Claude Skill
gbessoni/seobuild-onpage
SEOBuild Onpage is the first AI agent that writes pages ranking on Google and cited by LLMs. Built on DeerFlow with 2026 SEO+GEO strategies, forensic analysis, and verification tags. BYOK GSC & Dat...
Overview
Repository
Install this Skill
git clone https://github.com/gbessoni/seobuild-onpage.git ~/.claude/skills/seo-agiRegistry
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
Summary
SEOBuild Onpage is the first AI agent that writes pages ranking on Google and cited by LLMs. Built on DeerFlow, it uses tested 2026 SEO+GEO strategies, forensic competitive analysis, 500-token chunk architecture, entity consensus, and verification tags. Supports BYOK GSC and DataforSEO, compatible with OpenClaw, Claude Code, and Codex.
SEOBuild Onpage - 首个能写出谷歌排名页面且被大语言模型引用的AI代理。一条指令输入，排名页面输出。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、Codex。
Key features
- First AI agent for Google-ranked & LLM-cited pages
- Forensic competitive analysis with entity consensus
- 500-token chunk architecture for precision
- Verification tags for content trustworthiness
- BYOK integration with GSC and DataforSEO
- Compatible with OpenClaw, Claude Code, Codex
Use cases
- Automated SEO content creation for ranking pages
- LLM-optimized writing for AI citation
- Competitive content gap analysis
- Enterprise SEO workflow automation
- Multi-tool SEO pipeline with DeerFlow
README excerpt
# 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