What does this skill do, and when should you use it?
SEOBuild Onpage is an agent skill (SKILL.md plus Python scripts) that automates the author's 20+ years of manual SEO workflow from the ground-transportation industry: pull live SERPs, parse competitor pages, extract People Also Ask and related keywords, then write and score a full page. Its core framework is a Two-Gate AEO model (enter the retrieval pool, then get selected for citation), a 500-token chunk architecture, and an Anti-NLP entity-stuffing protocol. Data access is BYOK: DataForSEO is the required core, with GSC and Ahrefs/SEMRush MCP optional, degrading to web search without keys. Best suited to content and marketing teams who accept its assertive SEO theories and will configure API keys.
Runs scripts/research.py to pull DataForSEO's top-10 SERP, keyword volumes, PAA questions, and competitor content structure; optionally reads GSC data for cannibalization detection and ghost-path discovery; generates Markdown/HTML pages under rules including 500-token chunks, entity-fact pairing, and strict structural entity placement, complete with JSON-LD schema, an AI Summary Nugget, an original research block, and a 'Not For You' block; for rewrites it recommends 301 or 410 for every legacy URL; finally it scores the output against a 58-point quality checklist and prints a scorecard, flagging anything below 49/58.
- A local service business generating dozens of single-service-per-city location pages, each with LocalBusiness schema and a GBP inner-page link directive.
- An affiliate site owner needing compliant monetized pages: disclosed rel="sponsored nofollow" CTAs plus an FTC disclosure, no redirect cloaking.
- A content team refreshing underperforming legacy URLs: combining their own GSC data against the current top 3, deciding item by item whether to expand, rewrite, or 410-prune.
- Competitive intelligence research: brief only, no page — word-count ranges, heading structures, topic gaps, keyword volumes.
- Optimizers targeting AI Overviews who need outbound citations, anti-paragraph-snippet rules, and DOM-flattened extraction-friendly structure.
How do you install this skill?
- Effectiveness claims (e.g., all pages ranking page 1) are author self-reports, independently unverified; this is a static review with no code or tests executed.
- Core function depends on overseas paid APIs (DataForSEO, Massive) with BYOK; mainland-China reachability and cost are real frictions, and no Chinese-language support is declared.
- License metadata is unknown; confirm licensing terms before use. Publisher identity is unverified.
- Credentials are stored locally in ~/.config/seo-agi/.env; users must secure that file and the GSC service-account JSON themselves.
- Shell / CLI
- Network access
- Local filesystem
- MCP Server
Python 3requestsDataForSEO account (BYOK, core)Google Search Console service account (optional)Ahrefs/SEMRush MCP servers (optional)
Three options: 1) In Claude Code run claude install-skill gbessoni/seobuild-onpage, or download the release zip and upload via Settings > Skills; 2) OpenClaw/Codex users: git clone https://github.com/gbessoni/seobuild-onpage.git ~/.claude/skills/seo-agi (Codex: ~/.codex/skills/seo-agi); 3) manually clone into your agent's skill directory. Then pip install requests. Optional: mkdir -p ~/.config/seo-agi, copy .env.example, and fill in DATAFORSEO_LOGIN/PASSWORD and GSC_SERVICE_ACCOUNT_PATH.
git clone --depth 1 https://github.com/gbessoni/seobuild-onpage.git ~/.claude/skills/seobuild-onpageGenerated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- airport parking JFK
- Write an SEO page for \
- . The skill runs the research script, shows a brief for confirmation, writes the full page, and prints the 58-point scorecard. Use `research.py
Verify first without keys: python3 ~/.claude/skills/seo-agi/scripts/research.py "airport parking JFK" --mock --output=compact. In production, type a trigger phrase in Claude Code, OpenClaw, or Codex — e.g. "Write an SEO page for \"airport parking JFK\"", "rank for [keyword]", "GEO", or "seo-agi". The skill runs the research script, shows a brief for confirmation, writes the full page, and prints the 58-point scorecard. Use research.py "keyword" --output=brief for a brief-only handoff to a human writer.
What are this skill's strengths and limitations?
- Compresses the entire SERP-research-to-published-page pipeline into one command, with a mock mode for zero-key trial runs.
- The framework is concrete to the HTML level: 500-token chunks, entity-fact pairing, {{VERIFY}} tags — nothing left vague.
- A 58-point checklist is scored item by item and the scorecard is mandatory output, making delivery auditable.
- It draws explicit compliance lines (no cloaking, no NLP stuffing), reducing de-indexation risk.
- The author has verifiable industry background (operating ParkingAccess.com and Shuttlefare.com).
- Core DataForSEO is a paid API (~$0.002/query); without keys the skill degrades to web search with a clear precision loss.
- The repo metadata lists License as unknown while the README claims MIT — an inconsistency to verify before adopting.
- Many claims (e.g. 25% de-indexation, 'Gemini 3.5 Flash RAG shard extraction') are practitioner lore without public, independently verifiable experiments.
- The skill is clearly tuned for local-service/transport-style keywords; other verticals are not equally validated.
- Output strictly requires SSR/SSG — CSR SPA projects need prerendering work first.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| SEOBuild Onpage (SEO-AGI) this page | 50 · Use with care | ★ 257 | 9d ago | — |
| SERP Markup Builder | 58 · Recommended | ★ 3k | 11d ago | Apache-2.0 |
| Blog Keyword Conflict Detector | 43 · Not recommended | ★ 2.4k | 11d ago | MIT |
| Off-Site & AI Referral Analyzer | 59 · Recommended | ★ 3k | 11d ago | Apache-2.0 |
| GEO Client Report Generator (geo-report) | 48 · Use with care | ★ 11k | 20d ago | MIT |
It takes an adversarial stance toward NLP-coverage tools like Surfer SEO and Clearscope: the skill explicitly forbids copying their entity lists into body copy, citing de-indexation-filter risk. It also positions itself beyond audit tools — the README's line is that most SEO tools tell you what's wrong with your site, while this one writes the pages.
How did FollowSkills review this skill?
Files show keys stored locally in ~/.config, no malware, covert exfiltration or destructive defaults; the changelog explicitly rejects cloaking/JS-redirect schemes and includes junk-path filtering; data flow is largely transparent. Deductions: no explicit user-confirmation step, no rollback documentation, no formal disclosure document for external services (DataForSEO, Massive, GSC); least privilege and attribution incomplete.
CHANGELOG and CLAUDE.md reference multi-version regression tests (test_dataforseo.py shows genuine parsing tests) and a --mock mode requiring no keys; key paths are self-consistent. Deductions: static review only, nothing executed; error handling and failure-feedback quality only partially evidenced; capped below 10 by static calibration.
Triggers, target audience (English SEO/GEO content producers), inputs/outputs and process boundaries are clearly stated, including non-fit ranges (CSR SPAs, multi-service local pages). Deductions: depends entirely on overseas paid APIs (DataForSEO, Massive); no Chinese-language support declared; reachability and cost create real friction for mainland-China users.
Good doc layering (SKILL.md/README/SPEC/CLAUDE.md/CHANGELOG), versioning, detailed known-limitation disclosure (e.g., Crawl Stats not in the API), visible maintenance path. Deductions: license metadata unknown; publisher unverified, so license/governance chain is incomplete.
The workflow (research→brief→page→validation) with a 66-point checklist and printed scorecard is structurally complete and the core task path is plausible. Deductions: 'every page ranking page 1' claims are author self-reports; no third-party execution evidence; direct usability of output unverifiable statically, capped at 7.
CHANGELOG documents spec-deviation corrections and regression-test motivations; fact/inference separation is fairly rigorous (not_assessed states, {{VERIFY}} tags). Deductions: key figures (e.g., the ~25% de-indexation claim) are unauditable practitioner claims with no corroborating sources, capped at 5.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →