GEO Prompt Architecture

Use when the user wants to generate, structure, score, or audit GEO monitoring prompts for a client. Trigger when building topic-first prompt sets from a web...

Build GEO prompt systems that fit the client’s real business, not generic keyword lists.

Overview

Use this skill to generate and audit AI visibility monitoring prompts for GEO programs. It turns a client brief into a topic -> prompt architecture across non-brand discovery, competitor comparison, and brand defense, then helps translate monitoring results into concrete optimization actions.

When the work needs structured product inputs or outputs, use the JSON schemas in schemas/. When the client model is unclear or highly verticalized, use the examples in examples/ and the playbooks in references/.

Best For

• GEO software teams onboarding new clients
• GEO agencies building prompt sets at scale
• operators who need better prompt coverage by topic, product line, and funnel stage
• teams that want to rebalance prompt libraries away from brand-heavy bias
• teams that want monitoring prompts tied to later content and asset optimization

Start With

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Use $geo-prompt-architecture to generate GEO monitoring prompts for this client.

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Use $geo-prompt-architecture to review this prompt set and rebalance brand vs non-brand prompts.

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Use $geo-prompt-architecture to turn these monitoring results into prompt and content recommendations.

External Access And Minimum Credentials

This skill can work from a pasted brief, screenshots, exports, or a website URL.

• no private credentials are required for basic prompt generation or review
• live browsing is helpful when the client website, topics, product lines, or competitor overlap must be validated
• do not assume access to analytics, Search Console, CRM, AI monitoring dashboards, or private docs unless explicitly provided

Core Model

Always frame GEO prompts as a topic-first system:

1. Topic map Decide which problem spaces, categories, use cases, trust questions, competitor clusters, channels, and seasonal themes deserve monitoring.
2. Non-brand discovery Users do not know the brand yet. These prompts measure whether the brand can enter new answer spaces.
3. Competitor comparison Users are comparing brands, alternatives, or solution routes. These prompts measure competitive visibility.
4. Brand defense Users already know the brand and are validating fit, quality, pricing, sizing, shipping, returns, or worth. These prompts measure narrative control and decision-stage performance.

Topic sources can be:

• user-provided priority topics
• product lines turned into topic seeds
• inferred topics generated from the website, business model, use cases, competitors, channels, and weak AI surfaces

Default pack size:

• 5 topics
• 50 prompts total
• 10 prompts per topic

Default pack mix:

• 30-32 non-brand discovery prompts
• 12-15 competitor comparison prompts
• 5-8 explicit brand prompts

Recommended per-topic starting shape:

• 6 non-brand discovery prompts
• 3 competitor comparison prompts
• 1 brand defense prompt

Default target mix:

• 60-70% non-brand discovery
• 20-25% competitor comparison
• 10-20% brand defense

Do not let brand prompts dominate unless the user explicitly asks for a brand-defense-only set.

Workflow

1. Reconstruct the client model

Before generating prompts, identify:

• business model
• market and language
• target customer
• user-provided topics, if any
• core product lines, if any
• conversion path
• key competitors
• weak AI surfaces, if provided

Useful business-model labels:

• SaaS / software
• ecommerce / DTC
• services / consultancy
• marketplace / aggregator
• manufacturer / supplier
• content / media

If inputs are incomplete, infer carefully and label the inference.

If the user wants a standard onboarding shape, use schemas/client-brief.schema.json.

If the business model is ambiguous, read references/vertical-templates.md and compare against the sample cases in:

• examples/coofandy-topic-first-output.md
• examples/trip-com-consumer-travel-marketplace.md
• examples/movinghead-stage-lighting.md

2. Build the topic map

Do not jump straight into prompts.

First, build a topic map that explains what the monitoring system should cover.

Priority order:

1. normalize user-provided topics
2. turn product lines into topic seeds
3. infer missing topics from:
   • use cases
   • audience segments
   • competitor overlap
   • trust and evaluation questions
   • channels and marketplaces
   • seasonality and trend patterns

Useful topic types:

• product/category
• use-case
• audience/segment
• competitor/alternative
• trust/evaluation
• channel/marketplace
• seasonal/trend

Every output should make it clear whether a topic is:

• provided
• derived-from-product-line
• inferred

If the system identifies more than 5 valid topics, choose the top 5 by:

• business value
• monitoring value
• GEO leverage
• competitor pressure
• channel fit

3. Map the funnel

Prompt outputs should use the marketing-funnel labels your product shows:

• TOFU
• MOFU
• BOFU

Use this default mapping from the older buyer-journey model:

• Problem awareness -> TOFU
• Solution education -> TOFU
• Category evaluation -> MOFU
• Brand comparison -> MOFU
• Purchase decision -> BOFU
• Use / implementation / expansion -> BOFU

Commercial-intent override:

• if a prompt is clearly procurement-led, product-spec specific, supplier/vendor selection oriented, or near-term purchase oriented, prefer BOFU even if it would otherwise look like category evaluation or comparison

Read references/prompt-framework.md when you need the full generation framework.

4. Generate prompt sets by topic

Generate prompts inside each topic. Keep the layers separate:

• non-brand discovery prompts
• competitor comparison prompts
• brand defense prompts

If product lines exist, use them as one grouping dimension, but do not treat them as mandatory. Some clients need prompt sets grouped by:

• topic
• business problem
• audience segment
• marketplace channel
• competitor cluster

Prompt rules:

• write natural-language user questions, not SEO fragments
• prefer prompts that fit AI conversations and recommendation flows
• include scenarios, constraints, audiences, budgets, regions, or channels when useful
• avoid low-value navigational brand variants
• keep explicit brand-name prompts sparse in the default 50-prompt pack

5. Add GEO judgment, not just prompts

For each prompt, include enough structure to make the set operational. Default fields:

• prompt
• topic
• topic_source
• topic_type
• layer
• funnel stage (TOFU / MOFU / BOFU)
• category
• product line
• target customer
• business value
• GEO priority
• monitoring value
• likely answer-entry mode
• why it matters

If the user wants a compact output, keep the fields but shorten the explanations.

If the user wants a product-ready response shape, use:

• schemas/prompt-set-output.schema.json
• schemas/prompt-scorecard.schema.json

6. Audit and rewrite existing prompt sets

When reviewing an existing prompt list, do not regenerate everything by default. For each prompt:

• keep
• optimize
• downgrade
• delete
• replace

Common failure modes:

• no topic map before prompt generation
• too many topics with too few prompts per topic
• too many brand prompts
• no comparison prompts
• no true non-brand discovery prompts
• off-funnel or synthetic phrasing
• prompts that fit search engines better than AI answers
• prompts that mismatch the client’s real product line or market
• prompts that cluster around one topic while ignoring the real topic surface

7. Reverse-optimize from monitoring results

When the user brings AI monitoring results, use them to improve both content and the prompt library.

Track at least:

• was the brand mentioned?
• how was it mentioned?
• which brands replaced it?
• what source types were cited?
• what loss reason best explains the miss?

Then propose:

• content actions
• page / asset actions
• evidence / entity actions
• prompt-set changes

Read references/reverse-optimization.md when you need the loss-reason model or the reverse-optimization loop. Read references/scoring-model.md when the user wants prompt-set QA, scorecards, or benchmark-style review.

Output Patterns

Default output order:

1. client model summary
2. topic map
3. prompt strategy by layer
4. prompt set by topic
5. priority prompts
6. optional reverse-optimization actions

When auditing, prefer tables like:

Original	Action	Final	Reason
Prompt A	Keep	Prompt A	Fits the topic, product line, and funnel
Prompt B	Optimize	Better Prompt B	Original is too generic or too brand-heavy
Prompt C	Delete	—	Low monitoring value

Guardrails

• Do not treat prompt generation as generic keyword research.
• Do not skip topic generation just because the client did not provide topics.
• Do not over-index on brand terms.
• Do not collapse every prompt into bottom-funnel buying language.
• Do not invent product lines, topics, channels, or competitors without labeling the inference.
• Do not assume every prompt should become an article; some should map to category pages, comparison pages, FAQs, reviews, or marketplace listings.
• When the user asks for monitoring prompts, bias toward prompts that can reveal visibility movement over time.
• Do not apply an ecommerce prompt pattern to a marketplace, SaaS, or industrial manufacturer without checking business-model fit first.