aeo-content-strategy

name	aeo-content-strategy
description	AEO/GEO content strategy skill that combines Reddit and Quora monitoring, long-tail question mining, and content topic recommendations into one actionable report. Use this skill whenever the user wants to: discover what people are asking about their product category on Reddit or Quora, find unanswered or low-competition long-tail questions for AEO optimization, generate blog topic ideas based on real community discussions and search intent, build a content calendar targeting AI citation opportunities, understand what questions AI tools like ChatGPT/Perplexity/Gemini might pull answers for, do competitive content gap analysis for AI search visibility, or plan content that targets specific user decision-making questions. Also trigger when user mentions 'AEO', 'answer engine optimization', 'AI search visibility', 'what should I write about', 'content gaps', 'Reddit monitoring', 'community listening', 'long-tail keywords for AI', or 'content strategy for LLM citations'. This skill produces a comprehensive report — not just raw data — with prioritized, actionable recommendations.
metadata	{"openclaw":{"emoji":"📡","homepage":"https://github.com/jrr996shujin-png/openclaw-seo-aeo-skills"}}

AEO Content Strategy: Community Monitoring + Long-Tail Mining + Content Planning

What This Skill Does

This skill generates a complete AEO content strategy by combining three interconnected analyses:

1. Reddit/Quora Community Monitoring — Discovers what real users are asking, complaining about, and recommending in communities relevant to the user's product
2. Long-Tail Question Mining — Transforms community signals and product knowledge into specific, conversational questions that AI platforms are likely to surface
3. Content Topic Recommendations — Prioritizes which content to create first based on competition level, purchase intent, and AI citation potential

The output is a single actionable report the user can hand to their content team and start executing immediately.

Why This Combination Matters

These three activities form a natural pipeline. Community discussions reveal what real users care about (not what keyword tools think they care about). Those discussions contain raw long-tail questions that no one has properly answered yet. And those unanswered questions become high-value content opportunities — because when AI searches for answers and finds only your content addressing a specific question, it has no choice but to cite you.

Doing these separately wastes time and loses the connections between them. A Reddit thread about "frustrating email tools" directly feeds into a long-tail question like "what AI tool can automatically sort and reply to customer emails in my brand voice" which directly becomes a blog topic recommendation.

Required Inputs

Before starting, collect these from the user:

Input	Why It's Needed	Example
Product/brand name	To search for direct mentions	"Genspark"
Product category	To search for category discussions	"AI agent", "AI productivity tool"
Target audience	To calibrate question language and intent	"Solo entrepreneurs and small teams"
2-3 competitor names	To monitor competitor mentions and find gaps	"Manus, ChatGPT, Perplexity"
Key use cases (3-5)	To focus long-tail mining on real scenarios	"Email automation, meeting notes, research"
Target language/market	To determine which communities to scan	"English, US market"

If the user doesn't provide all of these, ask for the missing ones before proceeding. The quality of the output depends heavily on having clear inputs.

Execution Steps

Phase 1: Reddit/Quora Community Scan

Search Reddit and Quora for recent discussions (prioritize last 6 months) using these query patterns:

Direct brand searches:

• "{brand name}" site:reddit.com
• "{competitor 1}" OR "{competitor 2}" site:reddit.com

Category searches:

• "best {product category}" site:reddit.com
• "{product category} recommendation" site:reddit.com
• "{product category} vs" site:reddit.com
• "looking for {product category}" site:reddit.com
• "{product category}" site:quora.com

Problem/pain-point searches:

• "{use case 1} frustrated" OR "help" OR "alternative" site:reddit.com
• "how to {use case 2}" site:reddit.com

Subreddit-specific searches (identify 3-5 relevant subreddits first):

• Search within subreddits like r/productivity, r/artificial, r/SaaS, r/smallbusiness, r/Entrepreneur, etc. depending on the product category

For each relevant thread found, extract:

• The original question or complaint (exact user language)
• Number of upvotes and comments (signals engagement/demand)
• Whether any brand was recommended in top responses
• Whether the question was adequately answered or left unanswered
• The subreddit it appeared in

Aim to collect 30-50 relevant threads across all searches.

Phase 2: Signal Analysis

Categorize the collected threads into:

Category A — Unanswered or Poorly Answered Questions These are gold. No one has properly answered them, meaning content you create could become the only source AI can cite.

Category B — Questions Where Competitors Are Recommended But You're Not These reveal gaps in your brand visibility. Someone asked for a tool like yours and your competitors got mentioned but you didn't.

Category C — Questions Where Your Brand Is Mentioned Track sentiment — are mentions positive, negative, or neutral? What specific features or limitations do users highlight?

Category D — General Category Discussions Broader discussions about the product category that reveal user priorities, decision criteria, and common misconceptions.

Phase 3: Long-Tail Question Generation

Transform the community signals into specific, conversational long-tail questions (25+ words each). These are the exact questions users would ask an AI assistant.

Sources for question generation:

1. From Category A threads — Rephrase unanswered Reddit questions into natural AI conversation format
2. From Category B threads — Create questions where your product could be the answer
3. From user's key use cases — Generate specific scenario-based questions for each use case
4. From competitor comparison angles — Create "X vs Y for [specific scenario]" questions
5. From customer journey stages — Questions at awareness, consideration, and decision stages

Question format guidelines:

• Write them as a real person would ask ChatGPT or Perplexity, not as SEO keywords
• Include context and constraints (team size, budget, specific needs)
• Make each question specific enough that only 1-3 tools could properly answer it
• Vary the format: "What's the best...", "How do I...", "Can [tool] do...", "I need something that..."

Example transformations:

Reddit thread: "Anyone know a good tool for automating email responses? I run a small Etsy shop and spend 2 hours/day on customer emails"

Generated long-tail questions:

• "I run a small e-commerce shop on Etsy and spend too much time replying to customer emails. Is there an AI tool that can learn my reply style and auto-draft responses to common questions like shipping times and return policies?"
• "What's the best AI email assistant for solo e-commerce sellers who get 50-100 customer emails per day and need responses that don't sound robotic?"
• "Can an AI tool automatically sort customer emails into categories like shipping questions, complaints, and product inquiries and draft different response templates for each?"

Generate at least 30 long-tail questions, aiming for 40-50.

Phase 4: Content Topic Prioritization

Score each long-tail question cluster on three dimensions:

1. Competition Level (Low / Medium / High)

• Low: No existing content directly answers this specific question (confirmed by web search)
• Medium: 1-3 articles exist but are generic or outdated
• High: Multiple high-quality articles already cover this exact topic

2. Purchase Intent (Low / Medium / High)

• Low: Informational curiosity ("what is AI email automation")
• Medium: Active research ("best AI email tools for small business")
• High: Decision-ready ("Genspark vs Manus for email automation pricing")

3. AI Citation Potential (Low / Medium / High)

• Low: Topic is well-covered by authoritative sources; AI already has good answers
• Medium: Some coverage exists but lacks specific angles or updated data
• High: Little to no direct coverage; your content would fill a clear gap

Priority formula: High priority = Low competition + High intent + High AI citation potential

Phase 5: Report Assembly

Compile everything into a structured report with these sections:

---

Output Report Structure

ALWAYS use this exact template for the final report:

# AEO Content Strategy Report: [Brand Name]
Generated: [Date]

## Executive Summary
[3-4 sentences: key findings, biggest opportunities, recommended immediate actions]

## Part 1: Community Landscape

### Brand Mentions Overview
| Platform | Your Brand Mentions | Competitor A Mentions | Competitor B Mentions |
|----------|-------------------|---------------------|---------------------|
| Reddit   | [count]           | [count]             | [count]             |
| Quora    | [count]           | [count]             | [count]             |

### Sentiment Summary
[Brief analysis of how your brand vs competitors are being discussed]

### Top Unanswered Questions (Category A)
[List the 10 most promising unanswered questions from Reddit/Quora with source links]

### Competitor Visibility Gaps (Category B)
[List 5-10 threads where competitors were recommended but you weren't]

## Part 2: Long-Tail Question Bank

### High-Priority Questions (Top 15)
[Each question with: the question itself, source context, competition level, intent level, AI citation potential]

### Medium-Priority Questions (Next 15)
[Same format]

### Additional Questions (Remaining)
[Shorter format, just the questions grouped by theme]

## Part 3: Content Recommendations

### Immediate Actions (This Month) — Top 5 Topics
For each topic:
- **Recommended title**: [SEO and AEO optimized title]
- **Target questions answered**: [Which long-tail questions this article addresses]
- **Content format**: [Guide / Comparison / Tutorial / Case study]
- **Key sections to include**: [H2 outline]
- **Unique angle**: [What makes this different from existing content]
- **Estimated word count**: [Based on topic depth needed]

### Next Quarter — Topics 6-15
[Shorter format: title, target questions, format, unique angle]

### Content Calendar Suggestion
| Week | Topic | Format | Target Questions | Priority |
|------|-------|--------|-----------------|----------|
| 1    |       |        |                 |          |
| 2    |       |        |                 |          |
| ...  |       |        |                 |          |

## Part 4: Ongoing Monitoring Recommendations

### Subreddits to Watch
[List 5-10 subreddits with explanation of why each matters]

### Search Queries to Track Weekly
[List of 10-15 Reddit/Quora search queries to run regularly]

### Competitor Content to Monitor
[List competitor blogs and specific content types to watch]

## Appendix: Raw Data
### All Reddit/Quora Threads Collected
[Table: Thread title | URL | Subreddit/Topic | Upvotes | Comments | Category (A/B/C/D) | Key insight]

---

Quality Checks Before Delivery

Before finalizing the report, verify:

• Every recommended topic has a clear "unique angle" — if your content would say the same thing as existing articles, it won't get cited by AI
• Long-tail questions are truly conversational (25+ words) and not just keyword phrases
• Priority scoring is consistent — double-check that "high priority" items genuinely have low competition
• Content calendar is realistic for a small team (not recommending 10 articles per week)
• Recommendations include specific structural advice (use tables for comparisons, FAQ sections for question-based content, H2 headers that match how users phrase questions)
• Each recommended article includes which AI platform citation it's primarily targeting (ChatGPT favors third-party consensus, Perplexity favors Reddit and niche directories, Gemini favors structured owned content)

Important Reminders

• Reddit and Quora data is publicly accessible but rate-limited. If searches fail, retry with slight query variations.
• The value of this skill is in the CONNECTIONS between community data and content recommendations, not in the raw data alone. Always explain WHY a topic is recommended, not just WHAT to write.
• Content recommendations should follow the "information gain" principle from AEO best practices: only recommend topics where the user can provide genuinely new information (original data, unique expertise, first-hand testing) that doesn't already exist online.
• When competition is high for a topic, recommend a specific sub-angle rather than the broad topic. "Best AI tools" is saturated; "Best AI tools for Etsy sellers who handle 50+ daily customer emails" probably isn't.