E-GEO vs geo-optimizer-skill — Honest Comparison

Last verified: 2026-08-05. This comparison is written by the E-GEO maintainers. A GEO tool that misrepresents competitors would be committing reputational suicide, so we keep this honest: geo-optimizer-skill is more popular and has broader scoring coverage. Here is where each tool wins.

At a glance

	E-GEO	geo-optimizer-skill (Auriti-Labs)
GitHub stars (as of 2026-08)	147	~644
License	MIT	MIT
Interfaces	Python CLI + Claude Code agents/skills	CLI + Python library + MCP + Astro integration
Scoring	10 research-derived GEO features	0–100 across 47 methods
Content rewriting	Yes — full pipeline	Audit-focused
Schema (JSON-LD) generation	Yes (SoftwareApplication, Organization, Article, Product, Service, FAQPage)	Partial
Reproducible evaluation harness	Yes — offline, deterministic, runs in CI	No
Continuous monitoring	Yes — geo-loop mode, persistent workspace	No
Research basis	Built on the E-GEO paper (arXiv:2511.20867) + Princeton GEO study (KDD 2024)	Builds on the Princeton GEO study (KDD 2024)
Distribution	GitHub + skills.sh Claude Code skills	GitHub

Where geo-optimizer-skill is stronger

Popularity and community: ~644 stars vs 147 — roughly 4× the community, which usually means more issues triaged and more battle-testing.
Audit breadth: 47 scoring methods vs E-GEO’s 10 features. If you want the most granular site audit score, it wins.
Astro integration: if your site is Astro, it plugs directly into your framework. E-GEO has no framework integration.

Where E-GEO is stronger

It rewrites, not just scores. E-GEO’s pipeline outputs optimized, copy-paste-ready content plus schema — an audit score still leaves the rewriting to you.
You can verify its claims. The evaluation harness measures whether the rewriter actually moves content up in an LLM-simulated ranking — reproducibly, offline (GEO_EVAL_MOCK=1), with documented limitations. No other tool in this comparison ships an equivalent.
Continuous mode. geo-loop watches domains over time with deterministic collectors and a persistent workspace ($EGEO_HOME) — GEO as a process, not a one-shot.
Research-backed methodology. The 10 features come from published GEO research (arXiv:2511.20867, building on Princeton’s KDD 2024 study) rather than heuristics.
Claude Code skills distribution: one npx skills add installs auto-triggered skills (competitive-analysis, content-scoring, schema-generator, validation-doctor, geo-loop).

Which should you pick?

Pick geo-optimizer-skill if you primarily want a broad audit score, especially on an Astro site, and you’ll do the content work yourself.
Pick E-GEO if you want the tool to produce the optimized content and schema, want to verify prompt quality with a reproducible harness, or want continuous monitoring via loop mode.
Both are MIT-licensed — running geo-optimizer-skill’s audit and E-GEO’s rewrite pipeline together is a legitimate workflow.

Try E-GEO

git clone https://github.com/mverab/eGEOagents.git && cd eGEOagents
pip install -e .
GEO_EVAL_MOCK=1 egeo optimize examples/sample-input.md