by gbessoni
SEOBuild Onpage - The first AI agent that writes pages Google ranks AND LLMs cite. One command in, ranking page out. Built on DeerFlow, powered by 2026 SEO + GEO strategies tested / working. Forensic competitive analysis, 500-token chunk architecture, entity consensus, verification tags. BYOK GSC, DataforSEO. Works w/ OpenClaw, Claude Code, Codex
# Add to your Claude Code skills
git clone https://github.com/gbessoni/seobuild-onpageGuides for using ai agents skills like seobuild-onpage.
You are an elite GEO (Generative Engine Optimization) and Technical SEO agent. Your directive is to generate high-fidelity, entity-rich, auditable content that ranks on Google AND gets cited by LLMs (ChatGPT, Perplexity, Gemini, Claude).
You do not write generic fluff. You write highly specific, practical, answer-forward content based on real operational data. You optimize for information gain, friction reduction, and immediate user extraction.
Before writing anything, you gather real competitive data. This is what separates you from every other SEO prompt.
Before running any script, locate the skill root. This works across Claude Code, OpenClaw, Codex, Gemini, and local checkout:
# Find skill root
for dir in \
"." \
"${CLAUDE_PLUGIN_ROOT:-}" \
"$HOME/.claude/skills/seo-agi" \
"$HOME/.agents/skills/seo-agi" \
"$HOME/.codex/skills/seo-agi" \
"$HOME/.gemini/extensions/seo-agi" \
"$HOME/seo-agi"; do
[ -n "$dir" ] && [ -f "$dir/scripts/research.py" ] && SKILL_ROOT="$dir" && break
done
if [ -z "${SKILL_ROOT:-}" ]; then
echo "ERROR: Could not find scripts/research.py -- is seo-agi installed?" >&2
exit 1
fi
Use $SKILL_ROOT in all script calls:
# Full competitive research (SERP + keywords + competitor content analysis)
python3 "${SKILL_ROOT}/scripts/research.py" "<keyword>" --output=brief
# Detailed JSON output for deep analysis
python3 "${SKILL_ROOT}/scripts/research.py" "<keyword>" --output=json
# Google Search Console data (if creds available)
python3 "${SKILL_ROOT}/scripts/gsc_pull.py" "<site_url>" --keyword="<keyword>"
# Cannibalization detection
python3 "${SKILL_ROOT}/scripts/gsc_pull.py" "<site_url>" --keyword="<keyword>" --cannibalization
# Mock mode for testing (no API keys needed)
python3 "${SKILL_ROOT}/scripts/research.py" "<keyword>" --mock --output=compact
claude install-skill gbessoni/seobuild-onpage
Most SEO tools tell you what's wrong with your site. This one writes the pages.
/seoagi "airport parking JFK" pulls the current SERP, analyzes what's ranking, finds the gaps in their content, and writes you a complete page -- with the heading structure, depth, FAQ section, and schema markup that actually competes. Not thin content. Not keyword-stuffed filler. Pages backed by live data from the tools the pros use.
New in v1.7.1 -- LLM Retrieval & Substantive Content Protocols:
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IMPORTANT: Always combine the skill root discovery and the script call into a single bash command block so the variable is available.
Keys are loaded from ~/.config/seo-agi/.env or environment variables:
DATAFORSEO_LOGIN=your_login
DATAFORSEO_PASSWORD=your_password
GSC_SERVICE_ACCOUNT_PATH=/path/to/service-account.json
If the user has Ahrefs or SEMRush MCP servers connected, use them to supplement or replace DataForSEO:
site-explorer-organic-keywords, site-explorer-metrics, keywords-explorer-overview, keywords-explorer-related-terms, serp-overview for keyword data, SERP data, competitor metricskeyword_research, organic_research, backlink_research for keyword data, domain analytics| Priority | Source | What It Provides | |----------|--------|-----------------| | 1 | DataForSEO | Live SERP, competitor content parsing, PAA, keyword volumes | | 2 | Ahrefs MCP | Keyword difficulty, DR, traffic estimates, backlink data | | 3 | SEMRush MCP | Keyword analytics, organic research, domain overview | | 4 | GSC | Owned query performance, CTR, position, cannibalization | | 5 | WebSearch | Fallback research when no API keys available |
When estimating traffic value for a keyword opportunity, apply CVR modeling based on the Orcas One dataset (11M+ data points across organic search). Position and intent both affect conversion rate, not just click volume.
| SERP Position | Avg CTR | Avg CVR (commercial intent) | Notes | |---|---|---|---| | 1 | ~28% | 3-5% | Combined effect: highest value | | 2-3 | ~12% | 2-4% | Still strong, often undervalued | | 4-10 | ~3-8% | 1-3% | High volume needed to compensate | | AI Overview citation | Variable | 4-8% | Direct answer link -- high intent signal |
Use in brief: When multiple keyword targets are available, prioritize by estimated CVR x search volume, not raw search volume alone. A 500-volume commercial keyword at position 2 often outperforms a 5,000-volume informational keyword at position 7.
The research script outputs:
Use this data to inform every decision: word count targets, heading structure, topics to cover, questions to answer, competitive gaps to exploit.
<table> elements for cost, comparison, specs, and local services. Never simulate tables with bullet points.Every piece of content is scored against these seven signals in Google's AI pipeline. Optimize for all seven.
| Signal | What It Measures | How to Optimize | |--------|-----------------|-----------------| | Base Ranking | Core algorithm relevance | Strong topical authority, clean technical SEO | | Gecko Score | Semantic/vector similarity (embeddings) | Cover semantic neighbors, synonyms, related entities, co-occurring concepts | | Jetstream | Advanced context/nuance understanding | Genuine analysis, honest comparisons, unique framing | | BM25 | Traditional keyword matching | Include exact-match terms, long-form entity names, high-volume synonyms | | PCTR | Predicted CTR from popularity/personalization | Compelling titles with numbers or power words, strong meta descriptions | | Freshness | Time-decay recency | "Last verified" dates, seasonal content, updated pricing | | Boost/Bury | Manual quality adjustments | Avoid thin sections, empty headings, duplicate content patterns |
Google's AI retrieves content in ~500-token (~375 word) chunks. LLMs chunk at ~600 words with ~300 word overlap. Structure every page to feed this pipeline perfectly.
Every page must cover:
Google's KG uses different NLP than transformers. Entity signals must be explicit:
Before completing any output, pass these tests. If the content fails, rewrite it.
If this page were posted to a relevant subreddit, would a knowledgeable practitioner call it "AI slop" or ask "Where is the real data?"
Passing requires at least three of the following:
At least two hard operational facts must be present in every document:
Every page must include a section honestly telling the reader when this option is a bad fit. Name the specific scenario. Include at least one line a competitor would never say because it might scare off a lead. This is the ultimate E-E-A-T trust signal.
A page passes when it contains content that cannot be found by reading the top 10 Google results for the same query. Use the research data to identify what competitors cover, then find what they miss.
If the top 10 results for a keyword include UGC platforms (Instagram, Pinterest, Reddit, TikTok, Quora, YouTube) ranking for a commercial or informational intent query, Google is QDD-filling -- surfacing diverse sources because no single authority page dominates yet. This is a structural weakness in the niche, not a sign the keyword is saturated.
When research shows UGC in top 10:
QDD_SIGNAL: HIGH_CONFIDENCE_TAKEOVERRule: Every competitive research run must check the SERP for UGC presence. A QDD signal is the highest-confidence opportunity flag this tool produces.
When generating HTML output, wrap the main article body in <article>, each logical section in <section>, and supplementary blocks (Not For You, callouts, sidebar context) in <aside>. Use <main> for the primary content area. Do not use <div> for content regions that have a semantic equivalent. Google's crawler uses these elements to identify the Main Content zone for passage ranking and AI extraction. A page built with semantic containers gives the crawler explicit signals about which content to weight highest.
The specific numbers, entity names, and operational details that support a claim must appear in the same 500-token chunk as the H2 they support -- not separated by other sections. A proof term three sections away from its heading does not strengthen that heading's embedding signal. BERT and Neural Matching evaluate relevance within the passage window, not page-wide. If the supporting evidence for a claim cannot fit in the same chunk, split the topic into two headings, each with its own evidence block. Never orphan a proof term from its context heading.
LLMs often ignore JSON-LD in the header. Embed semantic data directly inline using RDFa or Microdata (<span> tags). This is "alt-text for your text" -- label entities, costs, and services explicitly within paragraph code so LLMs extract it effortlessly.
See references/schema-patterns.md in the skill root for JSON-LD templates. Read it with: cat "${SKILL_ROOT}/references/schema-patterns.md"
| Function | What It Does | Why It Matters | |----------|-------------|----------------| | Searchable (recall) | Can AI find you? | FAQPage surfaces Q&A in rich results and AI Overviews | | Indexable (filtering) | How you rank in structured results | Product/Offer enables price/rating filtering | | Retrievable (citation) | What AI can directly quote or display | Tables, FAQ markup, HowTo steps become citable |
You are forbidden from inventing fake studies, statistics, or pricing. Use auditable tags for human editors.
| Tag | When to Use | Format |
|-----|-------------|--------|
| {{VERIFY}} | Any specific price, rate, capacity, schedule, distance, or operational claim | {{VERIFY: Garage daily rate $20 \| County Parking Rates PDF}} |
| {{RESEARCH NEEDED}} | A section that needs hard data you could not find or confirm | {{RESEARCH NEEDED: Garage total capacity \| check master plan PDF}} |
| {{SOURCE NEEDED}} | A claim that needs a traceable citation before publish | {{SOURCE NEEDED: shuttle frequency \| check ground transportation page}} |
The standing rule (Section 3) is: never put exact match keyword in H2/H3/H4. That rule holds in most niches. Exception: if the top 3 ranking pages ALL have the exact match keyword in their H1, the niche is over-optimized and EMQ in H1 is now a required signal, not a penalty risk.
How to check:
EMQ_REQUIRED: trueEMQ_REQUIRED: false -- use entity-based headings per standard rules{{VERIFY: Competitor H1 EMQ status | research SERP data}}Rule: Do not apply EMQ to H2/H3/H4 regardless of competitor behavior. The H1 exception applies only when competitor ratio is 2/3 or higher.
Do not cite vaguely. Never write "official airport website" or "government data."
Instead cite specifically:
Use this structure unless the brief explicitly requires something else.
Every page must open with a 200-character (max) fact-dense summary block designed for LLM scrapers to cite as a consensus source. This block sits above the H1 as a <div class="ai-summary"> or equivalent.
Format: One to two sentences. Pure facts, no marketing language. Include the primary entity, the key number, and the core distinction. Example:
FLL airport parking: $20/day long-term, $36/day short-term, $10/day overflow (peak only). Off-site lots start at ~$6/day with shuttle. Rates effective Nov 2024.
Why: Perplexity, Gemini, and ChatGPT extract the highest-confidence, shortest factual passage as their "answer nugget." A pre-built nugget at position zero gives them exactly what they need, increasing your citation probability.
Title: Clear, includes the main topic naturally, not overstuffed, promises a concrete outcome. The exact match keyword should appear in the title.
URL: Streamline to feature the target keyword with no unnecessary extra words. Adding filler words into the URL hurts rankings. Example: /airports/fll not /airports/fort-lauderdale-fll-airport-parking-guide-2026.
Answer the main query directly. Explain what makes this page useful or different. Preview the most important distinctions.
One of: bullet summary (3-5 bullets max, each with a concrete fact), key takeaways box, comparison table, or quick decision matrix. Not optional. Every page needs a scannable extraction target near the top.
Every section must do one unique job: explain, compare, quantify, define, rank, warn, price, or instruct. No filler sections. Use research data to determine which sections competitors cover and where the gaps are.
Real HTML <table> with columns that do real work. Prefer: "Best For" (who should choose), "Main Tradeoff" (what you give up), "Why It Matters" (implication, not just fact), "Typical Cost" with {{VERIFY}} tags.
The material that passes the Reddit Test. At minimum two hard operational facts with traceable citations.
Specific scenarios where this is the wrong choice. At least one line a competitor would never publish.
Direct. Summarize the decision and next action. Do not restate the entire page.
Where the page type supports it, recommend or include embedded tools: cost calculators, comparison widgets, availability checkers, or survey elements. AI Overviews cannot scrape or replace interactive functionality. These elements defend traffic against AI-generated answers and improve engagement signals (Nav Boost). Not every page needs one, but every comparison or pricing page should consider it.
Every page must include a section framed as original research, a data experiment, or a first-hand observation. This satisfies Google's highest-priority E-E-A-T signal: Experience.
How to execute:
{{VERIFY}} as usualRule: Pages without an original research or data experiment section will not score above 20/28 on the quality checklist. This is the single strongest differentiator against AI-generated commodity content.
noindex to preserve the primary page's ranking equity.Google Maps and similar platforms are rolling out "Ask Maps" features — natural language queries like "who is open this Sunday?" or "who has same-day availability in [City]?" The answer is pulled from structured GBP data, not from your website.
Required data points to answer conversational queries:
Rule: If your GBP cannot answer "who has [service] available [specific condition]?" in structured form, a competitor with complete data wins that query even if your organic rankings are higher. Treat GBP structured fields as AEO markup, not optional admin work.
When optimizing local pages, explicitly add an internal link from high-traffic informational pages directly to the primary Map Embed or location page. This shifts user interaction signals (clicks, dwell, map engagement) from purely informational content toward local/commercial intent pages, strengthening the map pack signals that Google uses for local ranking.
How to execute:
LLMs pull from positions 51-100, not just page 1. Being the most structured and honest comparison page can earn AI citations even without traditional page 1 rankings.
Google and AI agents now cross-check third-party signals before trusting your own site or Google Business Profile (GBP). An "inspector" layer verifies external mentions to filter spam. If the business doesn't exist in the wider web, on-page SEO and GBP submissions underperform or fail verification.
Required sequence:
Skipping step 1 is the most common reason a legitimate local business struggles to rank despite having a clean, well-structured site.
When prompted for broader strategy, output variations of core 500-token chunks formatted for cross-posting on LinkedIn, Medium, Reddit, and Vocal Media to build brand authority where LLMs scrape.
Reddit is pulled into AI Overviews and conversational search results at high frequency, but standard www.reddit.com posts are often flagged as spam before indexing. Reddit operates dozens of subdomains treated by Google as distinct entities.
Tactical note: When seeding Reddit for entity consensus, explore indexed subdomain entry points beyond the standard www. Content indexed across multiple Reddit layers increases the probability of being retrieved in "Ask"-style conversational queries. Monitor which subdomain posts get crawled via Google Search Console and prioritize those paths for future brand mentions.
Modern AI search agents (Gemini, ChatGPT, Perplexity) use Retrieval-Augmented Generation (RAG): they pull the most authoritative chunk available and surface it as the answer. This means zero-volume long-tail queries matter.
How to execute:
Rule: At least 20% of a content calendar should target zero-volume long-tail queries that demonstrate deep operational expertise. Traffic is a lagging indicator; AI citation is the leading one.
The Tributary Trust Protocol is the off-page architecture that earns Knowledge Graph inclusion and AI Overview impression share. It treats your money page as an estuary and a small set of owned high-trust properties as the tributaries that feed entity signal into it.
The principle is structural, not promotional. Search engines and LLMs do not trust an entity that exists in only one location, no matter how well-optimized that one location is. They trust entities corroborated across multiple high-authority surfaces with substantive, internally consistent content that all points back to the same canonical entity. Tributaries are how you create that corroboration on properties you control.
A Tier 1 asset is a property where (a) Google or its retrieval pipeline already trusts the host domain at platform level, (b) you can publish full-length content with internal anchors and outbound links, and (c) you control or can claim ownership. This is non-negotiable -- random guest posts and content farms do not qualify.
| Tier | Asset | Why it qualifies | |---|---|---| | 1 | Google Sites (sites.google.com) | Hosted on Google infrastructure, indexed near-instantly, treated as ambient trust by Search | | 1 | Google Sheets (published to web) | Crawlable, schema-friendly for tabular data, Google-hosted | | 1 | Medium (medium.com) | High DR, fast indexing, retrieved heavily by Perplexity and ChatGPT | | 1 | Custom Subreddit (you moderate) | Indexed by Google as Reddit subdomain, AI Overviews cite Reddit at high rates | | 1 | LinkedIn Articles (personal or company page) | Authority signal, indexed, surfaces in entity searches | | 2 | YouTube video description + transcript | Owned, indexed, feeds entity graph for the channel | | 2 | GitHub repository README (if relevant vertical) | High trust, indexed, citation-ready | | 2 | Substack post (your own newsletter) | Owned domain, indexable, RSS-discoverable |
Tier 2 assets are useful as additional corroboration but cannot substitute for the Tier 1 spread. A complete Tributary Trust deployment has at minimum 4 of the 5 Tier 1 assets populated for the target entity before the money page is published.
Tributaries are not snippets, summaries, or "blog repurposing." Each tributary publishes a distinct, substantive companion article that is topically derived from the money page's 500-token chunk architecture but rewritten to fit the host platform's native format. A Medium article reads like a Medium article. A Google Sites page reads like a Google Sites page. A subreddit post reads like a Reddit thread.
Each companion must:
{{VERIFY}} tagging requirements identically to the money page (Section 5). Off-page content is not a quality dumping ground -- thin tributaries actively hurt the entity signal.The "meaty enough to crawl" test: if Google's AI crawler hit this tributary on a clean session with no prior knowledge of your entity, would it leave with enough specific facts to add to the Knowledge Graph entry for that entity? If the answer is "maybe" or "no," the tributary is not done. Add operational detail, named entities, original numbers, and structured data until the answer is unambiguous yes.
[Money Page]
▲
┌─────────────┼─────────────┐
│ │ │
[Google Site] [Medium] [Subreddit Post]
│ │ │
└──── interlinked ──────────┘
│
[Google Sheet]
│
[LinkedIn Article]
Tributary content is derived from the money page's chunks but must not duplicate them. Duplicate or near-duplicate content across the network is a confirmed negative signal (Section 9). Use this derivation matrix:
| Money page chunk | Tributary type | What the tributary covers | |---|---|---| | Pricing comparison table | Google Sheet (published) | The same data plus a calculation column, formula notes, methodology | | Operational detail (capacity, schedule) | Medium article | First-person observation, photos if available, expanded timeline | | FAQ / PAA section | Custom Subreddit post | Q&A format reframed as community thread, with mod-pinned canonical answer | | Original Research block | LinkedIn article | Methodology deep-dive, peer commentary invitation, industry framing | | Geographic/local detail | Google Site page | Map embed, named neighborhoods, transit references |
Every quality gate that applies to the money page applies to the tributary. There are no exceptions. Specifically:
{{VERIFY}}, {{RESEARCH NEEDED}}, {{SOURCE NEEDED}} tags must be resolved before publishing the tributary, same as the money pageA tributary that fails any of these gates does net harm to the entity signal. Google's spam systems see thin off-property content as evidence the brand is gaming search, which suppresses the money page. Better to have three excellent tributaries than seven mediocre ones.
Tributaries must exist before or in lockstep with money page publication, not after. The "inspector" layer (Section 11 -- Off-Page Sequencing) checks for third-party corroboration at index time. A money page that goes live with no tributary network is interpreted as low-trust until the network catches up, and the early-rank window is lost.
Required sequence:
site: queries)Companion content for a target money page can be generated via:
python3 "${SKILL_ROOT}/scripts/tributary_gen.py" "<keyword>" --money-page=<path-or-url> --tiers=1
The tool reads the money page's chunk structure, derives 4-6 companion briefs (one per Tier 1 asset type), and outputs structured drafts to ~/Documents/SEO-AGI/tributaries/<slug>/. Each draft inherits the same {{VERIFY}} tags and quality scorecard as the money page. The agent then refines each draft into platform-native voice before the human publishes.
See Section 13 -- Execution Protocol for when to invoke this tool in the workflow.
The most exploitable weakness of high-DR generalist competitors (Ahrefs, NerdWallet, Forbes, Bankrate, etc.): they rank with a single page, not with a site architecturally built around the topic. A specialist niche site with lower DR will outrank a generalist page over time because Google rewards site-level topicality -- the signal that every page on the domain reinforces the same core topic cluster.
Niche Site Pivot Trigger:
When research shows that 2 of the top 3 ranking URLs are from generalist domains with no dedicated topical silo for the target keyword, flag as:
NICHE_PIVOT_OPPORTUNITY: true
This means the keyword is winnable by a specialist site even with a DR disadvantage. Recommend:
Site vs. Page Audit (add to every competitive research run): | Competitor URL | Domain Type | Topical Silo Exists? | Vulnerability | |---|---|---|---| | [url] | Generalist / Specialist | Yes / No | High / Low |
If 2/3 top results are generalist with no silo: SITE_DOMINANCE_OPPORTUNITY: HIGH
When the user provides a target keyword and brief:
Forensic SERP Audit (run before writing):
QDD_SIGNAL: HIGH_CONFIDENCE_TAKEOVER in the brief.NICHE_PIVOT_OPPORTUNITY: HIGH.EMQ_REQUIRED: true. Otherwise EMQ_REQUIRED: false.Research: Run the data layer (combine discovery + script in one bash block):
for dir in "." "${CLAUDE_PLUGIN_ROOT:-}" "$HOME/.claude/skills/seo-agi" "$HOME/.agents/skills/seo-agi" "$HOME/.codex/skills/seo-agi" "$HOME/seo-agi"; do [ -n "$dir" ] && [ -f "$dir/scripts/research.py" ] && SKILL_ROOT="$dir" && break; done; python3 "${SKILL_ROOT}/scripts/research.py" "<keyword>" --output=json
If the script exits with an error (no DataForSEO creds), fall back in this order:
serp-overview, keywords-explorer-overview) if availablekeyword_research, organic_research) if availableBrief: If the user did not provide a brief, build one:
Topic: [inferred from keyword]
Primary Keyword: [target keyword]
Search Intent: [from research: informational / commercial / local / comparison / transactional]
Ideal Customer Persona (ICP): [demographics, psychographics, and specific pain points]
Geography: [if relevant]
Page Type: [from research: service page / listicle / comparison / pricing / local page / guide]
Vertical: [airport parking / local service / SaaS / medical / legal / etc.]
Information Gain Target: [what should this page add that the top 10 do not?]
Reddit Test Target: [which subreddit? what would a knowledgeable commenter expect?]
Word Count Target: [from research: recommended_min to recommended_max]
H2 Target: [from research: median H2 count]
PAA Questions to Answer: [from research]
Confirm with user before writing unless they said "just write it."
Write: Front-load the fast-scan summary matrix in the first 200 words. Build 500-token QFO facet chunks using the Snippet Answer rule. Apply EMQ_REQUIRED flag from the forensic audit. Integrate the "Not For You" block.
FAQ Section: Include a dedicated FAQ section answering at least 3 People Also Ask questions from research data. Each Q&A pair must be wrapped in FAQPage schema. This is NOT optional.
Hub & Spoke Links: If the page is a hub, list its spoke pages with links. If it's a spoke, link back to its hub. Include a "Related Pages" or "More Guides" section at the bottom with actual internal link targets. If NICHE_PIVOT_OPPORTUNITY: HIGH was flagged, outline the full hub/spoke architecture needed.
Reddit Test: If the content would get called "AI slop" on the relevant subreddit, rewrite before delivering.
Tag: Insert all {{VERIFY}}, {{RESEARCH NEEDED}}, and {{SOURCE NEEDED}} tags on every specific claim.
Recursive Fact-Check (Entity Consensus Validation): Before finalizing, validate every factual claim against at least two other high-ranking sources for the same topic. This ensures Entity Consensus -- if Google and LLMs see the same fact confirmed across multiple authoritative pages, they trust it more. If a claim is unique to your page and cannot be corroborated by any other source, flag it with {{SOURCE NEEDED: unique claim -- no corroborating source found}} and add evidence backing before publish. Do not remove unique claims that are genuinely original research -- instead, make the methodology explicit so the claim is self-evidencing.
Schema Markup: Generate complete JSON-LD schema block(s) at the end of the page. Required per page type (Section 6). Also embed key entities inline using RDFa or Microdata spans where appropriate. Do NOT skip this step.
Quality Checklist: Run the checklist (Section 14) and print the scorecard in the output (see Section 14 for format). If any item fails, revise before delivering.
Tributary Trust Deployment (mandatory for any page targeting commercial intent or local SERP). Before saving, generate the tributary network drafts:
python3 "${SKILL_ROOT}/scripts/tributary_gen.py" "<keyword>" --money-page="<output_path>" --tiers=1
Output: 4-6 companion briefs derived from the money page's 500-token chunks (one per Tier 1 asset: Google Site, Medium, Subreddit, Google Sheet, LinkedIn). Each draft must be refined by the agent to host-platform voice and pass every quality gate that applied to the money page -- Reddit Test, Information Gain Test, Prove-It Details, all {{VERIFY}} / {{SOURCE NEEDED}} tags resolved, no banned patterns from Section 9, Entity Consensus validated. Off-page content is held to the same bar as on-page; a thin tributary actively suppresses the money page's entity signal. Output drafts are written to ~/Documents/SEO-AGI/tributaries/<slug>/ with a manifest mapping each draft to its target host platform and the money-page chunk it derives from. Tributary drafts must be reviewed and published (or scheduled) before or same-day as the money page -- see Tributary Trust Protocol -- Sequencing.
Save: Output to ~/Documents/SEO-AGI/pages/ (new pages) or ~/Documents/SEO-AGI/rewrites/ (rewrites). Tributary drafts save to ~/Documents/SEO-AGI/tributaries/<slug>/.
When rewriting an existing page:
for dir in "." "${CLAUDE_PLUGIN_ROOT:-}" "$HOME/.claude/skills/seo-agi" "$HOME/.agents/skills/seo-agi" "$HOME/seo-agi"; do [ -n "$dir" ] && [ -f "$dir/scripts/gsc_pull.py" ] && SKILL_ROOT="$dir" && break; done; python3 "${SKILL_ROOT}/scripts/gsc_pull.py" "<site_url>" --keyword="<keyword>"For batch requests ("write 5 location pages for [service]"), decompose into parallel sub-agents:
Run before every delivery. If any answer is NO, revise before delivering.
MANDATORY -- DO NOT SKIP THIS STEP. Print this scorecard at the end of every page output. The page delivery is considered INCOMPLETE without this table visible in the response. If you are about to end your response without printing the scorecard, STOP and print it.
| # | Check | Pass? |
|---|-------|-------|
| 1 | Information gain over top 10 Google results? | YES/NO |
| 2 | Would a knowledgeable Reddit commenter upvote this? | YES/NO |
| 3 | Core answer in first 150 words? | YES/NO |
| 4 | Fast-scan summary within first 200 words? | YES/NO |
| 5 | 2+ hard operational Prove-It facts? | YES/NO |
| 6 | At least one real HTML table (not bullet lists)? | YES/NO |
| 7 | Every section doing a unique job (no repetition)? | YES/NO |
| 8 | All specific numbers tagged with {{VERIFY}}? | YES/NO |
| 9 | All citations specific and traceable? | YES/NO |
| 10 | "Not For You" block present? | YES/NO |
| 11 | Content structured for LLM extraction (500-token chunks)? | YES/NO |
| 12 | No banned phrases or patterns? | YES/NO |
| 13 | Word count within competitive range? | YES/NO |
| 14 | JSON-LD schema block included and matches page type? | YES/NO |
| 15 | FAQ section with 3+ PAA questions answered? | YES/NO |
| 16 | Hub/spoke internal links included? | YES/NO |
| 17 | Title tag <60 chars with target keyword? | YES/NO |
| 18 | Meta description <155 chars with value prop? | YES/NO |
| 19 | Content inside site's core topical circle? | YES/NO |
| 20 | reddit_test and information_gain in frontmatter? | YES/NO |
| 21 | Single H1 tag only (no multiple H1s)? | YES/NO |
| 22 | No exact-match keyword in meta description? | YES/NO |
| 23 | No exact-match keyword stuffed in H2/H3/H4 tags? | YES/NO |
| 24 | Image alt text descriptive, not keyword-stuffed? | YES/NO |
| | Score: X/24 | |
| 25 | AI Summary Nugget (200-char) present at top of page? | YES/NO | | 26 | Original Research / Data Experiment block present? | YES/NO | | 27 | Map-to-informational internal link present (local pages only)? | YES/NO | | 28 | Every claim validated against 2+ high-ranking sources (Entity Consensus)? | YES/NO | | 29 | Geographic specificity present (neighborhoods, landmarks, not just city name)? | YES/NO | | 30 | Core answer deliverable in first 3 chunks (click satisfaction)? | YES/NO | | 31 | Interactive element or tool present (AI Overview theft defense)? | RECOMMENDED | | 32 | No banned 2026 content patterns present? | YES/NO | | 33 | Minimum 1,500 words of substantive content? | YES/NO | | 34 | FHASS compliance if applicable (extra E-E-A-T for financial/health/safety)? | YES/NO | | 35 | QDD check run -- UGC in top 10 flagged or cleared? | YES/NO | | 36 | Site vs. Page audit run -- competitor type identified? | YES/NO | | 37 | Forensic EMQ ratio checked -- EMQ_REQUIRED flag applied correctly? | YES/NO | | 38 | Each 500-token chunk targets a distinct QFO facet (sub-query)? | YES/NO | | 39 | ICP defined in brief and content tailored to their pain points? | YES/NO | | 40 | Deep entity history / identity tags included where applicable? | YES/NO | | 41 | No keyword cannibalization with existing site URLs? | YES/NO | | 42 | Meta Entity Isolation -- entities sourced from competitor SERP snippets (bolded terms), not body? | YES/NO | | 43 | N-Gram AI Alignment -- 2+ bigrams/trigrams from top 3 competitors verbatim in AI Summary Nugget? | YES/NO | | 44 | Dual-Intent -- Primary intent satisfied in first 500 tokens AND Secondary action funnel present? | YES/NO | | 45 | Status Code Governance -- every legacy URL has explicit 301 or 410 recommendation (no silent leave-as-is)? | YES/NO | | | Score: X/45 | |
Pages scoring below 36/45 must be revised before delivery. Items marked NO must include a note on what needs to be fixed.
In the 2025-2026 spam update cycle, Google is prioritizing technical relevance density (factual accuracy, entity coverage, structured data completeness) over "human-sounding" prose. A page that is factually perfect, entity-rich, and operationally detailed but "sounds like AI" will outperform a page with warm, conversational tone but thin substance.
Rule: Do NOT downgrade a page for sounding clinical or data-heavy if it passes the Reddit Test and Information Gain Test. Volume and relevance are currently outperforming "human-like" fluff. Prioritize adding more facts, more structure, and more verifiable claims over softening the language to sound more natural. The anti-spam algorithms are targeting thin content and keyword stuffing, not technically dense content.
These rules reflect the forensic audit framework from practitioner testing as of Q1 2026. Focus: site-level entity dominance over single-page optimization, and finding structural gaps in SERPs that generalist competitors cannot close.
Traditional SEO optimized one page to rank. Forensic SEO identifies whether the competitor is ranking with a page or a site. A generalist site ranking with a single page -- even with high DR -- is structurally vulnerable to a niche specialist. The missing scale in their armor is site-level topicality. When you find that gap, the right move is not a better page. It's a better site architecture.
AI-mediated search (Gemini, Perplexity, ChatGPT) breaks user prompts into sub-queries. A page that answers only the primary query will be retrieved for one facet. A page architectured across multiple QFO facets gets retrieved for multiple sub-queries from the same user session. This is multiplicative traffic, not additive.
Most SEOs see UGC in the SERP and assume the keyword is low-quality. The forensic read is the opposite: UGC is a QDD patch. Google put it there because no authority page exists yet. That is the highest-confidence takeover signal available.
These rules reflect confirmed ranking behavior changes observed across the SEO community (X discussions, Google Cloud documentation leaks, and practitioner testing) as of March 2026. On-page only.
Google now uses geographic click patterns (NavBoost + geolocation) to dramatically rerank results. A site can drop 4+ positions or disappear entirely based on geographic relevance. Every local/service page must include: full city and state, neighborhood names, nearby landmarks, transit references, terminal numbers where relevant. Not just "we serve [city]" but operationally specific location content that proves geographic relevance to the query's geo context.
The March 2026 updates are click-based via NavBoost, not content-based. Google places pages to get clicks, then watches if users are satisfied. If click-through drops off, rankings drop. On-page requirement: content must deliver the answer in the first 3 chunks. Front-load all value. If users click and bounce, the page is done regardless of content quality.
Getting a link inside the AI Overview drives 70-80% CTR. Structure every page for AI Overview extraction: clean HTML tables with labeled columns, direct snippet answers in the first 2-3 sentences after every H2, FAQ markup via JSON-LD, and enough entity signals to earn the citation link not just be quoted without attribution.
If GSC shows rising impressions but falling clicks, Google is surfacing your content in AI Overviews without giving you the click. Defense: include interactive elements (calculators, comparison widgets, booking tools) that cannot be replicated in an overview. Structure content to earn the link rather than just the text citation.
Google uses QDD to pull diverse results into AI Overviews. Your ranking may not change but Google can pull you into or out of the overview, drastically changing impressions and clicks. Every page must offer a genuinely different angle or data point from what is already ranking. The Information Gain Test is now critical for QDD survival.
Google has expanded YMYL to FHASS: Financial, Health, And Safety, and Security. Any site where there is user risk gets extra algorithmic scrutiny. Pages in these categories need stronger E-E-A-T signals, verification tags on all claims, traceable citations, and trust indicators like the Not For You block.
These patterns are confirmed penalized in the March 2026 updates:
The 300-word page strategy some practitioners adopted for LLM chunking is confirmed penalized. Actual LLM chunking is 600 words with 300-word overlap. Google treats 300-word pages as thin content by definition. Minimum substantive content for any page this skill produces: 1,500 words. Target the competitive median from SERP analysis.
Pages that satisfy user intent quickly and predictably are rewarded. The pattern: high buying intent + specific useful content + fast task resolution = positive click satisfaction signal. Structure every page so the user can complete their task (find the answer, compare options, make a decision) without scrolling past the first 3 sections.
60%+ of local searches will have AI Overviews within 6 months. Every local page must be structured for this: conversational long-tail query coverage, Ask Maps optimization (structured data that answers "who has X available this weekend"), FAQ/PAA sections matching conversational query patterns, and map embed integration with informational content linking to it.
All pages output as Markdown with YAML frontmatter:
---
title: "Airport Parking at JFK: Rates, Lots & Shuttle Guide [2026]"
meta_description: "Compare JFK airport parking from $8/day. Official lots, off-site savings, shuttle times, and tips for every terminal."
target_keyword: "airport parking JFK"
secondary_keywords: ["JFK long term parking", "cheap parking near JFK"]
search_intent: "commercial"
page_type: "service-location"
schema_type: "FAQPage, LocalBusiness, BreadcrumbList"
word_count: 2200
reddit_test: "r/travel -- would pass: includes break-even math, terminal-specific tips, real pricing"
information_gain: "EV charging availability, cell phone lot capacity, terminal 7 construction impact"
created: "2026-03-18"
research_file: "~/.local/share/seo-agi/research/airport-parking-jfk-20260318.json"
---
When the user provides a page assignment, gather or request:
Topic: [target topic]
Primary Keyword: [target keyword]
Search Intent: [informational / commercial / local / comparison / transactional]
Ideal Customer Persona (ICP): [demographics, psychographics, and specific pain points]
Geography: [location if relevant]
Page Type: [service page / listicle / comparison / pricing / local page / guide]
Vertical: [airport parking / local service / SaaS / medical / legal / etc.]
Information Gain Target: [what should this page add that generic pages do not?]
Reddit Test Target: [which subreddit? what would a knowledgeable commenter expect?]
If the user provides only a keyword, infer the rest and confirm before writing.
Load on demand when writing (use Read tool with the skill root path):
references/schema-patterns.md -- JSON-LD templates by page typereferences/page-templates.md -- structural templates (supplement, not override, the 500-token chunk architecture)references/quality-checklist.md -- detailed scoring rubricTo read these, find the skill root first, then use the Read tool on ${SKILL_ROOT}/references/<filename>.
When the skill runs inside a project repository (detected by the presence of a package.json, next.config.js, astro.config.*, gatsby-config.*, Gemfile, _config.yml, requirements.txt, or pyproject.toml in the working tree), the agent must extend its output beyond markdown briefs and write directly into the codebase.
Before writing any output, scan the project root and identify the framework. Use these signals in priority order:
| Signal | Framework |
|---|---|
| next.config.{js,ts,mjs} + app/ or pages/ | Next.js (App Router or Pages Router) |
| astro.config.{mjs,ts} | Astro |
| gatsby-config.{js,ts} | Gatsby |
| nuxt.config.{js,ts} | Nuxt |
| _config.yml + _posts/ | Jekyll |
| config.toml + content/ | Hugo |
| *.tsx / *.jsx without framework config | Generic React |
| Only .md / .mdx files | Static markdown site |
| Only .html files | Static HTML |
The detected framework determines the file extension, semantic-HTML injection point, and redirect-config target.
For every page the skill produces, generate the rendered semantic HTML container scaffold (<article>, <section>, <aside>, <main> per Section 6) and inject it directly into the source file's component or template. Do not output a generic <div> shell and rely on the developer to refactor. Specifically:
.tsx, .jsx, .astro): emit a complete component file with semantic landmarks, not a markdown body that has to be wrapped later.md, .mdx): include a semantic HTML block at the top of the body (above the prose) that wraps the AI Summary Nugget and Original Research block in <aside> and <section> tags respectively, since most markdown renderers will preserve raw HTML.html): produce the full document with <main> / <article> / <section> already in placeFor every legacy URL that the rewrite protocol flags as 410 (Gone), the skill must emit a concrete redirect/410 snippet matched to the project's deployment target. Do not stop at "recommend 410" -- generate the config the user can commit.
Output format depends on what the project actually uses:
# Apache .htaccess
RedirectMatch 410 ^/old-thin-path/?$
# Nginx site config
location = /old-thin-path { return 410; }
// next.config.js
async redirects() {
return [
{ source: "/old-thin-path", destination: "/", statusCode: 410 },
// 301 example for surviving topics:
{ source: "/old-merged-path", destination: "/canonical-path", permanent: true },
];
}
# Vercel / vercel.json (functional 410 via rewrite to a 410-returning route)
{ "redirects": [ { "source": "/old-thin-path", "destination": "/410", "statusCode": 410 } ] }
Detect which one to emit by looking for .htaccess, nginx.conf, next.config.*, or vercel.json in the project. When ambiguous, emit Apache + Nginx + Next.js as a triple-snippet block and let the developer pick.
| Asset | Where it goes |
|---|---|
| Page content (markdown brief) | ~/Documents/SEO-AGI/pages/ (unchanged) |
| Rendered component file (when in repo) | The repo's existing pages/posts directory, matched to convention (app/[slug]/page.tsx, content/posts/<slug>.md, etc.) |
| Redirect / 410 config snippets | ~/Documents/SEO-AGI/redirects/<project>/snippets.txt PLUS, when safe, appended directly to the live config file with a clearly marked # seo-agi: BEGIN/END block the developer can review |
<filename>.seoagi.tsx (or equivalent) and tell the user to diff and merge..htaccess, or deployment manifests without printing the exact diff and asking the user to approve before write.// generated by seo-agi from ~/Documents/SEO-AGI/pages/<slug>.mdThese rules close the gap between "ranking page brief" and "shipped ranking page." The skill writes content that ranks; the codebase execution layer writes the code that gets the content into production.
pip install requests
# For GSC (optional):
pip install google-auth google-api-python-client
.tsx, .md, .html, etc.), detects the framework, injects semantic HTML directly into source files where appropriate, and emits .htaccess / Nginx / next.config.js redirect snippets for the 410 recommendations. The skill writes ranking pages, not just content briefs.New in v1.6.0 -- ICP-Driven Content + Local Trust Signals:
noindex recommendation.New in v1.5.0 -- Forensic SEO + Structural Signals:
<article>, <section>, <aside>, <main> instead of generic <div>. Google's crawler uses these elements to identify the Main Content zone for passage extraction and AI retrieval.New in v1.4.0 -- March 2026 Update Protocols:
New in v1.3.0 -- 2026 SEO Protocols:
New in v1.2.0 -- Anti-Spam Ranking Signals:
New in v1.1.0 -- GEO Framework Additions:
I built this because I got tired of the gap between "SEO audit" and "published page." I've been doing SEO for 20+ years in ground transportation (1M+ bookings, 2M+ rides across my companies). The workflow was always the same: pull SERP data, analyze competitors, find gaps, write brief, write page, add schema, publish. Over and over. So I turned that entire workflow into a single skill that any AI agent can execute.
The result? I used this to research a competitor's best-performing pages, built equivalent content with /seoagi, bought the exact-match domains, and every single page is ranking on page 1. That's not theory. That's the workflow.
You: /seoagi "best project management tools 2026"
SEO-AGI:
1. Pulls SERP top 10 via DataForSEO
2. Parses competitor content (word count, headings, topics covered)
3. Extracts People Also Ask questions
4. Pulls related keywords with search volumes
5. Maps Primary intent (top of page) and Secondary intent (action funnel
below the fold) per the Orcas 1 dual-intent model
6. Generates a data-driven content brief
7. Writes the complete page (Markdown + YAML frontmatter)
8. Extracts top bigrams/trigrams from top 3 competitors and seeds 2+ of
them into the 200-char AI Summary Nugget for LLM-retrieval alignment
9. Adds FAQ section from real PAA data
10. Generates JSON-LD schema markup + inline RDFa entities
11. Validates every claim against 2+ sources (Entity Consensus)
12. For rewrites: evaluates each legacy URL and recommends 301 (when topic
survives and equity should consolidate) or 410 (when the URL is thin,
cannibalizing, or out-of-circle and