geo-optimize

name	geo-optimize
description	Turn an ai-answer-audit into a prioritized plan to get a brand cited in AI answers (ChatGPT, Perplexity, Google AI Overviews). Acts only on the audit's affectable content layer — earned citations, your own pages, open territory — and never promises to move the model layer. User-run on an audit + your brand/URL; a one-shot plan, not citation monitoring.
disable-model-invocation	true
argument-hint	[paste the ai-answer-audit output + your brand name/URL — optional: geography, competitors to displace]
hooks	{"PostToolUse":[{"matcher":"*","hooks":[{"type":"command","command":"python3 \"$HOME/.claude/skills/skill-feedback/scripts/skill-event.py\" --skill geo-optimize --event skill_activated --agent-harness claude-code --quiet","timeout":5}]}]}

GEO Optimize

Turn an ai-answer-audit into a concrete plan to get a brand cited in AI answers. The audit diagnosed what an answer rested on; this acts on the one part you can change.

You consume the audit; you do not re-diagnose. Act only on the content layer — the affectable sources and domains. The model layer (the queries the model runs, its model prior claims) is intrinsic: it tells you which queries to target, but no content moves it, and you never promise a win there.

GEO is entity-first, not page-first. Most AI citations are earned on third-party sources — listicles, Reddit, review sites, primary docs — not on your own homepage. So the highest-leverage moves are usually getting into the sources that already won, not polishing your own site.

What you need

• The ai-answer-audit output — its evidence ledger (Level + Authority + Influence per row), the Ignored "open territory" domains, and the model-layer query set.
• The brand + canonical URL(s) to get cited; optionally the target query, geography, and competitors to displace.

Refuse without an audit. Handed a raw AI answer and no audit, stop and say "run ai-answer-audit first." The audit is the single source of truth; never reconstruct a ledger here.

The four levers

Every recommendation acts through exactly one lever, and names the audit row it acts on plus the model-layer query it answers:

• A — Get cited on a High/Medium-influence domain already driving the answer (earn a listing, review, or quote on the listicle, forum, or primary page that won). Usually the highest leverage — this is where most citations come from.
• B — Fix open territory — a page of yours that surfaced but fed nothing (Ignored): repair the intent mismatch, missing claim, or weak authority so the engine feeds from it. Lowest friction.
• C — Open a lane — target a model-layer query whose current winners are low-authority, with a primary-quality artifact (your own page, or for forum/Reddit-intent queries an indexable third-party thread). Cite the audit row the model actually used for that query and show its authority is low — a High-influence but low-authority winner is the prime C target (key on authority, not influence). If that query's used winners are high-authority, it isn't C — route to A or D.
• D — Upgrade evidence — turn a snippet-only High-influence seam you control or can get content into into open-page-worthy content.

One lever per recommendation. Never tag two (no C/D, no A/C); if two seem to apply the action is under-specified — split it into two rows. A vs C: A requires a domain that already appears as an audit row driving the answer; a domain or source type not in the ledger (Reddit, Wikipedia, G2, a fresh benchmark) is C, not A. A competitor-owned snippet seam is never D — use C (your own primary page) or A (earn a cite there). Tag only the final lever — never a compound like D→C; put any "looks-like-D-but-the-seam-isn't-mine" reasoning in a note, not the lever column.

Workflow

1. Ingest and validate the audit. Confirm it has an evidence ledger (Authority + Influence tags), an open-territory / Ignored list, and the model-layer query set; quote all three into the plan header. Work from the audit — do not re-search by default (re-running engines is opt-in, tagged rerun evidence and dated). Done when all three are present; if any is missing, stop and ask the user to run/extend ai-answer-audit.
2. Place the brand — exactly once. Find the brand's domain in the audit: a Driver (High/Med/Low), Open-territory (surfaced but Ignored), or Absent. If Absent, Lever B is unavailable — all work is net-new via A/C/D, and you don't borrow another brand's Ignored rows; if the competitor to displace is also Absent, reframe the goal as entering the answer, not a head-to-head. Done when the brand carries exactly one label, citing the audit row(s).
3. Run the crawler preflight. For each brand URL: robots.txt isn't blocking GPTBot / ClaudeBot / PerplexityBot / Google-Extended; critical content is server-rendered HTML, not JS-only; CDN/WAF allows AI crawlers. Done when each URL is marked pass/fail — or, for a consumed-only audit with no crawler data, required-but-unverified (never a fabricated pass/fail); any fail becomes a Fast Win sequenced before on-page tactics (most sites silently fail here).
4. Map each opportunity to exactly one lever. Done when every opportunity names a single lever (A–D) + a specific audit row number (never —; a blank row doesn't trace — cut it or re-express it as a Lever C with its model-layer query) + the model-layer query it answers. The crawler-preflight gate is the sole exception — it carries no lever and row —, listing instead the rows it unblocks.
5. Branch per engine. Cited domains barely overlap across engines, so tag each recommendation by where it pays off — ChatGPT (Wikipedia/G2, encyclopedic neutrality, named authors), Perplexity (Reddit + recency / year-stamps), Google AIO (topical coverage + YouTube). Done when each recommendation states which engine(s) it moves; recency advice is engine-scoped, not blanket.
6. Rank by leverage and write actions. Leverage = the audit's influence rank × feasibility for this brand × durability. For the top 3–7, write a content or earned-citation action tied to an exact target page/claim, grouped Fast Wins / Roadmap / Backlog. Don't recommend debunked tactics (llms.txt does nothing for AI search; ranking #1 alone isn't enough). Hub > leaf: if the audit's search path marks a source as a hub (it seeded a downstream hop), rank it above a leaf citation of equal influence — a hub shapes which options get compared and on what terms, and can win without being cited; landing a specific, falsifiable, numeric, entity-named claim on a hub is usually Lever A or D. Caveat: seed leverage steers the decision logic, not the verdict — the durable play is a seed whose claim survives verification. Done when each action has an exact target and a checkable "done when" line, leverage-ranked.
7. Name the model-layer wall and check honesty. List the queries and model prior claims no content will move, as intel — not promised wins. Done when every recommendation traces to a row + lever + query, the brand is placed once, no action promises to move a model prior claim, and the audit's vocabulary is reused verbatim.

Output format

Use this structure unless the user asks otherwise.

## GEO Optimization Plan

**Brand / URL:** <brand> — <url>
**Target answer/query:** <the "best X" being competed for>
**Source audit:** <reference + date>  ·  **Search stance:** consumed-only | rerun-augmented
**Queries the model ran (model layer):** "<q1>", "<q2>", …

### Where you stand now
<Driver (High/Med/Low) | Open-territory | Absent> — tied to the audit row(s). One label, no hedging.

### Authority gap & preflight
- **Gap:** the High/Med-influence domains the answer leaned on that the brand is absent from.
- **Preflight:** per-URL pass/fail (GPTBot/ClaudeBot/PerplexityBot/Google-Extended; SSR vs JS-only; WAF). Fails → Fast Wins.

### Opportunity map
| Opportunity | Lever | Target query | Target domain/page | Audit row | Influence to capture | Engine(s) | Feasibility | Priority |
|---|---|---|---|---|---|---|---|---|

### Prioritized actions
Each line restates lever + audit row + query + engine inline (don't rely on the table above):
**Fast Wins** — <action · lever + audit row # · model-layer query · engine(s) · exact target claim/page · done-when (a brand-verifiable artifact-state; defer ranking/citation outcomes to the re-audit loop)>
**Roadmap** — <…>
**Backlog** — <…>

### Open-territory targets
The `Ignored` pages where the brand already surfaced but fed nothing — what intent/claim/spec to add so the engine feeds from it. Lowest-friction lane (your own URL).

### The model-layer wall
`model prior` claims + intrinsic query behavior that no content will move — targeting intel, explicitly not promised as wins.

### What this plan can't do
A point-in-time plan, not citation monitoring: it won't track share over time, prove an action worked, or watch competitors. GEO moves over ~6–12 months and share is volatile. To re-measure, re-run `ai-answer-audit` after shipping and diff it.

### Bottom line
The single highest-leverage move and why — which High-influence slot it touches.

Scale to the audit: a handful of opportunities for a thin audit, more for a rich one. Lead with the earned placement that touches the highest-influence driver.

Honesty checks (run before returning)

• The brand is placed exactly once (Driver / Open-territory / Absent), citing an audit row.
• Every recommendation names exactly one lever + a specific audit row number + a model-layer query — anything untraceable (or row —, except the preflight gate) is invented; cut it or re-lever it.
• Every Lever D names a seam the brand controls or can place content into — a competitor/third-party domain re-levers to C or A.
• Every Lever C cites the audit row showing its current winners are low-authority.
• No product attribute, SKU, or competitor is claimed that the audit doesn't contain; for an Absent brand, product specifics are marked assumption-to-verify.
• No action promises to move a model prior or other model-layer item; the model-layer wall is stated.
• Preflight fails are sequenced before on-page tactics.
• The "what this plan can't do" boundary is present — the plan is never sold as monitoring.

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