inhouseseo/superseo-skills

Description and use

**Brief Summary (2‑3 sentences)**
inhouseseo/superseo‑skills is an open‑source collection of 11 Claude‑compatible “skills” that automate core SEO tasks—page audits, link‑building, article generation, E‑E‑A‑T assessments, semantic‑gap analysis, and more—using a standardized Model Context Protocol (MCP). Built on the methodologies of Koray Tuğberk, Kyle Roof and Lily Ray and hardened in production at InhouseSEO, the repo ships with an anti‑AI‑slop ruleset to keep outputs high‑quality and human‑like.

**Value Proposition**

• **Bridges AI and real SEO tools**: By exposing SEO functions through a common MCP interface, the project lets any Claude‑based (or compatible) agent invoke concrete actions—fetching live data, posting to link‑building platforms, or running audits—without custom glue code.
• **Accelerates AI‑first workflows**: Teams can plug the skill set into existing chat‑ops or autonomous agents, instantly gaining 11 ready‑made SEO capabilities and a vetted anti‑slop filter, which reduces the time spent building and fine‑tuning prompts.
• **Standardizes integrations**: The MCP server model encourages a plug‑and‑play ecosystem; other tools can adopt the same protocol, making future extensions and cross‑team collaboration straightforward.

**Practical Adoption Path**

1. **Proof‑of‑Concept (PoC)** – Clone the repo, run the provided Docker compose or local server, and use the sample README scripts to call a single skill (e.g., page-audit). Verify that the output meets your quality standards.
2. **Integrate with Your Agent** – Extend your Claude‑or‑compatible agent to issue MCP requests to the local server. Map the agent’s intents to the skill IDs (audit_page, write_article, etc.).
3. **Iterate & Extend** – Add custom adapters for any internal SEO platforms not covered out‑of‑the‑box, leveraging the same MCP schema.
4. **Production Deployment** – Containerize the MCP server, place it behind your internal API gateway, and enable authentication/rate‑limiting. Monitor logs and the built‑in anti‑slop metrics to ensure compliance.

**Production Readiness**

• **Activity & Adoption**: 166 ⭐, 25 🍴, recent commits (last updated 2026‑05‑12), and 16 topical tags indicate an actively maintained project with a modest but engaged community.
• **Stability**: The codebase has been “production‑tested at InhouseSEO,” suggesting real‑world reliability for core SEO workflows.
• **Readiness Level**: High for an OSS candidate; the repository includes clear README instructions, Docker support, and a well‑defined MCP contract, making it suitable for a serious pilot.
• **Remaining Checks**: Before a full rollout, perform a final review of the license (ensure compatibility with your stack), run a security audit of dependencies, and confirm that maintainers are responsive to issues.

Overall, superseo‑skills offers a mature, standards‑based way to empower AI agents with SEO expertise, and it can be safely piloted with a small PoC before scaling to production.

Use in a workflow

inhouseseo/superseo-skills helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.