all AI bots:**

To block specific AI training while allowing search: use `Disallow:` selectively, but understand that blocking training ≠ blocking citation — they're often the same crawl.

### Step 2 — Current Citation Audit

Manually test your target queries on each platform:

| Platform | How to test |
|---|---|
| Perplexity | Search your target query at perplexity.ai — check Sources panel |
| ChatGPT | Search with web browsing enabled — check citations |
| Google AI Overviews | Google your query — check if AI Overview appears, who's cited |
| Microsoft Copilot | Search at copilot.microsoft.com — check source cards |

For each query, document:
- Are you cited? (yes/no)
- Which competitors are cited?
- What content type gets cited? (definition? list? stats?)
- How is the answer structured?

This tells you the pattern that's currently winning. Build toward it.

### Step 3 — Content Structure Audit

Review your key pages against the Extractability Checklist:

- [ ] Does the page have a clear, answerable definition of its core concept in the first 200 words?
- [ ] Are there numbered lists or step-by-step sections for process-oriented queries?
- [ ] Does the page have a FAQ section with direct Q&A pairs?
- [ ] Are statistics and data points cited with source name and year?
- [ ] Are comparisons done in table format (not narrative)?
- [ ] Is the page's H1 phrased as the answer to a question, or as a statement?
- [ ] Does schema markup exist? (FAQPage, HowTo, Article, etc.)

Score: 0-3 checks = needs major restructuring. 4-5 = good baseline. 6-7 = strong.

---

## Mode 2: Content Optimization

### The Content Patterns That Get Cited

These are the block types AI systems reliably extract. Add at least 2-3 per key page.

See [references/content-patterns.md](references/content-patterns.md) for ready-to-use templates for each pattern.

**Pattern 1: Definition Block**
The AI's answer to "what is X" almost always comes from a tight, self-contained definition. Format:

> **[Term]** is [concise definition in 1-2 sentences]. [One sentence of context or why it matters].

Placed within the first 300 words of the page. No hedging, no preamble. Just the definition.

**Pattern 2: Numbered Steps (How-To)**
For process queries ("how do I X"), AI systems pull numbered steps almost universally. Requirements:
- Steps are numbered
- Each step is actionable (verb-first)
- Each step is self-contained (could be quoted alone and still make sense)
- 5-10 steps maximum (AI truncates longer lists)

**Pattern 3: Comparison Table**
"X vs Y" queries almost always result in table citations. Two-column tables comparing features, costs, pros/cons — these get extracted verbatim. Format matters: clean markdown table with headers wins.

**Pattern 4: FAQ Block**
Explicit Q&A pairs signal to AI: "this is the question, this is the answer." Mark up with FAQPage schema. Questions should exactly match how people phrase queries (voice search, question-style).

**Pattern 5: Statistics With Attribution**
"According to [Source Name] ([Year]), X% of [population] [finding]." This format is extractable because it has a complete citation. Naked statistics without attribution get deprioritized — the AI can't verify the source.

**Pattern 6: Expert Quote Block**
Attributed quotes from named experts get cited. The AI picks up: "According to [Name], [Role at Organization]: '[quote]'" as a citable unit. Build in a few of these per key piece.

### Rewriting for Extractability

When optimizing existing content:

1. **Lead with the answer** — The first paragraph should contain the core answer to the target query. Don't save it for the conclusion.

2. **Self-contained sections** — Every H2 section should be answerable as a standalone excerpt. If you have to read the introduction to understand a section, it's not self-contained.

3. **Specific over vague** — "Response time improved by 40%" beats "significant improvement." AI systems prefer citable specifics.

4. **Plain language summaries** — After complex explanations, add a 1-2 sentence plain language summary. This is what AI often lifts.

5. **Named sources** — Replace "experts say" with "[Researcher Name], [Year]." Replace "studies show" with "[Organization] found in their [Year] survey."

### Schema Markup for AI Discoverability

Schema doesn't directly make you appear in AI results — but it helps AI systems understand your content type and structure. Priority schemas:

| Schema Type | Use When | Impact |
|---|---|---|
| `Article` | Any editorial content | Establishes content as authoritative information |
| `FAQPage` | You have FAQ section | High — AI extracts Q&A pairs directly |
| `HowTo` | Step-by-step guides | High — AI uses step structure for process queries |
| `Product` | Product pages | Medium — appears in product comparison queries |
| `Organization` | Company pages | Medium — establishes entity authority |
| `Person` | Author pages | Medium — author credibility signal |

Implement via JSON-LD in the page `<head>`. Validate at schema.org/validator.

---

## Mode 3: Monitoring

AI search is volatile. Citations change. Track them.

### Manual Citation Tracking

Weekly: test your top 10 target queries on Perplexity and ChatGPT. Log:
- Were you cited? (yes/no)
- Rank in citations (1st source, 2nd, etc.)
- What text was used?

This takes ~20 minutes/week. Do it before automated solutions exist (they don't yet, not reliably).

### Google Search Console for AI Overviews

Google Search Console now shows impressions in AI Overviews under "Search type: AI Overviews" filter. Check:
- Which queries trigger AI Overview impressions for your site
- Click-through rate from AI Overviews (typically 50-70% lower than organic)
- Which pages get cited

### Visibility Signals to Track

| Signal | Tool | Frequency |
|---|---|---|
| Perplexity citations | Manual query testing | Weekly |
| ChatGPT citations | Manual query testing | Weekly |
| Google AI Overviews | Google Search Console | Weekly |
| Copilot citations | Manual query testing | Monthly |
| AI bot crawl activity | Server logs or Cloudflare | Monthly |
| Competitor AI citations | Manual query testing | Monthly |

See [references/monitoring-guide.md](references/monitoring-guide.md) for the full tracking setup and templates.

### When Your Citations Drop

If you were cited and suddenly aren't:
1. Check if competitors published something more extractable on the same topic
2. Check if your robots.txt changed (block AI bots = instant disappearance)
3. Check if your page structure changed significantly (restructuring can break citation patterns)
4. Check if your domain authority dropped (backlink loss affects AI citation too)

---

## Proactive Triggers

Flag these without being asked:

- **AI bots blocked in robots.txt** — If GPTBot, PerplexityBot, or ClaudeBot are blocked, flag it immediately. Zero AI visibility is possible until fixed, and it's a 5-minute fix. This trumps everything else.
- **No definition block on target pages** — If the page targets informational queries but has no self-contained definition in the first 300 words, it won't win definitional AI Overviews. Flag before doing anything else.
- **Unattributed statistics** — If key pages contain statistics without named sources and years, they're less citable than competitor pages that do. Flag all naked stats.
- **Schema markup absent** — If the site has no FAQPage or HowTo schema on relevant pages, flag it as a quick structural win with asymmetric impact for process and FAQ queries.
- **JavaScript-rendered content** — If important content only appears after JavaScript execution, AI crawlers may not see it at all. Flag content that's hidden behind JS rendering.

---

## Output Artifacts

| When you ask for... | You get... |
|---|---|
| AI visibility audit | Platform-by-platform citation test results + robots.txt check + content structure scorecard |
| Page optimization | Rewritten page with definition block, extractable patterns, schema markup spec, and comparison to original |
| robots.txt fix | Updated robots.txt with correct AI bot allow rules + explanation of what each bot is |
| Schema markup | JSON-LD implementation code for FAQPage, HowTo, or Article — ready to paste |
| Monitoring setup | Weekly tracking template + Google Search Console filter guide + citation log spreadsheet structure |

---

## Communication

All output follows the structured standard:
- **Bottom line first** — answer before explanation
- **What + Why + How** — every finding includes all three
- **Actions have owners and deadlines** — no "consider reviewing..."
- **Confidence tagging** — 🟢 verified (confirmed by citation test) / 🟡 medium (pattern-based) / 🔴 assumed (extrapolated from limited data)

AI SEO is still a young field. Be honest about confidence levels. What gets cited can change as platforms evolve. State what's proven vs. what's pattern-matching.

---

## Related Skills

- **content-production**: Use to create the underlying content before optimizing for AI citation. Good AI SEO requires good content first.
- **content-humanizer**: Use after writing for AI SEO. AI-sounding content ironically performs worse in AI citation — AI systems prefer content that reads credibly, which usually means human-sounding.
- **seo-audit**: Use for traditional search ranking optimization. Run both — AI SEO and traditional SEO are complementary, not competing. Many signals overlap.
- **content-strategy**: Use when deciding which topics and queries to target for AI visibility. Strategy first, then optimize.