AEO Prompt Research (Free)
Source: github.com/psyduckler/aeo-skills
Part of: AEO Skills Suite — Prompt Research → Content → Analytics
Discover which prompts and topics matter for a brand's AI visibility — using zero paid APIs.
Requirements
- -
web_fetch — crawl the target site - INLINECODE1 — Brave Search free tier (optional but recommended)
- LLM reasoning — the agent's own model does the heavy lifting
No API keys, no paid tools, no accounts needed.
Workflow
Input
The user provides:
- - Domain URL (required) — e.g. INLINECODE2
- Niche/category (optional) — e.g. "SEO software for content teams"
- Competitors (optional) — e.g. "Surfer SEO, MarketMuse, Frase"
Step 1: Crawl the Site
Use web_fetch on key pages to understand the brand:
CODEBLOCK0
Alternatively, run scripts/crawl_site.sh <domain> for a batch crawl.
Extract from crawled content:
- - Core product/service offering
- Target audience (industry, company size, persona)
- Key differentiators / value props
- Competitor mentions
- Content themes from blog titles
Step 2: Discover the Topic Universe
Using the brand understanding, brainstorm topic categories. For methodology and category types, read references/aeo-methodology.md.
Core prompt categories to generate:
- 1. Problem-aware — "How do I solve [problem]?"
- Solution-aware — "What tools exist for [category]?"
- Comparison — "[Brand] vs [competitor]"
- Best-of — "Best [category] for [use case]"
- How-to — "How to [task the product helps with]"
- Evaluation — "Is [brand] good for [need]?"
- Industry — "[Industry] trends / best practices"
Step 3: Generate Prompts
For each category, generate 5-15 specific prompts people would actually ask an AI assistant.
Guidelines:
- - Write naturally — how people talk to ChatGPT, not how they Google
- Be specific — include context (company size, industry, use case)
- Vary intent — research, comparison, how-to, buying decision
- Avoid jargon-heavy or unrealistic prompts
Step 4: Prioritize
Score each prompt (1-5) on:
- - Relevance — How closely tied to the brand's core offering?
- Volume potential — How many people likely ask this?
- Winability — Can this brand realistically be the best answer?
- Intent value — Does this indicate buying/conversion intent?
Formula: INLINECODE6
Sort into Tier 1 (≥3.5), Tier 2 (2.5-3.4), Tier 3 (<2.5).
Step 5: Audit Existing Coverage
For Tier 1 prompts, use web_search with site:domain.com [topic keywords] to check if content already exists.
Rate coverage:
- - Strong — Dedicated page directly answers the prompt
- Partial — Related content exists but doesn't fully address it
- None — No relevant content found
Step 6: Deliver Results
Output a structured report with:
- 1. Brand summary (2-3 sentences)
- Prioritized prompt list with scores and coverage status
- Content gap analysis (high-priority prompts with no coverage)
- Top 5 recommended content pieces to create first
Use the output format from references/aeo-methodology.md.
Tips for Better Results
- - If
web_search is unavailable, the skill still works — just skip the coverage audit or have the user manually check - For competitor analysis, crawl competitor sites too and compare topic coverage
- Re-run quarterly — AI prompt trends shift as models and user behavior evolve
- The agent's own knowledge of the industry is a valid research input — use it
AEO 提示词研究(免费版)
来源: github.com/psyduckler/aeo-skills
所属: AEO 技能套件 — 提示词研究 → 内容 → 分析
发现哪些提示词和主题对品牌的 AI 可见性至关重要——无需使用付费 API。
要求
- - webfetch — 抓取目标网站
- websearch — Brave 搜索免费版(可选但推荐)
- LLM 推理 — 代理自身的模型承担主要工作
无需 API 密钥、无需付费工具、无需注册账户。
工作流程
输入
用户提供:
- - 域名 URL(必填)— 例如 clearscope.io
- 细分领域/类别(可选)— 例如“面向内容团队的 SEO 软件”
- 竞争对手(可选)— 例如“Surfer SEO、MarketMuse、Frase”
步骤 1:抓取网站
使用 web_fetch 抓取关键页面以了解品牌:
需抓取的页面(逐一尝试,跳过 404):
- - /(首页)
- /about 或 /about-us
- /pricing
- /products 或 /features 或 /services
- /blog(仅索引)
或者,运行 scripts/crawl_site.sh <域名> 进行批量抓取。
从抓取内容中提取:
- - 核心产品/服务
- 目标受众(行业、公司规模、用户画像)
- 关键差异化优势/价值主张
- 竞争对手提及
- 博客标题中的内容主题
步骤 2:发现主题领域
基于品牌理解,头脑风暴主题类别。关于方法论和类别类型,请阅读 references/aeo-methodology.md。
需生成的核心提示词类别:
- 1. 问题感知型 — “如何解决[问题]?”
- 解决方案感知型 — “有哪些[类别]的工具?”
- 对比型 — “[品牌] vs [竞争对手]”
- 最佳型 — “最适合[使用场景]的[类别]”
- 操作指南型 — “如何[产品帮助完成的任务]”
- 评估型 — “[品牌]适合[需求]吗?”
- 行业型 — “[行业]趋势/最佳实践”
步骤 3:生成提示词
针对每个类别,生成 5-15 个用户实际会向 AI 助手提出的具体提示词。
指导原则:
- - 自然表达 — 像人们与 ChatGPT 对话的方式,而非谷歌搜索的方式
- 具体明确 — 包含上下文(公司规模、行业、使用场景)
- 意图多样化 — 研究、对比、操作指南、购买决策
- 避免术语过多或不切实际的提示词
步骤 4:优先级排序
对每个提示词进行评分(1-5 分):
- - 相关性 — 与品牌核心产品的关联程度?
- 潜在搜索量 — 可能有多少人提出此问题?
- 可获胜性 — 该品牌能否真正成为最佳答案?
- 意图价值 — 是否体现购买/转化意图?
公式:优先级 = (相关性 × 2 + 潜在搜索量 + 可获胜性 + 意图价值) / 5
分为三个层级:第一层级(≥3.5)、第二层级(2.5-3.4)、第三层级(<2.5)。
步骤 5:审计现有覆盖
针对第一层级提示词,使用 web_search 配合 site:domain.com [主题关键词] 检查是否已有相关内容。
覆盖评级:
- - 强覆盖 — 有专门页面直接回答该提示词
- 部分覆盖 — 存在相关内容但未完全解决
- 无覆盖 — 未找到相关内容
步骤 6:交付结果
输出结构化报告,包含:
- 1. 品牌摘要(2-3 句话)
- 按优先级排序的提示词列表,附评分和覆盖状态
- 内容缺口分析(高优先级但无覆盖的提示词)
- 建议优先创建的 5 个内容
使用 references/aeo-methodology.md 中的输出格式。
获得更好结果的技巧
- - 如果 web_search 不可用,该技能仍可运行——只需跳过覆盖审计或让用户手动检查
- 对于竞争对手分析,也抓取竞争对手网站并比较主题覆盖
- 每季度重新运行——随着模型和用户行为演变,AI 提示词趋势也在变化
- 代理自身的行业知识是有效的研究输入——请善加利用