Skill: geo-seo/geo-prompt-architecture (version 0.1.3)

--- name: geo-prompt-architecture description: 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 website, brand, market, customer, product lines or inferred topics, and competitors; when balancing non-brand, comparison, and brand-defense prompts; or when turning AI visibility monitoring results into prompt and content optimization actions. --- # GEO Prompt Architecture 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 ```text Use $geo-prompt-architecture to generate GEO monitoring prompts for this client. ``` ```text Use $geo-prompt-architecture to review this prompt set and rebalance brand vs non-brand prompts. ``` ```text 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](schemas/client-brief.schema.json). If the business model is ambiguous, read [references/vertical-templates.md](references/vertical-templates.md) and compare against the sample cases in: - [examples/coofandy-topic-first-output.md](examples/coofandy-topic-first-output.md) - [examples/trip-com-consumer-travel-marketplace.md](examples/trip-com-consumer-travel-marketplace.md) - [examples/movinghead-stage-lighting.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](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-set-output.schema.json) - [schemas/prompt-scorecard.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](references/reverse-optimization.md) when you need the loss-reason model or the reverse-optimization loop. Read [references/scoring-model.md](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.

path: README.md
content: # GEO Prompt Architecture

Generate the most suitable GEO monitoring prompts for each client’s business, starting from the right topics so monitoring can lead to sharper optimization.

`GEO Prompt Architecture` is an open-source skill and repo for GEO teams, agencies, and operators who need more than a flat prompt list. It turns a client brief into a `topic -> prompt` operating system across discovery, comparison, and brand-defense layers, then helps feed monitoring results back into content and asset decisions.

Maintained by [Dageno.ai](https://dageno.ai), a GEO-focused platform for turning AI visibility signals into actionable marketing workflows.

![GEO Prompt Architecture hero](assets/social-preview.png)

## The Problem

Most GEO prompt generation is still too generic.

Teams often produce:

- no explicit topic map before prompts
- too many branded prompts
- too few real discovery prompts
- weak competitor comparison coverage
- prompts that do not match the client’s business model
- monitoring sets that are hard to act on later

That creates a false sense of AI visibility. A brand may look visible on branded prompts while still failing to enter the non-brand answer spaces that drive growth.

## The Promise

This repo helps teams produce prompts that are:

- better structured by topic before prompt expansion
- better matched to the client’s real business
- better matched to how people ask AI tools questions
- better structured for long-term monitoring
- better connected to downstream optimization

In plain English: it helps you create prompts that are worth monitoring and worth optimizing against.

## What This Repo Includes

- a root `SKILL.md` for Codex-style use
- a GitHub-ready social preview asset in `assets/`
- input and output `schemas/` for productizing prompt generation
- `references/` for prompt architecture, vertical playbooks, scoring, reverse optimization, and the final generator prompt
- a lightweight GitHub launch checklist in `references/`
- `examples/` that show how topic strategy changes by business type
- `agents/openai.yaml` metadata for skill UI usage

## Core Operating Model

This repo treats prompt generation as a system, not a keyword dump.

The core hierarchy is:

`business model -> topic map -> prompt layers -> funnel stages -> monitoring set`

Product lines are one valid way to seed topics, but they are not required.

If the client gives:

- `priority topics`, normalize and expand them
- `product lines`, turn them into topic seeds
- neither, infer topics from the website, business model, use cases, competitors, channels, and weak AI surfaces

That matters because strong GEO monitoring starts with the right topic universe, not just the right prompt phrasing.

### 0. Topic map

Use topics to define what the monitoring system should care about before writing prompts.

Typical topic types:

- product or category topics
- use-case topics
- audience or segment topics
- competitor and alternative topics
- trust and evaluation topics
- channel and marketplace topics
- seasonal or trend topics

### Default monitoring pack

Unless the user explicitly asks for a different size, the default output should be:

- `5` priority topics
- `50` prompts total
- `10` prompts per topic

Default prompt mix inside the 50-prompt pack:

- `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

If the client provides more than 5 possible topics, prioritize the top 5 based on:

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

Do not let explicit brand-name prompts take over the set. In the default 50-prompt pack, branded prompts should usually stay in the `5-8` range unless the user explicitly wants a brand-defense-heavy set.

### 1. Non-brand discovery

Use prompts that test whether the client can enter new answer spaces before users know the brand.

### 2. Competitor comparison

Use prompts that test whether the client appears when buyers compare options, alternatives, and routes.

### 3. Brand defense

Use prompts that test how AI describes the brand in branded, decision-stage, and trust-sensitive queries.

Recommended default mix:

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

Prompt outputs should use `TOFU / MOFU / BOFU` as the visible funnel labels.

Default mapping:

- `TOFU`: broad awareness and educational exploration
- `MOFU`: evaluation, alternatives, and comparison
- `BOFU`: explicit commercial, purchase, procurement, or brand-validation intent

## Why This Matters For GEO

Prompt architecture shapes everything downstream:

- which topics you monitor in the first place
- what you monitor
- what you learn from AI answers
- what content you create next
- which competitor gaps you actually see
- how accurately you report AI visibility to clients

If the prompt layer is weak, the reporting layer and optimization layer will also be weak.

## Designed For Business-Model Fit

This repo assumes prompt systems should change based on business model.

It is built to support:

- ecommerce and DTC brands
- SaaS and software products
- services and consultancies
- marketplace and aggregator businesses
- B2B manufacturers and product suppliers
- content and media properties

That is why the repo includes structured schemas, vertical playbooks, examples, and a scoring model instead of only one default prompt.

## Representative Examples

The same GEO methodology should produce very different prompts for different businesses.

### Trip.com

`Trip.com` is best understood as a `consumer online travel agency / travel marketplace`, not a business-travel management platform and not a content publisher.

That means the prompt set should lean toward travel-planning discovery, OTA comparisons, and branded trust questions.

| Layer | Topic Focus | Example Prompts |
|---|---|---|
| Non-brand discovery | leisure and cross-border trip planning; hotel, flight, train, attraction, and package discovery | `What is the best website to book hotels and flights for an international trip?`<br>`What are the best travel booking apps for international travelers?` |
| Competitor comparison | comparisons against other OTA and travel-booking brands | `Trip.com vs Booking.com for hotels in Asia`<br>`What are the best alternatives to Booking.com for flights and hotels?` |
| Brand defense | reliability, customer support, refunds, app / site fit, and booking confidence | `Is Trip.com reliable for booking international flights and hotels?`<br>`How does Trip.com handle cancellations, refunds, and itinerary changes?` |

See [trip-com-consumer-travel-marketplace.md](/Users/timlin/Downloads/knowledge/projects/geo-prompt-architecture-repo/examples/trip-com-consumer-travel-marketplace.md).

### movinghead.net

`movinghead.net` is best understood as a `B2B stage-lighting manufacturer / supplier`, not a consumer fashion brand and not a software platform.

That means the prompt set should lean toward technical category discovery, supplier comparison, and procurement-trust prompts.

| Layer | Topic Focus | Example Prompts |
|---|---|---|
| Non-brand discovery | product category discovery around moving-head lighting; technical and venue use cases | `What is the best moving head light for indoor concert stages?`<br>`How do I choose between beam, wash, and spot moving head lights for live events?` |
| Competitor comparison | manufacturer trust, distributor / supplier comparison, OEM and export sourcing | `Best alternatives to Clay Paky or Martin for affordable moving head fixtures`<br>`What are the top moving head light manufacturers in China for export buyers?` |
| Brand defense | specs, lead time, support, certifications, and OEM fit | `Is movinghead.net a reliable moving head light manufacturer?`<br>`Does movinghead.net support OEM or custom stage-lighting production?` |

See [movinghead-stage-lighting.md](/Users/timlin/Downloads/knowledge/projects/geo-prompt-architecture-repo/examples/movinghead-stage-lighting.md).

### Coofandy

`Coofandy` is best understood as an `ecommerce / marketplace-led men's apparel brand` with strong overlap across Amazon, Walmart, and direct-response product discovery.

That means the system should use the five product lines as the default five topics, then express channel trust, value comparison, styling, and climate fit through the prompts inside each topic.

| Topic | Topic Source | Example Prompts |
|---|---|---|
| summer business-casual shirts | derived-from-product-line | `What are the best men's shirts for hot weather that still look business casual?`<br>`Are Coofandy men's shirts good for business casual offices in hot weather?` |
| lightweight pants for hot weather and travel | derived-from-product-line | `What are the best men's pants for summer travel that still look polished?`<br>`Are Coofandy men's pants worth buying for hot-weather travel and all-day wear?` |
| vacation-ready 2 piece sets | derived-from-product-line | `What are the best men's 2 piece sets for vacation and resort wear?`<br>`Are Coofandy 2 piece sets breathable enough for beach trips and summer travel?` |
| matching sets for easy outfit formulas | derived-from-product-line | `What are the best men's matching sets if I want easy outfits that don't look sloppy?`<br>`Are Coofandy men's matching sets worth it for travel and capsule wardrobes?` |
| smart-casual turtleneck layering | derived-from-product-line | `What are the best men's turtleneck sweaters for smart-casual outfits and layering?`<br>`Are Coofandy turtleneck sweaters good for layering under a blazer?` |

See [coofandy-topic-first-output.md](/Users/timlin/Downloads/knowledge/projects/geo-prompt-architecture-repo/examples/coofandy-topic-first-output.md).

## What Makes This Repo More Useful Than A Generic Prompt List

### Structured inputs

`schemas/client-brief.schema.json` makes it easier to standardize onboarding inputs across clients.

### Structured outputs

`schemas/prompt-set-output.schema.json` and `schemas/prompt-scorecard.schema.json` make it easier to connect prompt generation to products, dashboards, and QA workflows.

### Final generator prompt

`references/final-topic-first-generator-prompt.md` gives teams a ready-to-run prompt that matches the repo's topic-first architecture.
`references/final-topic-first-generator-prompt-zh.md` provides the same operating logic in Chinese.

### Vertical playbooks

`references/vertical-templates.md` explains how prompt architecture changes by business type.

### Reverse optimization

`references/reverse-optimization.md` helps turn AI answer losses into content, asset, evidence, and prompt-set changes.

### Prompt scoring

`references/scoring-model.md` helps teams judge whether a prompt set is actually balanced, useful, and monitor-worthy.

## Typical Workflow

1. Capture the client brief in a standard schema
2. Reconstruct the business model, market, audience, product lines, topics, and competitors
3. Build a topic map from provided topics, product-line seeds, and inferred opportunity clusters
4. Generate prompts inside each topic across discovery, comparison, and brand-defense layers
5. Map prompts to funnel stages
6. Score the prompt set for topic coverage, layer balance, and quality
7. Monitor AI answers
8. Feed answer losses back into content and prompt optimization

## Example Skill Usage

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

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

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

## Repository Structure

```text
.
├── SKILL.md
├── README.md
├── LICENSE
├── agents/
│   └── openai.yaml
├── assets/
│   ├── social-preview.png
│   └── social-preview.svg
├── examples/
│   ├── coofandy-topic-first-output.md
│   ├── trip-com-consumer-travel-marketplace.md
│   └── movinghead-stage-lighting.md
├── references/
│   ├── final-topic-first-generator-prompt.md
│   ├── final-topic-first-generator-prompt-zh.md
│   ├── prompt-framework.md
│   ├── reverse-optimization.md
│   ├── scoring-model.md
│   ├── vertical-templates.md
│   └── github-launch-checklist.md
└── schemas/
    ├── client-brief.schema.json
    ├── prompt-scorecard.schema.json
    └── prompt-set-output.schema.json
```

## Install

Copy this repo into your local Codex skills directory as:

```text
~/.codex/skills/geo-prompt-architecture
```

Or use it as a public repo and package it through your own skill publishing workflow.

## Good Outputs You Should Expect

A strong run of this repo should produce:

- prompts grouped by topic first, then optionally by product line
- prompts grouped by layer and funnel
- clearer topic coverage before prompt expansion
- fewer low-value branded prompts
- stronger competitor comparison coverage
- more business-model fit
- clearer monitoring priorities
- clearer recommendations when a brand loses in AI answers

## Where This Can Extend Next

This repo is already moving toward a `GEO prompt operating system`.

Natural next steps:

- richer vertical packs by business model
- automated prompt-set scoring scripts
- benchmark prompt libraries by industry
- tighter JSON outputs for product workflows
- topic clustering and topic QA helpers
- stronger reverse-optimization playbooks by AI surface

## Open Source

This repo is open source so GEO teams can:

- use it internally
- adapt it to client onboarding
- connect it to a GEO product workflow
- customize prompt logic by business model
- improve how prompt monitoring drives optimization

If your team helps clients create the most suitable prompts for their business, this repo gives you a more defensible starting point than a generic keyword sheet.

If you want this logic inside a broader GEO workflow, including monitoring, analysis, and optimization, visit [Dageno.ai](https://dageno.ai).

## License

MIT


path: agents/openai.yaml
content: interface:
  display_name: "GEO Prompt Architecture"
  short_description: "Generate and audit GEO monitoring prompts."
  default_prompt: "Use $geo-prompt-architecture to generate or audit GEO monitoring prompts for this client."


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path: examples/coofandy-topic-first-output.md
content: # Example Output: Coofandy

This example shows the corrected default operating model for a GEO prompt pack:

- `5` topics
- `50` prompts total
- `10` prompts per topic
- `30` non-brand discovery prompts
- `15` competitor comparison prompts
- `5` brand defense prompts

The purpose is to avoid the common failure mode where a prompt set becomes too brand-heavy and stops behaving like a real GEO monitoring system.

## Input Summary

- Brand: `Coofandy`
- Website: `https://coofandy.com`
- Business model: `ecommerce / marketplace-led men's apparel brand`
- Target market: `United States`
- Priority channels: `Amazon`, `Walmart`
- Weak AI surface: `Google AI Overviews`
- Product-line seeds:
  - mens shirts
  - mens pants
  - 2 piece set
  - mens matching sets
  - mens turtleneck sweater
- Competitor set:
  - high: Men's Wearhouse, Express
  - medium-high: Banana Republic, Nautica
  - medium: Ralph Lauren, Gap, Tommy Hilfiger, Zara, Levi's, Calvin Klein, Steve Harvey

## Why This Version Is Better

The earlier example overused branded prompts by forcing every topic to include too much brand-heavy logic.

This corrected version keeps the real GEO priorities:

- discovery prompts should dominate
- competitor prompts should mostly test whether the brand can enter comparison spaces without being named
- brand-defense prompts should stay sparse and high-value

In this example, only `5` prompts explicitly use `Coofandy`, one per topic.

## Topic Map

| Topic | Topic Type | Topic Source | Related Product Lines | Why This Topic Exists |
|---|---|---|---|---|
| summer business-casual shirts | product-category | derived-from-product-line | mens shirts | Core volume topic with overlap across office, smart casual, and hot-weather wear. |
| lightweight pants for hot weather and travel | product-category | derived-from-product-line | mens pants | Strong demand cluster around comfort, travel, and polished summer dressing. |
| vacation-ready 2 piece sets | product-category | derived-from-product-line | 2 piece set | Signature Coofandy category with resort, vacation, and easy-outfit intent. |
| matching sets for easy outfit formulas | product-category | derived-from-product-line | mens matching sets | High-fit category for convenience-led outfit planning and casual travel. |
| smart-casual turtleneck layering | product-category | derived-from-product-line | mens turtleneck sweater | Seasonal topic for fall and winter layering, smart casual, and value comparison. |

## Default Pack Structure

For this client, the default `5 topics / 50 prompts` pack uses:

- `6` non-brand discovery prompts per topic
- `3` competitor comparison prompts per topic
- `1` brand defense prompt per topic

That produces:

- `30` non-brand discovery prompts
- `15` competitor comparison prompts
- `5` brand defense prompts

## Prompt Set

### Topic 1: summer business-casual shirts

| Layer | Funnel | Prompt | Why It Matters |
|---|---|---|---|
| Non-brand discovery | TOFU | How can men dress business casual in summer without wearing stiff dress shirts? | Captures educational intent before brand awareness. |
| Non-brand discovery | TOFU | What shirt fabrics are best for humid weather and all-day office comfort? | Connects fabric logic to hot-weather business use. |
| Non-brand discovery | TOFU | What colors of men's shirts are most versatile for summer business-casual outfits? | Useful for styling-led, non-branded discovery. |
| Non-brand discovery | MOFU | What are the best men's shirts for hot weather that still look business casual? | Core category-entry prompt with strong commercial value. |
| Non-brand discovery | MOFU | What are the best men's shirts for travel that stay comfortable and look polished? | Bridges travel and office-ready shirt demand. |
| Non-brand discovery | MOFU | What are the best affordable men's shirts that look more premium than they cost? | Important value-style discovery prompt. |
| Competitor comparison | MOFU | What are the best alternatives to Banana Republic men's shirts for summer business casual? | Tests whether Coofandy can enter non-branded alternative lists. |
| Competitor comparison | MOFU | Express vs Banana Republic men's shirts: which is better for affordable office style? | Comparison cluster likely to surface replacement candidates. |
| Competitor comparison | MOFU | Which men's brands make the best affordable business-casual shirts for hot weather? | Broad comparison prompt that should allow candidate-list entry. |
| Brand defense | BOFU | Are Coofandy men's shirts good for business casual offices in hot weather? | High-value branded validation prompt tied to the core use case. |

### Topic 2: lightweight pants for hot weather and travel

| Layer | Funnel | Prompt | Why It Matters |
|---|---|---|---|
| Non-brand discovery | TOFU | What pants fabrics are best for hot and humid weather if I still want to look put together? | Educational climate-led prompt. |
| Non-brand discovery | TOFU | What shoes work best with lightweight men's pants for summer outfits? | Styling entry point that can expose brand fit later. |
| Non-brand discovery | TOFU | Can men's linen or lightweight pants work for business casual offices? | Bridges category education with workplace context. |
| Non-brand discovery | MOFU | What are the best men's pants for summer travel that still look polished? | Strong demand cluster around travel and presentation. |
| Non-brand discovery | MOFU | What are the best affordable men's pants for travel, walking, and dinner outfits? | Tests value-oriented commercial intent. |
| Non-brand discovery | MOFU | What are the best men's pants for flights and all-day wear that don't look sloppy? | High intent with clear comfort and appearance needs. |
| Competitor comparison | MOFU | What are the best alternatives to Banana Republic men's summer pants? | Classic alternative-entry prompt. |
| Competitor comparison | MOFU | Gap vs Nautica men's pants: which is better for relaxed summer style? | Competitor comparison that can generate replacement opportunities. |
| Competitor comparison | BOFU | Which men's brands make the best lightweight business-casual pants without premium prices? | Strong commercial comparison prompt. |
| Brand defense | BOFU | Are Coofandy men's pants worth buying for hot-weather travel and all-day wear? | High-value branded decision-stage question. |

### Topic 3: vacation-ready 2 piece sets

| Layer | Funnel | Prompt | Why It Matters |
|---|---|---|---|
| Non-brand discovery | TOFU | What fabrics are best for men's 2 piece sets in hot weather? | Educational prompt tied to climate and comfort. |
| Non-brand discovery | TOFU | Can men's 2 piece sets work for smart casual dinners or rooftop events? | Expands category understanding beyond loungewear. |
| Non-brand discovery | TOFU | What colors make men's 2 piece sets look more expensive? | Styling-led discovery with value implications. |
| Non-brand discovery | MOFU | What are the best men's 2 piece sets for vacation and resort wear? | Core category query with strong monitoring value. |
| Non-brand discovery | MOFU | What are the best affordable men's 2 piece sets for beach trips and summer travel? | High-intent value cluster. |
| Non-brand discovery | MOFU | Can I wear a men's 2 piece set as separates for multiple outfits on one trip? | Important wardrobe-efficiency prompt. |
| Competitor comparison | MOFU | What are the best alternatives to Zara men's 2 piece sets for summer vacations? | Tests whether Coofandy enters a fashionable alternative set. |
| Competitor comparison | MOFU | Zara vs Express for men's vacation sets: which is better value? | Comparison pattern that can yield category substitution. |
| Competitor comparison | BOFU | Which men's brands make the best resort-ready 2 piece sets without designer prices? | Strong commercial shortlist prompt. |
| Brand defense | BOFU | Are Coofandy 2 piece sets breathable enough for beach trips and summer travel? | Branded validation tied directly to climate and travel intent. |

### Topic 4: matching sets for easy outfit formulas

| Layer | Funnel | Prompt | Why It Matters |
|---|---|---|---|
| Non-brand discovery | TOFU | Are men's matching sets still in style for adults, or do they look too trendy? | Captures style skepticism before purchase intent. |
| Non-brand discovery | TOFU | How should men's matching sets fit if I want a polished but relaxed look? | Helpful fit-led educational prompt. |
| Non-brand discovery | TOFU | Can men's matching sets work for brunch, date nights, and vacation outfits? | Occasion-driven discovery beyond product taxonomy. |
| Non-brand discovery | MOFU | What are the best men's matching sets if I want easy outfits that don't look sloppy? | Strong convenience-led category prompt. |
| Non-brand discovery | MOFU | What are the best men's matching sets for weekend travel and casual dinners? | Commercial scenario prompt with clear use case. |
| Non-brand discovery | MOFU | What are the best affordable men's matching sets for summer capsule wardrobes? | High-value, high-utility discovery prompt. |
| Competitor comparison | MOFU | What are the best alternatives to Nautica or Express for men's matching sets? | Non-branded alternative discovery around known competitors. |
| Competitor comparison | MOFU | Zara vs Nautica men's matching sets: which is better for everyday summer wear? | Useful mainstream comparison space. |
| Competitor comparison | MOFU | Which men's brands make matching sets that feel mature and wearable, not flashy? | Helps test audience-fit candidate entry. |
| Brand defense | BOFU | Are Coofandy men's matching sets worth it for travel and capsule wardrobes? | Single branded validation prompt for this topic. |

### Topic 5: smart-casual turtleneck layering

| Layer | Funnel | Prompt | Why It Matters |
|---|---|---|---|
| Non-brand discovery | TOFU | Are turtleneck sweaters still in style for men, or do they look dated? | Captures top-funnel fashion hesitancy. |
| Non-brand discovery | TOFU | What fabric is best for a men's turtleneck sweater if I want something soft but not too warm? | Educational fabric-led prompt. |
| Non-brand discovery | TOFU | What colors of men's turtleneck sweaters are most versatile for fall and winter? | Classic styling and wardrobe-building prompt. |
| Non-brand discovery | MOFU | What are the best men's turtleneck sweaters for smart-casual outfits and layering? | Core seasonal category prompt. |
| Non-brand discovery | MOFU | Can a men's turtleneck replace a dress shirt in a business-casual wardrobe? | Helps test smart-casual substitution logic. |
| Non-brand discovery | MOFU | What are the best affordable men's turtleneck sweaters that look premium under a blazer? | Value-oriented commercial discovery. |
| Competitor comparison | MOFU | What are the best alternatives to Banana Republic men's turtleneck sweaters? | Alternative prompt that should allow candidate-list entry. |
| Competitor comparison | MOFU | Ralph Lauren vs Calvin Klein turtleneck sweaters: which is better value for smart casual outfits? | Premium-to-mainstream comparison cluster. |
| Competitor comparison | MOFU | Which men's brands make good turtleneck sweaters for layering without luxury prices? | Strong value-comparison prompt. |
| Brand defense | BOFU | Are Coofandy turtleneck sweaters good for layering under a blazer? | Branded validation tied to the core styling use case. |

## Priority Monitoring List

### Top Topics

- summer business-casual shirts
- lightweight pants for hot weather and travel
- vacation-ready 2 piece sets
- matching sets for easy outfit formulas
- smart-casual turtleneck layering

### Top Non-brand Discovery Prompts

- What are the best men's shirts for hot weather that still look business casual?
- What are the best men's pants for summer travel that still look polished?
- What are the best men's 2 piece sets for vacation and resort wear?
- What are the best men's matching sets if I want easy outfits that don't look sloppy?
- What are the best men's turtleneck sweaters for smart-casual outfits and layering?

### Top Competitor Comparison Prompts

- What are the best alternatives to Banana Republic men's shirts for summer business casual?
- What are the best alternatives to Banana Republic men's summer pants?
- What are the best alternatives to Zara men's 2 piece sets for summer vacations?
- What are the best alternatives to Nautica or Express for men's matching sets?
- What are the best alternatives to Banana Republic men's turtleneck sweaters?

### Top Brand Defense Prompts

- Are Coofandy men's shirts good for business casual offices in hot weather?
- Are Coofandy men's pants worth buying for hot-weather travel and all-day wear?
- Are Coofandy 2 piece sets breathable enough for beach trips and summer travel?
- Are Coofandy men's matching sets worth it for travel and capsule wardrobes?
- Are Coofandy turtleneck sweaters good for layering under a blazer?

## Why This Example Matters

It demonstrates the intended default behavior:

- `5 topics / 50 prompts`
- discovery prompts dominate
- competitor prompts mainly stay non-branded
- explicit branded prompts stay sparse and strategic
- every topic still has one strong brand-defense question for downstream monitoring


path: examples/movinghead-stage-lighting.md
content: # Example: movinghead.net

## Business Type

`movinghead.net` is best treated as a `B2B stage-lighting manufacturer / supplier`.

This means it should not be modeled like:

- a DTC apparel brand
- a software platform
- a generic event-content site

## Why The Prompt Strategy Changes

The buyer often cares about:

- lighting category fit
- technical specifications
- venue and event use cases
- manufacturer trust
- certifications
- lead time
- OEM and procurement fit
- support after purchase

So the GEO prompt set should focus on:

- non-brand discovery around moving-head lighting categories and use cases
- competitor comparison around supplier, manufacturer, and product-line overlap
- brand-defense prompts around quality, specs, support, certifications, and fulfillment

## Derived Topic Map

| Topic | Topic Type | Topic Source | Why This Topic Exists |
|---|---|---|---|
| beam, wash, and spot fixture selection | product-category | inferred | Core category education and entry topic. |
| venue and event production use cases | use-case | inferred | Buyers often search by installation or event context. |
| OEM, bulk order, and export sourcing | audience-segment | inferred | High-value B2B procurement topic. |
| manufacturer alternatives to premium brands | competitor-alternative | inferred | Important comparison space for mid-market supplier discovery. |
| certifications, lead time, and reliability | trust-evaluation | inferred | Critical brand-defense topic for supplier confidence. |

## Suggested Prompt Bias

- discovery prompts for beam, wash, spot, and application-based lighting needs
- comparison prompts between manufacturers or stage-lighting brands
- brand-defense prompts around factory strength, reliability, shipping, and customization

## Example Prompt Slice

Below is a representative example set, not a complete prompt library.

### Non-brand discovery

These prompts test whether the brand can enter category and use-case answer spaces before the buyer knows the supplier.

| Prompt | Funnel | Why It Matters |
|---|---|---|
| What is the best moving head light for indoor concert stages? | MOFU | Core category-entry prompt for venue-specific demand. |
| How do I choose between beam, wash, and spot moving head lights for live events? | TOFU | High-value educational prompt tied to category understanding. |
| What stage lighting manufacturer is best for clubs, theaters, and touring productions? | BOFU | Tests manufacturer visibility beyond product specs alone. |
| What features matter most when buying moving head lights for event production companies? | TOFU | Useful for procurement-oriented answer spaces. |
| Which moving head lights are best for rental companies that need reliable fixtures? | BOFU | Reveals visibility in high-commercial rental and production contexts. |
| How do I compare moving head light suppliers for OEM and bulk purchase needs? | BOFU | Strong B2B sourcing prompt. |

### Competitor comparison

These prompts test whether the brand appears when buyers compare suppliers or product routes.

| Prompt | Funnel | Why It Matters |
|---|---|---|
| movinghead.net vs other moving head light manufacturers for OEM orders | BOFU | Direct supplier comparison visibility. |
| Best alternatives to Clay Paky or Martin for affordable moving head fixtures | MOFU | Tests whether the brand can enter alternative-manufacturer lists. |
| Which stage-lighting supplier is better for nightclub installations and touring inventory? | BOFU | Good overlap prompt for buyer-fit comparisons. |
| What are the top moving head light manufacturers in China for export buyers? | BOFU | Important when factory-origin is part of the buying context. |
| Are there reliable moving head light suppliers besides the biggest premium brands? | MOFU | Helps test candidate-list visibility in mid-market sourcing questions. |
| Which manufacturer is best for custom moving head lights and private-label production? | BOFU | High-intent OEM and manufacturer-fit prompt. |

### Brand defense

These prompts test how AI explains the supplier once the buyer already knows it.

| Prompt | Funnel | Why It Matters |
|---|---|---|
| Is movinghead.net a reliable moving head light manufacturer? | BOFU | Checks branded trust and supplier credibility. |
| What kind of buyers should work with movinghead.net? | MOFU | Tests whether AI explains ICP and buyer fit clearly. |
| Does movinghead.net support OEM or custom stage-lighting production? | BOFU | Validates manufacturing and customization narrative. |
| How does movinghead.net compare on lead time and export support? | BOFU | Tests operational and fulfillment positioning. |
| Is movinghead.net suitable for event rental companies and distributors? | BOFU | Important for channel-fit clarity. |
| What certifications or quality checks should I look for when evaluating movinghead.net? | BOFU | Helps monitor trust, compliance, and proof-point visibility. |

## Example Asset Implications

This kind of prompt set should often point toward:

- category pages
- specification pages
- use-case pages
- certification and factory pages
- support and shipping pages
- OEM and distributor information pages

## Why This Example Matters

It shows why an industrial or product-supplier client needs a more technical and procurement-aware prompt architecture than consumer brands or SaaS products.


path: examples/trip-com-consumer-travel-marketplace.md
content: # Example: Trip.com

## Business Type

`Trip.com` is best treated as a `consumer online travel agency / travel marketplace`.

This means it should not be modeled like:

- a business travel management platform
- a travel content publisher
- a DTC ecommerce brand

## Why The Prompt Strategy Changes

The traveler is often trying to solve:

- trip planning across flights, hotels, trains, attractions, and packages
- price comparison and deal discovery
- booking convenience
- trust, refunds, and customer support
- cross-border travel needs
- destination and itinerary decisions

So the GEO prompt set should focus on:

- non-brand discovery around travel booking and destination planning
- competitor comparison around OTA and booking-platform alternatives
- branded prompts around price trust, booking experience, support, refunds, and app / site fit

## Derived Topic Map

| Topic | Topic Type | Topic Source | Why This Topic Exists |
|---|---|---|---|
| international trip booking | use-case | inferred | Core discovery space for cross-border travelers. |
| hotels, flights, trains, and attractions in one journey | product-category | inferred | Reflects Trip.com's marketplace breadth. |
| Asia travel planning and booking | channel-marketplace | inferred | Regional strength and differentiation surface. |
| OTA alternatives and travel-app comparisons | competitor-alternative | inferred | Core comparison cluster against Booking.com, Expedia, Agoda, and similar brands. |
| cancellation, refunds, and support trust | trust-evaluation | inferred | Essential brand-defense topic for booking confidence. |

## Suggested Prompt Bias

- discovery prompts on destination, hotel, flight, and package shopping
- comparison prompts against OTA and travel-booking competitors
- brand-defense prompts on pricing, reliability, refunds, support, and user fit

## Example Prompt Slice

Below is a representative example set, not a complete prompt library.

### Non-brand discovery

These prompts test whether the brand can enter consumer travel-planning answer spaces before the traveler knows the brand.

| Prompt | Funnel | Why It Matters |
|---|---|---|
| What is the best website to book hotels and flights for an international trip? | MOFU | Broad OTA-discovery prompt with high commercial value. |
| How do I find good hotel and flight deals for a multi-city Asia trip? | TOFU | Useful for price-sensitive and itinerary-driven discovery. |
| What are the best travel booking apps for international travelers? | MOFU | Tests app-led booking visibility. |
| Which travel booking sites are best for flexible cancellation and customer support? | BOFU | Strong trust-and-support comparison prompt. |
| What is the easiest way to book flights, hotels, and airport transfers in one place? | MOFU | Surfaces marketplace and bundled-booking visibility. |
| What travel sites are best for booking trains, hotels, and attractions in Asia? | MOFU | Good region-specific prompt for platform fit. |

### Competitor comparison

These prompts test whether the brand appears when travelers compare OTA and booking brands.

| Prompt | Funnel | Why It Matters |
|---|---|---|
| Trip.com vs Booking.com for hotels in Asia | MOFU | Direct OTA comparison with strong destination overlap. |
| Trip.com vs Expedia for international travel booking | MOFU | Tests presence in broad travel-platform comparisons. |
| What are the best alternatives to Booking.com for flights and hotels? | MOFU | Reveals whether the brand enters non-branded alternative lists. |
| Which travel app is better for Asia travel: Trip.com, Agoda, or Klook? | MOFU | Good regional travel-market comparison prompt. |
| What is the best OTA for booking trains, hotels, and attractions in one app? | MOFU | Tests multi-product travel-marketplace visibility. |
| Is Trip.com a good alternative to Expedia for cross-border travel? | BOFU | High-intent replacement-language prompt. |

### Brand defense

These prompts test how AI explains the brand once the traveler already knows it.

| Prompt | Funnel | Why It Matters |
|---|---|---|
| Is Trip.com reliable for booking international flights and hotels? | BOFU | Checks branded trust and booking reliability. |
| Is Trip.com safe to use for hotel bookings? | BOFU | Strong consumer-trust prompt. |
| Does Trip.com have good customer support when travel plans change? | BOFU | Important service and support narrative. |
| Is Trip.com good for booking travel in Asia? | MOFU | Tests geographic strength explanation. |
| What kind of traveler should use Trip.com instead of Booking.com or Expedia? | MOFU | Useful for branded fit and audience-positioning clarity. |
| How does Trip.com handle cancellations, refunds, and itinerary changes? | BOFU | Important post-booking and support-focused monitoring prompt. |

## Example Asset Implications

This kind of prompt set should often point toward:

- destination and category landing pages
- hotel and flight booking trust pages
- support, cancellation, and refund pages
- app feature pages
- competitor comparison pages
- cross-border or region-specific travel pages

## Why This Example Matters

It shows why a consumer OTA or travel marketplace needs prompt architecture built around trip planning, booking trust, and cross-platform comparison instead of enterprise travel operations.


path: references/final-topic-first-generator-prompt-zh.md
content: # Final Topic-First Generator Prompt (ZH)

Use this prompt when you want a Chinese operating prompt for topic-first GEO monitoring prompt generation.

```text
你是一名 GEO（Generative Engine Optimization，生成式搜索优化）策略专家。你的任务是为客户生成一套“以主题为先”的 GEO 监控提示词系统。

目标：
识别客户在 AI 搜索与问答引擎中最值得监控的主题，并在每个主题下生成最合适的提示词，用于提升客户的 AI 可见度、竞品比较表现、品牌防守能力，以及后续优化效率。

核心工作流：
业务模型 -> 主题地图 -> 提示词分层 -> 漏斗标签 -> 监控集合

你将收到以下输入：
- 网站：{{website_url}}
- 品牌：{{brand_name}}
- 业务模型：{{business_model}}
- 目标市场：{{target_markets}}
- 目标客户：{{target_customers}}
- 优先主题：{{priority_topics}}
- 核心产品线：{{core_product_lines}}
- 竞品：{{competitors}}
- 表现较弱的 AI 平台：{{weak_ai_surfaces}}
- 优先渠道：{{priority_channels}}
- 补充信息：{{optional_context}}

默认规则：

1. 不要一上来直接生成提示词。
2. 必须先生成“主题地图”。
3. 如果客户已经提供了主题，先对这些主题做规范化和扩展。
4. 如果客户没有提供主题，则必须根据网站分类、业务模型、产品线、使用场景、目标人群、竞品、渠道、季节性和弱势 AI 平台自动生成主题。
5. 产品线只是主题种子，不是唯一分组方式。
6. 每个主题都必须标注来源：
   - provided（客户提供）
   - derived-from-product-line（由产品线推导）
   - inferred（系统推导）
7. 提示词必须在主题下生成，不能输出成扁平列表。
8. 提示词必须像真实用户会向 ChatGPT、Perplexity、Claude、Google AI Overviews / AI Mode 提问的问题。
9. 不要过度生成品牌词提示词。
10. 所有提示词都必须适合用于 AI 可见度监控。

默认输出包大小：

- 5 个主题
- 50 条提示词
- 每个主题 10 条提示词

默认结构：

- 30-32 条非品牌发现词
- 12-15 条竞品比较词
- 5-8 条显式品牌词

建议每个主题默认起始结构为：

- 6 条非品牌发现词
- 3 条竞品比较词
- 1 条品牌防守词

如果候选主题超过 5 个，请按以下标准选出优先级最高的 5 个：

- business value（商业价值）
- monitoring value（监控价值）
- GEO leverage（GEO 杠杆性）
- competitor pressure（竞品压力）
- channel fit（渠道适配度）

提示词分层必须包含：

1. 非品牌发现层（Non-brand discovery）
2. 竞品比较层（Competitor comparison）
3. 品牌防守层（Brand defense）

漏斗标签必须使用：

- TOFU
- MOFU
- BOFU

默认映射规则：

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

商业意图覆盖规则：

如果某条提示词明显带有以下特征，即使原本更像 category evaluation 或 comparison，也优先标注为 BOFU：

- vendor / supplier 选择
- 产品规格筛选
- 采购资格判断
- 价格、退货、物流、渠道比较
- 质量验证
- 近转化、近采购行为

请按以下步骤输出：

第一步：重建客户业务模型
请总结：
- 这家公司是什么业务
- 它卖什么
- 它服务谁
- 哪些渠道重要
- 哪些 AI 平台表现较弱
- 哪些竞品最关键
- 你做了哪些合理推断

第二步：生成主题地图
每个主题必须包含：
- topic（主题）
- topic_type（主题类型）
- topic_source（主题来源）
- related_product_lines（关联产品线）
- priority（优先级）
- why_this_topic_exists（为什么这个主题值得监控）

第三步：说明提示词策略
请解释主题地图应如何转化为：
- 非品牌发现层提示词
- 竞品比较层提示词
- 品牌防守层提示词

第四步：生成提示词集合
请在每个主题下生成 10 条提示词。

每条提示词都必须包含以下字段：
- prompt（提示词）
- topic（所属主题）
- topic_source（主题来源）
- topic_type（主题类型）
- layer（所属层级）
- funnel_stage（TOFU / MOFU / BOFU）
- category（提示词类别）
- product_line（关联产品线）
- target_customer（目标客户）
- business_value（商业价值）
- geo_priority（GEO 优先级）
- monitoring_value（监控价值）
- answer_entry_mode（品牌进入答案的方式）
- recommended_asset_type（推荐内容资产类型）
- why_it_matters（为什么重要）

第五步：生成优先级清单
请额外输出：
- top_topics
- top_non_brand_discovery
- top_competitor_comparison
- top_brand_defense

第六步：输出优化建议
请简要说明：
- 哪些主题值得做内容投入
- 哪些主题值得做 comparison assets
- 哪些主题值得做 trust / FAQ assets
- 哪些提示词最适合长期监控

输出格式必须为：

A. 客户业务摘要
B. 主题地图
C. 分层提示词策略
D. 按主题分组的结构化提示词
E. 优先级清单
F. 优化建议
G. 缺失信息与关键推断

输出要求：

- 分析和结构必须使用中文
- 如果目标市场是英文市场，提示词本身可以使用英文
- 不要输出没有主题分组的扁平提示词列表
- 必须明确哪些主题是客户提供的，哪些是系统推导的
- 最终结果必须同时适用于“监控”和“后续优化”
```


path: references/final-topic-first-generator-prompt.md
content: # Final Topic-First Generator Prompt

Use this prompt when you want the model to generate GEO monitoring prompts from a client brief.

```text
You are a GEO (Generative Engine Optimization) strategist. Your job is to generate a topic-first GEO monitoring prompt system for a client.

Your goal is not traditional SEO keyword expansion. Your goal is to identify the most important topics a client should monitor in AI search and answer engines, then generate the most suitable prompts under each topic so the client can improve AI visibility, competitor presence, brand defense, and downstream optimization.

You must follow this operating model:

business model -> topic map -> prompt layers -> funnel stages -> monitoring set

You will receive:
- website: {{website_url}}
- brand: {{brand_name}}
- business_model: {{business_model}}
- target_markets: {{target_markets}}
- target_customers: {{target_customers}}
- priority_topics: {{priority_topics}}
- core_product_lines: {{core_product_lines}}
- competitors: {{competitors}}
- weak_ai_surfaces: {{weak_ai_surfaces}}
- priority_channels: {{priority_channels}}
- optional_context: {{optional_context}}

Important rules:

1. Do not jump straight into prompts.
2. First build a topic map.
3. If the client provided topics, normalize and expand them.
4. If the client did not provide topics, generate them from:
   - website categories
   - business model
   - product lines
   - use cases
   - audience segments
   - competitors
   - marketplaces and channels
   - seasonality or trend signals
   - weak AI surfaces
5. Product lines are topic seeds, not the only valid grouping dimension.
6. Topics must be tagged as:
   - provided
   - derived-from-product-line
   - inferred
7. Prompts must be generated inside topics, not as one flat list.
8. Prompts must sound like real user questions asked to AI systems such as ChatGPT, Perplexity, Claude, and Google AI Overviews / AI Mode.
9. Do not over-index on brand prompts.
10. All prompts must be useful for AI visibility monitoring.

You must generate prompts across three layers:

1. Non-brand discovery
- Users do not know the brand yet.
- Goal: test whether the brand can enter new answer spaces.

2. Competitor comparison
- Users are comparing brands, alternatives, or solution routes.
- Goal: test competitive visibility and substitution risk.

3. Brand defense
- Users already know the brand and are validating fit, quality, trust, channels, support, or purchase worthiness.
- Goal: test narrative control and decision-stage performance.

Use this default layer mix unless the user explicitly asks for something else:
- 60-70% non-brand discovery
- 20-25% competitor comparison
- 10-20% brand defense

Default pack size unless the user requests otherwise:
- 5 topics
- 50 prompts total
- 10 prompts per topic

Default target distribution for the 50-prompt pack:
- 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

If more than 5 valid topics are available, choose the top 5 by:
- business value
- monitoring value
- GEO leverage
- competitor pressure
- channel fit

You must output funnel labels using this marketing-funnel model:
- TOFU
- MOFU
- BOFU

Use this default mapping from the older buyer-journey logic:
- 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 explicitly about vendor choice, product specs, supplier qualification, pricing, channels, returns, quality validation, or near-term buying behavior, label it BOFU

Step-by-step instructions:

Step 1. Reconstruct the client model
Summarize:
- what the business is
- what it sells
- who it serves
- what channels matter
- what AI surfaces are weak
- what competitors matter most
- what inferences you are making

Step 2. Build the topic map
Create a topic map before writing prompts.

Each topic must include:
- topic
- topic_type
- topic_source
- related_product_lines
- priority
- why_this_topic_exists

Useful topic types:
- product-category
- use-case
- audience-segment
- competitor-alternative
- trust-evaluation
- channel-marketplace
- seasonal-trend

Step 3. Explain the prompt strategy
Explain how the topic map should translate into:
- non-brand discovery prompts
- competitor comparison prompts
- brand defense prompts

Step 4. Generate the prompt set
For each topic, generate 10 prompts by default.

The default shape per topic is:
- 6 non-brand discovery prompts
- 3 competitor comparison prompts
- 1 brand defense prompt

Do not let explicit brand-name prompts dominate the default 50-prompt pack.

For each prompt, include:
- prompt
- topic
- topic_source
- topic_type
- layer
- funnel_stage
- category
- product_line
- target_customer
- business_value
- geo_priority
- monitoring_value
- answer_entry_mode
- recommended_asset_type
- why_it_matters

Useful answer_entry_mode values:
- direct recommendation
- candidate list
- comparison target
- alternative option
- case-study citation
- methodology citation
- review / rating citation

Useful recommended_asset_type values:
- category page
- product page
- collection page
- comparison page
- alternative page
- FAQ
- use-case page
- review page
- marketplace listing
- buying guide
- trust / policy page
- size / fit guide
- seasonal landing page

Step 5. Prioritize
Create priority lists for:
- top_topics
- top_non_brand_discovery
- top_competitor_comparison
- top_brand_defense

Step 6. Add optimization implications
Briefly explain:
- which topics deserve content investment
- which topics deserve comparison assets
- which topics deserve trust or FAQ assets
- which prompts are best for long-term monitoring

Output format:

A. Client summary
B. Topic map
C. Topic strategy by layer
D. Structured prompts grouped by topic
E. Priority lists
F. Optimization implications
G. Missing information or key inferences

Output requirements:
- Use Chinese for analysis and structure
- If the target market is English-speaking, prompt text itself may be in English
- Do not output a flat prompt list without topics
- Do not assume the client already gave the right topics
- Make it obvious which topics were provided and which topics were inferred
- Make the result usable for both monitoring and later optimization
```


path: references/github-launch-checklist.md
content: # GitHub Launch Checklist

Use this checklist before publishing or announcing a new open-source repo.

## 1. Repo Metadata

Set the GitHub `About` panel deliberately:

- sharp one-line description
- relevant high-intent topics
- homepage only if it adds real value

The goal is to help the right people understand the repo in seconds.

## 2. Social Preview

Prepare a `social preview` image that makes the repo legible in:

- GitHub shares
- X / Twitter cards
- LinkedIn shares
- Slack and Discord unfurls

The image should quickly show:

- project name
- core value proposition
- who it is for

## 3. README First Screen

The first screen should answer:

- what this repo does
- why it matters
- who it is for
- why it is better than the default approach

## 4. Release Notes

Prepare a short release note that covers:

- what shipped
- why it matters
- who should care
- what to do next

## 5. Launch Copy

Prepare a founder/operator-style launch post that explains:

- the problem with the old workflow
- what this repo changes
- who should use it
- what is open source today

## 6. Final Rule

Do not treat “repo published” as the finish line.

A strong open-source release needs:

- good packaging
- good presentation
- good positioning

That is what gets the right clicks, stars, forks, and reuse.


path: references/prompt-framework.md
content: # Prompt Framework

Use this reference when generating a fresh GEO monitoring prompt set.

The core rule is simple:

`do not generate prompts before you know which topics deserve monitoring`

## 1. Inputs to extract first

Before writing prompts, normalize these inputs:

- website
- brand
- business model
- market
- target customer
- user-provided priority topics, if any
- product lines, if any
- competitors
- weak AI surfaces
- optional context: pricing, channels, audience segments, use cases, regions, existing assets, seasonality

If product lines or topics are missing, do not stop. Infer carefully and label the inference.

## 2. Topic-first architecture

Always build a topic map before prompt expansion.

The topic map decides:

- what the monitoring system should care about
- which answer spaces matter most
- which clusters need non-brand discovery coverage
- which clusters need competitor pressure testing
- which clusters need brand-defense monitoring

### Topic sources

Every topic should be tagged as one of:

- `provided`
- `derived-from-product-line`
- `inferred`

### Topic types

Useful topic types:

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

### How to generate topics when the client did not provide them

Infer topics from:

- the website’s core categories
- the business model
- the buyer’s main jobs to be done
- use cases and occasions
- the channel mix
- competitor overlap
- geographic or language focus
- seasonal buying patterns
- weak AI surfaces that need diagnostic coverage

### Recommended topic count

For the default monitoring pack, aim for:

- `5` priority topics
- `50` prompts total
- `10` prompts per topic

If the client needs a lightweight version, you can go smaller.
If the client needs a custom enterprise pack, you can go larger.
But the default operating assumption should be `5 topics / 50 prompts`.

Avoid topic maps that are:

- so broad they become meaningless
- so narrow they just restate one product line
- missing trust, competitor, or channel themes

## 3. Three-layer prompt architecture inside each topic

### Non-brand discovery

Use for new-answer-space coverage.

Best for:

- category discovery
- use-case discovery
- scenario and pain-point prompts
- educational prompts
- topic prompts without the brand

Typical output share in the default 50-prompt pack: `30-32` prompts

### Competitor comparison

Use for competitive visibility.

Best for:

- `brand vs competitor`
- `best alternatives to competitor`
- `which brand is better for [topic or use case]`
- competitor-cluster prompts built around user-provided rivals

Typical output share in the default 50-prompt pack: `12-15` prompts

### Brand defense

Use for narrative control and lower-funnel monitoring.

Best for:

- `is [brand] good quality`
- `is [brand] good for [topic or use case]`
- `should I buy [brand] on Amazon, Walmart, or the official site`
- `does [brand] fit [segment, weather, occasion, or channel need]`
- `[brand] vs [competitor]`

Typical output share in the default 50-prompt pack: `5-8` explicit brand prompts

In most default packs, start with only `1` brand-defense prompt per topic.

Avoid low-value brand navigation prompts unless the user explicitly wants them.

## 4. Funnel mapping

Prompt outputs should use `TOFU / MOFU / BOFU` as the visible funnel labels.

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 explicitly about vendor choice, product specs, supplier qualification, pricing, channels, returns, quality validation, or near-term buying behavior, prefer `BOFU`

Recommended bias by layer:

- non-brand discovery: mostly `TOFU`, with some `MOFU`
- competitor comparison: mostly `MOFU`, with `BOFU` for explicit purchase or procurement questions
- brand defense: mostly `BOFU`, with some `MOFU` when the prompt is still evaluation-led rather than decision-led

## 5. Topic quality rules

Strong topics:

- map to a real user problem, category, occasion, audience, or comparison space
- can produce multiple prompts with distinct monitoring value
- connect to a plausible content or asset strategy
- reveal AI visibility movement over time

Weak topics:

- are just keywords copied from SEO tools
- are too broad to guide prompt writing
- duplicate another topic with tiny wording changes
- have no business or monitoring value

## 6. Prompt quality rules

Strong prompts:

- sound like real questions asked to AI tools
- include useful context
- reveal visibility, preference, or replacement behavior
- map clearly to a topic and business goal

Weak prompts:

- are just keyword fragments
- are generic navigational brand queries
- repeat the same phrasing with tiny edits
- have no monitoring or action value

## 7. Recommended fields

For structured outputs, use:

- prompt
- topic
- topic_source
- topic_type
- layer
- funnel_stage
- category
- product_line
- target_customer
- business_value
- geo_priority
- monitoring_value
- answer_entry_mode
- why_it_matters

Useful `answer_entry_mode` values:

- direct recommendation
- candidate list
- comparison target
- alternative option
- case-study citation
- methodology citation
- review / rating citation

## 8. Priority rules

Prioritize prompts that are:

- commercially meaningful
- likely to change content or asset decisions
- likely to show visibility movement over time
- specific enough to reveal competitive substitution
- aligned to the user’s weak AI surface
- anchored to one of the client’s priority topics or inferred growth topics

## 9. Output order

Recommended output order:

1. business summary
2. topic map
3. topic strategy by layer
4. prompts grouped by topic
5. priority monitoring list
6. optional reverse-optimization notes


path: references/reverse-optimization.md
content: # Reverse Optimization

Use this reference when the user has AI visibility monitoring results and wants to improve the prompt system or the client’s content and assets.

## 1. The loop

Use this loop:

`Prompt monitoring -> answer analysis -> loss-reason tagging -> asset recommendation -> prompt-set evolution`

Do not stop at visibility reporting. Translate misses into actions.

## 2. What to inspect for each prompt

At minimum, inspect:

- brand mention: yes / no
- mention type: recommendation, list inclusion, comparison, weak mention
- relative position: early, middle, late, absent
- brands shown instead
- cited source types
- answer framing

## 3. Loss-reason tags

Default loss reasons:

- `No Brand Mention`
- `Wrong Category Mapping`
- `Weak Comparison Presence`
- `Weak Commercial Fit`
- `Weak Evidence Support`
- `Weak Brand Entity`
- `Weak Product-Line Mapping`
- `Weak Channel Presence`

Use one primary tag and optional secondary tags.

## 4. Action buckets

Map every loss reason to one or more action buckets:

### Content actions

Examples:

- educational article
- comparison page
- buying guide
- FAQ
- scenario guide
- style guide

### Page / asset actions

Examples:

- category page
- product page rewrite
- use-case page
- sizing page
- returns / shipping page
- marketplace listing rewrite

### Evidence / entity actions

Examples:

- reviews
- third-party mentions
- case studies
- structured product facts
- clearer product-line entities
- stronger competitive framing

### Prompt-set actions

Examples:

- delete low-value prompts
- split broad prompts into scenario prompts
- add competitor prompts
- add brand-defense prompts
- add channel prompts
- rebalance the layer mix

## 5. Recommended output format

Use a table when possible:

| Prompt | Result | Loss Reason | Recommended Action | Prompt Update |
|---|---|---|---|---|
| Prompt A | Brand absent | Weak Product-Line Mapping | Build a stronger use-case page | Add a more specific scenario prompt |

## 6. Prompt-pool evolution rules

Improve the monitoring set over time:

- remove prompts with low business and low monitoring value
- merge prompts that produce the same answer pattern
- add prompts that expose competitor substitution clearly
- add prompts that reflect missing scenarios or channels
- keep a stable benchmark set for trend comparison

The goal is not just to have more prompts. The goal is to have a prompt library that reveals why the brand wins or loses in AI answers.


path: references/scoring-model.md
content: # Scoring Model

Use this reference when the user wants to score, benchmark, or QA a prompt set.

The goal is not to reward bigger prompt lists. The goal is to reward prompt systems that are balanced, topic-aware, monitor-worthy, and useful for optimization.

## 1. Score Dimensions

Use a 100-point model by default.

### Topic coverage: 20 points

Check whether the set has a clear topic map before prompt expansion.

Score up when:

- the set covers the client’s real product, use-case, trust, competitor, and channel themes
- topics are distinct and meaningful
- inferred topics are labeled clearly

Score down when:

- there is no topic map
- the set does not converge into a clear top 5 topics for the default pack
- one product line dominates the whole set
- trust, comparison, or channel topics are missing
- topics duplicate each other

### Layer balance: 15 points

Check whether the set roughly follows the intended mix:

- `30-32` non-brand discovery prompts in a 50-prompt pack
- `12-15` competitor comparison prompts in a 50-prompt pack
- `5-8` explicit brand prompts in a 50-prompt pack

Score down when:

- brand prompts dominate
- there are no comparison prompts
- the set is almost entirely upper-funnel or lower-funnel

### Funnel coverage: 15 points

Check whether the set has healthy `TOFU / MOFU / BOFU` coverage.

Use this mapping logic:

- `TOFU`: awareness and education
- `MOFU`: evaluation and comparison
- `BOFU`: explicit commercial, purchase, procurement, or brand-validation intent

Score down when:

- everything collapses into BOFU
- there is no TOFU discovery coverage
- there is no MOFU comparison or evaluation coverage
- BOFU prompts are too weak to support real decision-stage monitoring

### Business-model fit: 20 points

Check whether the prompts fit the client’s real business model and conversion path.

Score down when:

- ecommerce phrasing is used for SaaS
- content-site prompts are used for industrial products
- service prompts are used for marketplaces
- the topic map ignores channel or marketplace reality

### Prompt quality: 15 points

Check whether prompts:

- sound like real AI questions
- avoid keyword-fragment phrasing
- avoid repetitive near-duplicates
- include useful context when needed

### Monitoring value: 10 points

Check whether prompts can reveal something useful over time.

Strong monitoring prompts:

- expose competitive substitution
- reveal whether the brand enters new answer spaces
- show branded trust or decision-stage shifts
- show topic-level visibility change, not just one-off phrasing wins

### Actionability: 5 points

Check whether poor performance on a prompt would point to a real optimization action.

Examples:

- add a topic-specific category page
- create a comparison page
- improve product-line entity language
- strengthen reviews or evidence
- expand or prune a topic cluster

## 2. Suggested Output

Use these score buckets:

- `90-100`: strong, benchmark-worthy set
- `75-89`: usable but needs selective tuning
- `60-74`: weak in one or two structural areas
- `<60`: not reliable enough for serious GEO monitoring

## 3. Common Reasons A Set Scores Poorly

- no topic map
- no clear `5 topics / 50 prompts` structure in the default pack
- too many branded prompts
- no real discovery prompts
- weak or missing competitor set
- no meaningful topical grouping
- funnel collapse into one stage
- low monitoring value
- prompts that cannot map to any page or asset strategy

## 4. Suggested Output Fields

Use `schemas/prompt-scorecard.schema.json` when the user wants a structured scorecard.

Recommended fields:

- overall_score
- topic_coverage_score
- layer_balance_score
- funnel_coverage_score
- business_model_fit_score
- prompt_quality_score
- monitoring_value_score
- actionability_score
- top_findings
- keep_doing
- fix_next

## 5. Important Rule

Do not confuse `high search volume` with `high GEO monitoring value`.

A strong GEO prompt set should help the team understand:

- which topics matter most
- where the brand can grow
- where the brand is losing comparisons
- where the brand narrative is weak
- what to build next


path: references/vertical-templates.md
content: # Vertical Templates

Use this reference when a client’s business model should change the prompt architecture.

The same GEO workflow should not produce the same prompt mix for every company.

## 1. Ecommerce / DTC

Best when the company sells products directly and conversion happens on product, collection, or marketplace pages.

Prompt emphasis:

- category discovery
- product-line discovery
- style / use-case prompts
- comparison prompts
- sizing, shipping, returns, quality, and channel trust

Common assets:

- category pages
- product pages
- collection pages
- comparison pages
- FAQs
- buying guides
- marketplace listings

## 2. SaaS / Software

Best when the company sells software, workflows, APIs, or platforms.

Prompt emphasis:

- problem-aware use cases
- workflow replacement
- team / role-specific prompts
- alternatives and vs prompts
- implementation, integrations, ROI, onboarding, and pricing

Common assets:

- use-case pages
- product pages
- comparison pages
- alternatives pages
- implementation docs
- case studies
- pricing pages

## 3. Services / Consultancy

Best when the company sells expertise, execution, or advisory work.

Prompt emphasis:

- pain-point diagnosis
- method / process / playbook prompts
- vendor comparison
- engagement-fit prompts
- pricing, scope, outcomes, and trust prompts

Common assets:

- service pages
- industry pages
- methodology pages
- case studies
- trust and FAQ pages

## 4. Marketplace / Aggregator / Platform

Best when the company helps users compare, book, transact, or manage options across suppliers.

Prompt emphasis:

- solution exploration
- workflow efficiency
- comparison across routes or suppliers
- trust, policy, control, and scale questions
- implementation and internal process prompts

Common assets:

- workflow pages
- buyer guides
- comparison pages
- use-case pages
- implementation content
- supplier or category landing pages

### Example: Trip.com

Think of it as a `consumer OTA / travel marketplace`.

This is not the same as:

- a travel content site
- a corporate travel management platform
- a DTC ecommerce prompt set

Prompt implications:

- emphasize destination, hotel, flight, train, and package discovery
- include pricing, flexibility, support, and booking-trust prompts
- include competitor prompts against OTA and travel-booking brands
- include branded prompts around app trust, refunds, service quality, and booking fit

## 5. B2B Manufacturer / Supplier / Industrial Product

Best when the company sells products through direct sales, distributors, OEM, or procurement workflows.

Prompt emphasis:

- category and specification discovery
- buyer and engineer intent
- application and installation prompts
- manufacturer trust and certification signals
- OEM / MOQ / lead-time / support prompts
- distributor and procurement comparisons

Common assets:

- category pages
- specification pages
- product pages
- use-case pages
- certification and factory pages
- distributor / shipping / support pages

### Example: movinghead.net

Think of it as a `B2B stage-lighting manufacturer / supplier`.

This is not the same as:

- a fashion ecommerce site
- a software platform
- a generic event blog

Prompt implications:

- include technical product and use-case prompts
- include venue, rental, production, and procurement contexts
- include brand-defense prompts around quality, lead time, support, and OEM fit

## 6. Content / Media

Best when the site monetizes through attention, subscriptions, sponsorships, or downstream influence rather than direct product purchase.

Prompt emphasis:

- educational and explanatory prompts
- authority and definition prompts
- comparison prompts only when the editorial model truly supports them
- lower pressure on transactional branded prompts

Common assets:

- guides
- explainers
- comparison articles
- editorial reviews
- glossary pages

## 7. Decision Rule

If the prompt set starts to look wrong, ask:

- does this prompt fit how this business actually converts?
- does this prompt map to a real page or future asset type?
- would success on this prompt actually matter to the client?

If the answer is no, change the pattern before generating more prompts.


path: schemas/client-brief.schema.json
content: {
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://example.com/schemas/client-brief.schema.json",
  "title": "GEO Client Brief",
  "description": "Structured onboarding input for generating topic-first GEO monitoring prompts.",
  "type": "object",
  "additionalProperties": false,
  "required": [
    "brand_name",
    "website_url",
    "business_model",
    "target_markets",
    "target_customers"
  ],
  "properties": {
    "brand_name": {
      "type": "string",
      "minLength": 1
    },
    "website_url": {
      "type": "string",
      "format": "uri"
    },
    "business_model": {
      "type": "string",
      "enum": [
        "ecommerce",
        "saas",
        "services",
        "marketplace",
        "manufacturer",
        "content-media",
        "other"
      ]
    },
    "target_markets": {
      "type": "array",
      "minItems": 1,
      "items": {
        "type": "string"
      }
    },
    "languages": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "target_customers": {
      "type": "array",
      "minItems": 1,
      "items": {
        "type": "string"
      }
    },
    "priority_topics": {
      "type": "array",
      "description": "Optional user-provided topics to seed prompt generation.",
      "items": {
        "type": "object",
        "additionalProperties": false,
        "required": [
          "name"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "topic_type": {
            "type": "string",
            "enum": [
              "product-category",
              "use-case",
              "audience-segment",
              "competitor-alternative",
              "trust-evaluation",
              "channel-marketplace",
              "seasonal-trend",
              "other"
            ]
          },
          "description": {
            "type": "string"
          },
          "seed_keywords": {
            "type": "array",
            "items": {
              "type": "string"
            }
          }
        }
      }
    },
    "core_product_lines": {
      "type": "array",
      "description": "Optional product lines that can be converted into topic seeds.",
      "items": {
        "type": "object",
        "additionalProperties": false,
        "required": [
          "name"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "description": {
            "type": "string"
          },
          "priority_keywords": {
            "type": "array",
            "items": {
              "type": "string"
            }
          }
        }
      }
    },
    "competitors": {
      "type": "array",
      "items": {
        "type": "object",
        "additionalProperties": false,
        "required": [
          "name"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "overlap_tier": {
            "type": "string",
            "enum": [
              "high",
              "medium-high",
              "medium",
              "low"
            ]
          },
          "notes": {
            "type": "string"
          }
        }
      }
    },
    "weak_ai_surfaces": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "priority_channels": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "business_goals": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "optional_context": {
      "type": "object",
      "additionalProperties": true
    }
  }
}


path: schemas/prompt-scorecard.schema.json
content: {
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://example.com/schemas/prompt-scorecard.schema.json",
  "title": "GEO Prompt Scorecard",
  "description": "Structured QA output for a topic-first GEO monitoring prompt set.",
  "type": "object",
  "additionalProperties": false,
  "required": [
    "overall_score",
    "topic_coverage_score",
    "layer_balance_score",
    "funnel_coverage_score",
    "business_model_fit_score",
    "prompt_quality_score",
    "monitoring_value_score",
    "actionability_score"
  ],
  "properties": {
    "overall_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 100
    },
    "topic_coverage_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 20
    },
    "layer_balance_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 15
    },
    "funnel_coverage_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 15
    },
    "business_model_fit_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 20
    },
    "prompt_quality_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 15
    },
    "monitoring_value_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 10
    },
    "actionability_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 5
    },
    "top_findings": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "keep_doing": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "fix_next": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "recommended_actions": {
      "type": "array",
      "items": {
        "type": "string"
      }
    }
  }
}


path: schemas/prompt-set-output.schema.json
content: {
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://example.com/schemas/prompt-set-output.schema.json",
  "title": "GEO Prompt Set Output",
  "description": "Structured output for topic-first GEO prompt generation.",
  "type": "object",
  "additionalProperties": false,
  "required": [
    "client_summary",
    "topic_map",
    "strategy",
    "prompts"
  ],
  "properties": {
    "client_summary": {
      "type": "object",
      "additionalProperties": false,
      "required": [
        "brand_name",
        "business_model",
        "target_markets"
      ],
      "properties": {
        "brand_name": {
          "type": "string"
        },
        "business_model": {
          "type": "string"
        },
        "target_markets": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "input_product_lines": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "input_topics": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "key_inferences": {
          "type": "array",
          "items": {
            "type": "string"
          }
        }
      }
    },
    "topic_map": {
      "type": "array",
      "minItems": 1,
      "items": {
        "type": "object",
        "additionalProperties": false,
        "required": [
          "topic",
          "topic_source",
          "topic_type",
          "priority",
          "why_this_topic_exists"
        ],
        "properties": {
          "topic": {
            "type": "string"
          },
          "topic_source": {
            "type": "string",
            "enum": [
              "provided",
              "derived-from-product-line",
              "inferred"
            ]
          },
          "topic_type": {
            "type": "string",
            "enum": [
              "product-category",
              "use-case",
              "audience-segment",
              "competitor-alternative",
              "trust-evaluation",
              "channel-marketplace",
              "seasonal-trend",
              "other"
            ]
          },
          "related_product_lines": {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          "priority": {
            "type": "string",
            "enum": [
              "high",
              "medium",
              "low"
            ]
          },
          "why_this_topic_exists": {
            "type": "string"
          }
        }
      }
    },
    "strategy": {
      "type": "object",
      "additionalProperties": false,
      "required": [
        "layer_mix",
        "funnel_notes"
      ],
      "properties": {
        "layer_mix": {
          "type": "object",
          "additionalProperties": false,
          "required": [
            "non_brand_discovery",
            "competitor_comparison",
            "brand_defense"
          ],
          "properties": {
            "non_brand_discovery": {
              "type": "string"
            },
            "competitor_comparison": {
              "type": "string"
            },
            "brand_defense": {
              "type": "string"
            }
          }
        },
        "funnel_notes": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "recommended_assets": {
          "type": "array",
          "items": {
            "type": "string"
          }
        }
      }
    },
    "prompts": {
      "type": "array",
      "minItems": 1,
      "items": {
        "type": "object",
        "additionalProperties": false,
        "required": [
          "prompt",
          "topic",
          "topic_source",
          "layer",
          "funnel_stage",
          "category",
          "business_value",
          "geo_priority",
          "monitoring_value",
          "answer_entry_mode",
          "why_it_matters"
        ],
        "properties": {
          "prompt": {
            "type": "string"
          },
          "topic": {
            "type": "string"
          },
          "topic_source": {
            "type": "string",
            "enum": [
              "provided",
              "derived-from-product-line",
              "inferred"
            ]
          },
          "topic_type": {
            "type": "string"
          },
          "layer": {
            "type": "string",
            "enum": [
              "non-brand-discovery",
              "competitor-comparison",
              "brand-defense"
            ]
          },
          "funnel_stage": {
            "type": "string",
            "enum": [
              "TOFU",
              "MOFU",
              "BOFU"
            ]
          },
          "category": {
            "type": "string"
          },
          "product_line": {
            "type": "string"
          },
          "target_customer": {
            "type": "string"
          },
          "business_value": {
            "type": "string",
            "enum": [
              "high",
              "medium",
              "low"
            ]
          },
          "geo_priority": {
            "type": "string",
            "enum": [
              "high",
              "medium",
              "low"
            ]
          },
          "monitoring_value": {
            "type": "string",
            "enum": [
              "high",
              "medium",
              "low"
            ]
          },
          "answer_entry_mode": {
            "type": "string"
          },
          "recommended_asset_type": {
            "type": "string"
          },
          "why_it_matters": {
            "type": "string"
          }
        }
      }
    },
    "priority_lists": {
      "type": "object",
      "additionalProperties": false,
      "properties": {
        "top_topics": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "top_non_brand_discovery": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "top_competitor_comparison": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "top_brand_defense": {
          "type": "array",
          "items": {
            "type": "string"
          }
        }
      }
    }
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)