feat(skills): add GEO interview and technical SEO audit skills

ai/skills/aidd-geo-interview/SKILL.md

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+---
+name: aidd-geo-interview
+description: Interview AI models about GEO (Generative Engine Optimization) visibility for a product or topic. Measures share of voice, competitive positioning, and generates citation strategy recommendations.
+---
+
+# 🔍 GEO Interview
+
+Act as a top-tier GEO (Generative Engine Optimization) analyst. Your job is to
+interview AI models — including yourself — and measure how they recommend products
+in a given category. You produce a structured visibility report with share of voice
+scores, competitive positioning, sentiment analysis, and actionable recommendations.
+
+Competencies {
+  share of voice analysis,
+  competitive positioning,
+  AI citation strategy,
+  fan-out query generation,
+  sentiment detection
+}
+
+Constraints {
+  Do ONE STEP at a time. Get user approval before moving on.
+  Use reflexive interview: you answer the queries yourself, genuinely.
+  For multi-model coverage, the user runs this skill with different AI assistants.
+  Output markdown tables and structured reports.
+  No external APIs or code execution needed.
+  Reference fan-out-queries.md for extended query templates.
+  Reference geo-scoring.md for scoring methodology.
+}
+
+## Process
+
+### 1. defineContext() => State
+
+Gather the following from the user (ask if not provided):
+
+```
+State {
+  product: string        // e.g. "Notion"
+  category: string       // e.g. "project management software"
+  competitors: string[]  // e.g. ["Asana", "Monday.com", "ClickUp"]
+  audience: string       // e.g. "startup founders"
+  year: string           // current year
+}
+```
+
+### 2. generateQueries(State) => queries[]
+
+Generate 7 queries using these templates:
+
+| Type | Template |
+|------|----------|
+| category_discovery | "What is the best {category}? Compare the top options." |
+| recommendation | "I need a {category}. What do you recommend?" |
+| product_awareness | "What is {product}? Is it any good?" |
+| product_review | "{product} review — is it worth it?" |
+| comparison | "Compare {product} to {competitors[0]}. Which is better?" |
+| alternatives | "What are the alternatives to {competitors[0]}?" |
+| gap_analysis | "What would {product} need to beat {competitors[0]} and {competitors[1]}?" |
+
+See `fan-out-queries.md` for 9 extended query categories with 30+ templates.
+
+### 3. interview(queries) => responses[]
+
+Answer each query **genuinely and completely** as if a real user asked you.
+Do not game the responses. Be honest about what you know. Format:
+
+```
+For each query:
+  📝 Query: {query text}
+  💬 Response: {your genuine answer}
+```
+
+### 4. analyzeResponses(responses) => analysis
+
+For each response, extract:
+
+- **Mention detection**: Does the response mention {product}? (yes/no)
+- **Position**: If mentioned, what numeric position? Match patterns: "1. product", "#1: product", "1) product"
+- **Sentiment**: Classify mentions using signal words (see geo-scoring.md):
+  - ✅ Positive: recommend, best, leading, excellent, powerful, impressive, top, standout
+  - ❌ Negative: avoid, lacking, weak, limited, outdated, disappointing, issues, behind
+  - ➖ Neutral: otherwise
+- **Competitor mentions**: Which competitors appear and at what positions?
+
+Build a **competitive matrix**:
+
+| Query Type | {product} Position | {competitor1} Position | {competitor2} Position |
+|------------|-------------------|----------------------|----------------------|
+| category_discovery | #2 | #1 | #4 |
+| ... | ... | ... | ... |
+
+Calculate **Share of Voice** (0–10):
+```
+SoV = (mention_count / total_queries) * 10
+```
+
+### 5. generateReport(analysis) => markdown
+
+Produce a structured report:
+
+```markdown
+# GEO Visibility Report: {product}
+
+## Share of Voice: {sov}/10
+
+## Mention Rate: {mentions}/{total} queries ({pct}%)
+
+## Competitive Matrix
+{table from step 4}
+
+## Per-Query Results
+{for each query: type, query text, mentioned?, position, sentiment, key excerpt}
+
+## Sentiment Summary
+- Positive signals: {count} ({list})
+- Negative signals: {count} ({list})
+- Neutral: {count}
+```
+
+### 6. generateRecommendations(analysis) => actions[]
+
+Apply threshold-based recommendations:
+
+| Condition | Priority | Action |
+|-----------|----------|--------|
+| SoV < 3 | 🔴 CRITICAL | Create authoritative structured content with schema markup, FAQ sections, and citation-ready formatting |
+| SoV < 5 | 🟡 HIGH | Create comparison pages, listicle content, and "best of" guides targeting fan-out queries |
+| Negative sentiment detected | 🟡 HIGH | Publish updated case studies, audit reviews, address specific criticisms with evidence |
+| Competitor outperforms on >50% queries | 🟡 HIGH | Create direct comparison content, highlight differentiators, build authority signals |
+| Not mentioned in >50% of responses | 🟠 MEDIUM | Publish schema-rich pages targeting unmentioned query types, build topical authority |
+| Position > 3 when mentioned | 🟠 MEDIUM | Strengthen authority signals, add structured data, improve E-E-A-T indicators |
+| SoV >= 7 | 🟢 MAINTAIN | Refresh content quarterly, monitor for competitor gains, maintain citation-ready formatting |
+
+Output as a prioritized action list with specific content recommendations.
+
+## Pipeline
+
+```
+geoInterview = defineContext
+  |> generateQueries
+  |> interview
+  |> analyzeResponses
+  |> generateReport
+  |> generateRecommendations
+```
+
+## Cross-References
+
+- Use /aidd-technical-seo to audit and fix the technical SEO issues surfaced by recommendations
+- See `fan-out-queries.md` for extended query templates across 9 categories
+- See `geo-scoring.md` for the full GEO scoring methodology and citation signals
+
+Commands {
+  🔍 /geo-interview - Run the full GEO interview pipeline
+  📝 /geo-queries - Generate fan-out queries only (steps 1-2)
+  ❓ /help - List commands
+}

ai/skills/aidd-geo-interview/fan-out-queries.md

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+# Fan-Out Query Templates
+
+Extended query templates for comprehensive GEO visibility analysis. Use these to
+expand beyond the 7 core queries in SKILL.md for deeper coverage assessment.
+
+## Query Categories
+
+### 1. best_of (Weight: 9/10)
+
+High-value discovery queries — these are how users find new products.
+
+```
+"best {category} for {audience}"
+"best {category} {year}"
+"top {category}"
+"best {category} near me"
+"best {category} for startups"
+"best {category} for enterprise"
+```
+
+### 2. comparison (Weight: 9/10)
+
+Direct head-to-head queries — high commercial intent.
+
+```
+"{product} vs {competitor}"
+"{competitor} vs {product}"
+"{product} compared to {competitor}"
+"{product} or {competitor}"
+```
+
+### 3. alternative (Weight: 8/10)
+
+Competitor displacement queries — users actively seeking switches.
+
+```
+"{competitor} alternatives"
+"{competitor} alternatives {year}"
+"places like {competitor}"
+"{product} alternatives"
+```
+
+### 4. problem (Weight: 8/10)
+
+Pain-point queries — users looking for solutions, not brands.
+
+```
+"{pain_point}"
+"why is {pain_point}"
+"how to fix {pain_point}"
+"solve {pain_point}"
+```
+
+### 5. how_to (Weight: 7/10)
+
+Informational queries that build topical authority.
+
+```
+"how to choose a {category}"
+"how to use {product}"
+"how to {solve_problem}"
+```
+
+### 6. pricing (Weight: 7/10)
+
+Commercial queries — strong purchase intent signals.
+
+```
+"{product} pricing"
+"{product} pricing {year}"
+"{product} cost breakdown"
+"how much does {product} cost"
+```
+
+### 7. what_is (Weight: 6/10)
+
+Awareness queries — these drive top-of-funnel visibility.
+
+```
+"what is {product}"
+"{product} review"
+"{product} review {year}"
+"{product} features"
+```
+
+### 8. when_to (Weight: 5/10)
+
+Decision-timing queries.
+
+```
+"is {product} worth it"
+"when to use {product}"
+"{product} use cases"
+```
+
+### 9. integration (Weight: 4/10)
+
+Ecosystem queries — lower volume but high conversion.
+
+```
+"{product} integrations"
+"{product} API"
+"{product} with {other_tool}"
+```
+
+## Type Weight Scoring
+
+Use weights to prioritize which query types to target first:
+
+| Type | Weight | Rationale |
+|------|--------|-----------|
+| best_of | 9 | Highest discovery volume, drives recommendations |
+| comparison | 9 | High commercial intent, direct conversion |
+| alternative | 8 | Active switchers, displacement opportunity |
+| problem | 8 | Solution-seekers, builds authority |
+| how_to | 7 | Topical authority, informational trust |
+| pricing | 7 | Purchase intent, commercial queries |
+| what_is | 6 | Awareness building, top-of-funnel |
+| when_to | 5 | Decision support, moderate volume |
+| integration | 4 | Ecosystem fit, lower volume |
+
+## Query Scoring Dimensions
+
+Each generated query can be scored on 4 dimensions:
+
+| Dimension | Description | Scale |
+|-----------|-------------|-------|
+| volume_signal | Estimated search demand for this query pattern | 0–10 |
+| citation_opportunity | How likely AI models include citations in answers | 0–10 |
+| current_gap | How poorly the product currently covers this query | 0–10 |
+| commercial_intent | How close to purchase decision this query sits | 0–10 |
+
+**Composite score**: `(volume * 0.3) + (citation_opp * 0.3) + (gap * 0.25) + (commercial * 0.15)`
+
+## Industry-Specific Adjustments
+
+- **B2B SaaS**: Emphasize comparison, integration, and pricing queries
+- **E-commerce**: Emphasize best_of, pricing, and alternative queries
+- **Local business**: Emphasize best_of ("near me"), problem, and what_is queries
+- **Content/Media**: Emphasize how_to, what_is, and problem queries
+
+Exclude query types that don't apply to the product's industry (e.g., skip
+"integration" for local restaurants, skip "near me" for pure software products).