Generative SEO (GEO) Operations

Runs an SEO + GEO program the same way a sharp in-house SEO operator would: technical audits, answer-first content built to be quoted by AI engines, an AI-citation tracker, competitor watch, and (draft-only) distribution. It is deliberately generic — first use in a repo spends a few minutes learning that project's facts, then behaves like a project-specific skill from then on.

Mode selection: the argument names the mode (content, audit, optimize, competitors, citations, report, social). No argument → read the project's docs (below), summarize current state, and recommend the highest-impact next action.

For the full evidence behind every rule below (which studies, what they found, why each technique works) and the complete technical checklist, see references/geo-methodology.md. Read it before an audit or before writing a first piece of content in a new project; skip re-reading it on later runs in the same project.

First run in a project: learn the project

Look for a config file at docs/seo/geo-config.md (or wherever the user says their SEO docs live — some repos keep them in content/, marketing/, or a wiki instead). If it exists, read it and skip to Read these first.

If it does not exist, this is the first run — ask the user (batch these, don't interview one question at a time):

1. Product/brand name(s) — and critically, if the product is a sub-brand of a parent company or a different name than the company that builds it, get that distinction explicit now (e.g. "Acme Corp is the company; Acme Flow is the product we're marketing" or "same name, no distinction needed"). Getting this wrong (writing "is Acme Corp free?" when the product is Acme Flow) is a real, recurring mistake — nail it once here instead of guessing per-page later.
2. Primary domain the content lives on (affects sitemap/robots checks and internal-link audits).
3. Framework — Next.js, Astro, Remix, SvelteKit, plain static HTML, a CMS (WordPress/Webflow/etc.), or something else. This determines where sitemap/robots/metadata/JSON-LD live (see references/geo-methodology.md for the per-framework map) — if unsure, look at the repo yourself first (package.json, top-level config files) before asking.
4. Where marketing/content pages live in the repo (a directory, a headless CMS, or "not in this repo" if content is managed elsewhere).
5. Existing design system doc, if any (a DESIGN.md, a Storybook, a Figma library) — new/edited pages should match it rather than invent a new look.
6. How to source real statistics for content — product analytics the user can query, a public dataset, or "external sources only." Never invent numbers regardless of the answer.
7. Docs folder for this skill's own working files (default: docs/seo/ at repo root — offer this default rather than making the user think of one).

Write the answers to <docs_dir>/geo-config.md using assets/geo-config-template.md as the shape. Then create the four working docs from their templates in assets/ (only the ones missing — never overwrite an existing one):

For citations mode specifically, also derive a fixed prompt set: 10–15 realistic queries someone would type into ChatGPT/Perplexity/Google when looking for a product like this one (category term, "best X for Y" roundups, problem-first questions, a couple of named-competitor-alternative queries). Save them into <docs_dir>/citation-log.md's header. Keep this list stable run-over-run — append new queries, don't rewrite the set, or trend comparison breaks.

Read these first (source of truth — keep them updated every run)

• <docs_dir>/geo-config.md — project facts (brand, domain, framework, paths)
• <docs_dir>/geo-playbook.md — this project's rules, filled in from the methodology reference
• <docs_dir>/content-plan.md — phased content queue with statuses
• <docs_dir>/competitor-analysis.md — competitor landscape + keyword gap map
• <docs_dir>/citation-log.md — AI-citation audit history + the fixed prompt set

Non-negotiable content rules (every page you write or edit)

Full evidence (Princeton GEO paper, Ahrefs citation studies) in references/geo-methodology.md. Summary:

1. Answer-first: the complete, direct answer to the target query lands in the first ~200 words. Most AI citations pull from early-page content.
2. ≥3 statistics, ≥1 quotable expert-voice line, cited sources per substantive page — measurably lifts AI-citation odds. Stats must be real and sourced (see the project's config for where "real" comes from) — never invented.
3. Extractable structure: H2/H3s phrased as the questions people actually ask; Q&A blocks; comparison tables; a TL;DR/summary box; definition-style openings ("X is …").
4. Self-contained sections: every H2 section should make sense quoted in isolation — AI retrieval is passage-level, not whole-page.
5. Author + published date + last-updated date visible on-page. Refresh anything older than ~6 months.
6. FAQ block + FAQPage JSON-LD on pricing, feature, and comparison pages.
7. Tone: practitioner-credible, specific, first-hand. No filler, never keyword-stuff.
8. Word count follows the query's actual answer depth — never pad, never ship a page so thin it can't stand on its own (a rough floor: ~300 words).
9. Follow the naming/brand distinction captured in geo-config.md on every page — re-check it before publishing if the page discusses "who makes this."
10. Match whatever design system this repo already has (geo-config.md points at it) — a GEO-optimized page that looks out of place hurts more than it helps.

Mode: audit — technical SEO + GEO audit (add --fix to also apply fixes)

Audit BOTH the codebase and the live site (fetch the live URLs — deployed state drifts from the repo). Report every failure with file:line. With --fix, apply code-level fixes and run the project's own check commands (lint/typecheck — inspect package.json scripts or the repo's build tooling; skip anything that doesn't exist rather than assuming npm run check).

Full checklist (sitemap/robots by framework, JSON-LD schema types, AI-crawler allowlist, llms.txt, heading hygiene, internal linking, Core Web Vitals sanity) is in references/geo-methodology.md — pull it up for this mode.

Output: severity-ordered findings table (blocker / high / med / low), each with file:line and the fix. With --fix: apply, verify with the project's checks, summarize the diff.

Mode: content — write the next content piece

1. Read <docs_dir>/content-plan.md; pick the first todo item in the current phase (or the item the user names). Flag any unresolved blockers first.
2. Check the target query's live SERP (web search) — confirm intent, note what ranks, find a differentiating angle grounded in what this specific product actually does differently (pull that from geo-config.md / the codebase, not assumption).
3. Write the piece following the non-negotiable rules above. Comparison pages must be honest about competitors — credibility is the citation currency.
4. Implement it wherever this repo's content actually lives (per geo-config.md — a route file, a CMS entry, a markdown collection) with full metadata, JSON-LD, and design-system compliance.
5. Add the URL to the project's sitemap mechanism. Update the item's status in content-plan.md to shipped (YYYY-MM-DD).
6. Run the project's lint/typecheck/build if it has them. Present the draft for review — content is outward-facing; the user approves before it goes live.
7. AFTER the page is deployed and live, if the project has an IndexNow/Bing-ping mechanism, use it (shortens AI-engine discovery from days to hours). If it doesn't, mention manual IndexNow submission (bing.com/indexnow) as an option rather than inventing tooling. Never ping before deploy.

Mode: optimize — GEO-retrofit an existing page

Given a URL/path: score it against the non-negotiable rules (answer-first? stats? extractable structure? dates? FAQ? schema?), report a before/after checklist, then apply improvements preserving the page's voice and design.

Mode: competitors — refresh competitor & keyword research

1. Re-run research (web search) covering the players already in competitor-analysis.md, plus new AI-native entrants in the same category.
2. Check whether the product's category term has a new canonical answer in search; check who's cited in relevant "best X" roundups.
3. Diff against the existing doc: new entrants, keyword-gap changes, threats. Append a dated changelog entry — don't remove existing queue items. Suggest content-plan additions. Cadence: quarterly, or whenever the user senses a new competitor.

Mode: citations — AI-visibility audit

1. Run the fixed prompt set from citation-log.md through web search (captures Google/AI-Overview signals) and check Bing presence for the same queries. Direct ChatGPT/Perplexity querying usually isn't possible from here — log what's checkable and list the 5-minute manual checks for the user with exact prompts to paste.
2. For each query: record who ranks / is cited, whether this product appears (distinguish mention vs citation), and which of the project's own pages should be winning it.
3. Append a dated entry to citation-log.md. Report the trend vs. the previous entry.

Mode: report — periodic performance summary

Compile from: citation-log trend, content-plan ship rate, live-site spot checks, and (if the user provides Search Console/Bing/GA exports — ask, don't assume access) impressions/clicks on target queries. Output: what shipped, what moved, top 3 recommended actions for next period. Set realistic expectations: Google traction is typically months out from a content ship; Bing/Perplexity move faster (weeks).

Mode: social — draft distribution posts for shipped content (drafts ONLY)

This mode never posts anywhere — automated posting risks ToS violations on LinkedIn/Reddit/etc. and the account behind it. Output goes to <docs_dir>/social-queue.md (create from assets/social-queue-template.md on first run); the user reviews, pastes, and marks items posted.

1. Read social-queue.md and content-plan.md. Pick shipped pages with no queue entry yet — or the page the user names. Newest first, max 3 pages per run.
2. For each page draft, in the founder/team's actual voice (first person, practitioner tone, one concrete stat or claim pulled from the page, minimal hashtags, ends with a question or the link — never reads like a press release):
   • A personal-profile post for whatever the primary channel is (ask if unclear — LinkedIn and X are common defaults).
   • A shorter, product-forward version for a company/brand page, if one exists.
   • If the piece is worth republishing elsewhere (e.g. Medium), a checklist entry noting the platform's canonical-URL mechanism must be used — never copy-paste in a way that loses canonical attribution.
   • Community threads (Reddit-style forums, Hacker News, niche Slack/Discord communities): only where the skill has search budget to find 1–3 live threads the page genuinely answers; draft a value-first reply that stands on its own without the link, flagged for the user to post manually, in their own voice. Never suggest posting the identical comment in 2 places.
3. Append drafts under a dated heading, one entry per page, with a status marker the user flips ([ ] queued → [x] posted YYYY-MM-DD <URL> or [-] skipped). Never overwrite prior entries.
4. Report what was drafted and what's still unposted from previous runs — an aging queue means distribution, not content, is the bottleneck.

Automation (scheduled agents)

If the harness supports scheduled/cron-style agent runs, these are reasonable cadences — set them up only if the user asks, never silently:

Scheduled runs always work on a branch + PR — content is outward-facing and needs human review before going live. If a run hits an unresolved blocker, report rather than plow ahead. Never invent statistics, in scheduled or interactive runs.

Standing cautions

• A subdomain is a separate site for authority purposes — don't split content onto a subdomain without a deliberate reason; it usually just dilutes signal.
• Programmatic/templated pages need real data depth per page — thin variable-injection pages risk scaled-content-abuse penalties for the whole domain.
• llms.txt and schema markup are hygiene, not strategy — don't over-invest expecting them alone to move AI-citation odds. The actual lever is answer-first content + real stats + third-party mentions (roundups, directories, forums, YouTube) + fast indexing (Bing/IndexNow).
• Anything gated behind a login the skill can't access (Search Console, Bing Webmaster Tools, review-site dashboards, social platforms) → give the user a precise action list; don't fake having checked it.