Claude Skill
gbessoni/seobuild-onpage
SEOBuild Onpage 是首个能写出谷歌排名且被大语言模型引用的AI代理。基于DeerFlow，采用2026年SEO+GEO策略、取证分析和验证标签。支持自带密钥的GSC和DataforSEO。
概览
仓库信息
安装这个 Skill
git clone https://github.com/gbessoni/seobuild-onpage.git ~/.claude/skills/seo-agiRegistry 信息
git clone https://github.com/gbessoni/seobuild-onpage.git ~/.claude/skills/seo-agigit clone https://github.com/gbessoni/seobuild-onpage.git ~/.codex/skills/seo-agipip install requests
项目简介
SEOBuild Onpage 是首个能写出谷歌排名页面且被大语言模型引用的AI代理。基于DeerFlow构建，采用经过验证的2026年SEO+GEO策略，具备取证式竞争分析、500令牌分块架构、实体共识机制与验证标签。支持自带密钥的Google Search Console和DataforSEO，兼容OpenClaw、Claude Code和Codex。
SEOBuild Onpage - The first AI agent that writes pages Google ranks AND LLMs cite. One command in, ranking page out. Built on DeerFlow, powered by 2026 SEO + GEO strategies tested / working. Forensic competitive analysis, 500-token chunk architecture, entity consensus, verification tags. BYOK GSC, DataforSEO. Works w/ OpenClaw, Claude Code, Codex
要点
- 首个能生成谷歌排名且被LLM引用的AI代理
- 取证式竞争分析与实体共识机制
- 500令牌分块架构确保精准度
- 验证标签提升内容可信度
- 支持自带密钥的GSC和DataforSEO集成
- 兼容OpenClaw、Claude Code和Codex
使用场景
- 自动化创建排名页面的SEO内容
- 为AI引用优化写作内容
- 竞争内容差距分析
- 企业级SEO工作流自动化
- 基于DeerFlow的多工具SEO流水线
README 摘要
# seobuild-onpage v1.9.1 ### One command. Competitive data in. Ranking pages out. ``` claude install-skill gbessoni/seobuild-onpage ``` Most SEO tools tell you what's wrong with your site. This one writes the pages. `/seoagi "airport parking JFK"` pulls the current SERP, analyzes what's ranking, finds the gaps in their content, and writes you a complete page -- with the heading structure, depth, FAQ section, and schema markup that actually competes. Not thin content. Not keyword-stuffed filler. Pages backed by live data from the tools the pros use. **New in v1.9.1 -- Decision Fit Mapping + Brand Voice + Missing Spoke Detection:** - **Brand differentiator injection** via `--differentiators` on `research.py` (e.g. `--differentiators="women-owned, 24/7 service, no hidden fees"`). Passes through to the brief output so the writing agent has strict brand constraints. Differentiators must be woven verbatim into the 500-token chunks and surfaced in the AI Summary Nugget -- paraphrased fluff fails the new Brand Identity check. - **Missing Spoke Detection** -- the research pipeline now extracts internal-link anchor text from the top 3 competitors, filters out navigational generics (Home, Contact Us, Privacy, FAQ, etc.) and image-link leakage, and outputs a ranked `missing_spokes` list. SKILL.md Section 12 now requires every generated page to append a `## Recommended Spoke Pages` section built from this data. - **Decision Fit Mapping** -- new checklist enforcement: heading structure must map to the user's psychological buying stage (Research / Compare / Buy) instead of copy-pasting competitor H2s. - **Execution Protocol now prompts for differentiators** if the user didn't supply them up front -- the agent stops and asks before writing rather than producing generic AI homogeni