# Aeo Content Strategy > 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… - **Type:** Skill - **Install:** `agentstack add skill-jrr996shujin-png-openclaw-seo-aeo-skills-aeo-content-strategy` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [jrr996shujin-png](https://agentstack.voostack.com/s/jrr996shujin-png) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [jrr996shujin-png](https://github.com/jrr996shujin-png) - **Source:** https://github.com/jrr996shujin-png/openclaw-seo-aeo-skills/tree/main/aeo-content-strategy ## Install ```sh agentstack add skill-jrr996shujin-png-openclaw-seo-aeo-skills-aeo-content-strategy ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [jrr996shujin-png](https://github.com/jrr996shujin-png) - **Source:** [jrr996shujin-png/openclaw-seo-aeo-skills](https://github.com/jrr996shujin-png/openclaw-seo-aeo-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-jrr996shujin-png-openclaw-seo-aeo-skills-aeo-content-strategy - Seller: https://agentstack.voostack.com/s/jrr996shujin-png - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.