Key takeaways
- Most architecturally ambitious tool in the category — 16 sub-skills, 10 specialist agents, and 88 Python scripts as evidence collectors as of June 2026 (up from 33 at launch)
- 643 stars, MIT license, Python. Created March 2, 2026; v3.0.1 shipped May 14, 2026. Platform support expanded from 3 to 9 agents including Cursor, Windsurf, GitHub Copilot, and Cline
- Reasoning-first approach with confidence labels (Confirmed, Likely, Hypothesis) on each finding — unique transparency feature
- GitHub-specific SEO optimization is a standout niche feature not found in any competitor
FAQ
What is Agentic-SEO-Skill?
An LLM-first SEO tool with 16 sub-skills and 10 specialist agents that uses 88 Python scripts as evidence collectors. It takes a reasoning-first approach, labeling each finding with confidence levels.
What does reasoning-first mean?
Each SEO finding is tagged with a confidence label — Confirmed (evidence-backed), Likely (strong signals), or Hypothesis (educated guess). This transparency lets users prioritize which recommendations to trust and act on.
What agents does Agentic-SEO-Skill support?
Nine platforms as of June 2026 — Claude Code, Codex, Antigravity IDE, Claude Cowork, Cursor, Windsurf, Continue.dev, GitHub Copilot, and Cline — each with a native installation format. Broader than most Claude Code-only tools but narrower than seo-geo-claude-skills which supports 35+ agents.
What is the GitHub SEO feature?
A sub-skill dedicated to optimizing GitHub repository visibility — README structure, topic tags, description optimization, and discoverability in GitHub search. No other tool in the category addresses this niche.
Overview
Agentic-SEO-Skill is the most architecturally ambitious tool in the SEO/GEO agent skills category — 16 sub-skills, 10 specialist agents, and 88 Python scripts that act as evidence collectors for SEO analysis. Its defining feature is a reasoning-first approach where every finding is tagged with a confidence label, giving users transparency into how much to trust each recommendation. The project is openly built on claude-seo by AgriciDaniel, restructured into a portable skill package for multiple agent platforms.
Key stats: 643 stars, 99 forks, MIT license, Python. Created March 2, 2026; v3.0.1 released May 14, 2026; last commit May 26, 2026. Free and open source — no paid tier.
| Attribute | Value |
|---|---|
| Stars | 643 (as of June 2026) |
| License | MIT |
| Language | Python |
| Created | March 2, 2026 |
| Latest Release | v3.0.1 (May 14, 2026) |
| Sub-skills | 16 |
| Specialist Agents | 10 |
| Evidence Scripts | 88 Python + 1 shell helper |
| Agent Support | Claude Code, Codex, Antigravity IDE, Claude Cowork, Cursor, Windsurf, Continue.dev, GitHub Copilot, Cline |
| Category | SEO/GEO Agent Skills |
How It Works
The architecture is three-layered: sub-skills define what to analyze, specialist agents coordinate the analysis, and Python scripts collect the evidence.
Evidence Collection
The 88 Python scripts (up from 33 at launch) are the foundation — they crawl, parse, measure, and extract raw data from target sites. Think of them as the "eyes and ears" of the system: checking response codes, parsing HTML structure, measuring load times, extracting metadata, and building the evidence base that agents reason over. The scripts are optional — the skill can run LLM-only — and a standardized llm-audit-rubric.md enforces a consistent finding format across audits.
Specialist Agents
Ten specialist agents each own a domain of SEO analysis — Technical SEO, Content Quality, Performance, Schema Markup, Sitemap, Visual Analysis, three GitHub-focused agents (Analyst, Benchmark, Data), and a Verifier. Rather than a single monolithic audit, each agent works independently on its specialty. This parallel architecture produces comprehensive audits faster than sequential tools.
Reasoning-First Output
The standout feature: every finding includes a confidence label.
- Confirmed — backed by measurable evidence from the Python scripts
- Likely — strong signals from multiple indicators
- Hypothesis — educated guess based on patterns, needs manual verification
This transparency is unique in the category. Most SEO tools present all findings with equal confidence, leaving users to guess which recommendations are reliable. Agentic-SEO-Skill makes the uncertainty explicit.
16 Sub-Skills
The skill library spans traditional SEO analysis with some unique additions:
| Domain | Coverage |
|---|---|
| Technical SEO | Core Web Vitals, crawlability, site structure, mobile optimization |
| Content Analysis | Quality assessment, keyword optimization, readability |
| E-E-A-T | Experience, Expertise, Authoritativeness, Trustworthiness evaluation |
| Link Analysis | Internal linking, external link quality, anchor text distribution |
| Schema Markup | Structured data detection, validation, and generation |
| GEO | AI search visibility and citation optimization |
| Competitor Analysis | Side-by-side comparison with competing domains |
| GitHub SEO | Repository discoverability optimization |
GitHub SEO
The GitHub-specific SEO sub-skill is a niche feature unique to this tool. It optimizes repository visibility through README structure, topic tags, description formatting, and GitHub search discoverability, generating GITHUB-SEO-REPORT.md and GITHUB-ACTION-PLAN.md outputs. Given that many of these tools are themselves GitHub repositories, the meta-relevance is notable.
Competitive Position
Agentic-SEO-Skill sits in the mid-tier of the SEO/GEO agent skills category at 643 stars as of June 2026 — more than triple its March count, but still well behind category leaders claude-seo (~8.7K) and geo-seo-claude (~8.1K), and behind seo-geo-claude-skills (~2.1K). Its differentiators are architectural ambition (more moving parts than anything else in the category) and the confidence labeling system.
Platform support expanded sharply between March and June 2026: from three agents (Antigravity IDE, Claude Code, Codex) to nine, adding Claude Cowork, Cursor, Windsurf, Continue.dev, GitHub Copilot, and Cline, each with a native installation format. That gives it far broader reach than most Claude Code-only tools, though it still trails seo-geo-claude-skills' 35+ agent support.
The "reasoning-first" positioning is philosophically distinct. Where claude-seo leads with breadth and geo-seo-claude leads with GEO depth, Agentic-SEO-Skill leads with analytical rigor and transparency.
Strengths
- Confidence labels — unique transparency on finding reliability
- 88 evidence scripts — deep (and growing) data collection layer
- 10 specialist agents — comprehensive parallel analysis, now including a dedicated Verifier agent
- Nine-platform support — Claude Code, Codex, Antigravity IDE, Claude Cowork, Cursor, Windsurf, Continue.dev, GitHub Copilot, Cline
- HTML report dashboards — shareable audit output via
generate_report.py, added since launch - Active maintenance — v3.0.1 in May 2026, project wiki, commits through May 26, 2026
- GitHub SEO — unique niche capability
Weaknesses
- Complexity — 16 sub-skills + 10 agents + 88 scripts is a lot of moving parts
- Moderate adoption — 643 stars as of June 2026; growing fast but still an order of magnitude behind the category leaders
- No MCP integration — can't connect to external search data
- No PDF reports — HTML dashboards now exist, but output still lacks the client-ready polish of geo-seo-claude's PDF reports
- Derivative core — heavily built from claude-seo; original value is in the restructuring, evidence scripts, and multi-platform packaging
What Developers Say
No attributable developer testimonials surfaced in searches of Hacker News, Reddit, or the broader web as of June 2026. The 643 stars and 99 forks indicate real uptake, but public commentary on the tool has not yet materialized — a gap worth rechecking as adoption grows.
Bottom Line
Recommended for teams that value analytical rigor and want to understand the reasoning behind each recommendation. Agentic-SEO-Skill remains the most technically ambitious SEO tool in the category, and the confidence labeling system alone justifies evaluation — knowing which findings are evidence-backed vs. hypothetical is genuinely valuable for prioritizing SEO work. The 88-script evidence collection layer and 10 specialist agents provide thorough analysis, though the complexity may be overkill for simpler audits.
Outlook: the trajectory since launch is the strongest signal — stars more than tripled (188 to 643), scripts grew from 33 to 88, platform support tripled to nine agents, and v3.0.1 shipped with HTML report dashboards, all within three months. The early-stage concern from March 2026 has eased; the remaining question is whether it can close the adoption gap with the 8K-star category leaders.