OpenClaw SEO Audit Skill Released for Technical Website Analysis

A new OpenClaw skill has been uploaded that performs comprehensive SEO audits. After installation, users can ask OpenClaw 'seo audit [url]' to get a detailed analysis of any website.
What the Skill Checks
The audit covers seven main categories with specific checks in each:
Technical SEO (25%)
- robots.txt validity
- XML sitemap existence & structure
- HTTPS/SSL implementation
- Hreflang tags for multi-language sites
- Redirect chains
- Canonical tags
- Security headers
Content Quality (25%)
- E-E-A-T signals (Experience, Expertise, Authority, Trust)
- Author bylines & credentials
- Social proof (testimonials, logos)
- Thin content detection
- Readability analysis
On-Page SEO (20%)
- Title tags
- Meta descriptions
- H1-H6 heading structure
- Internal linking
- URL structure
- CTAs (Calls to Action)
Schema/Structured Data (10%)
- JSON-LD detection
- Organization, Product, FAQ, Review schemas
- Validation against Google's supported types
- Deprecation warnings (HowTo, FAQ restrictions)
Performance/Core Web Vitals (10%)
- LCP (Largest Contentful Paint) ≤2.5s target
- INP (Interaction to Next Paint) ≤200ms target
- CLS (Cumulative Layout Shift) ≤0.1 target
- TTFB (Time to First Byte)
- Page weight
Images (5%)
- Alt text presence
- Image sizing/compression
- Modern formats (WebP/AVIF)
AI Search Readiness / GEO (5%)
- AI crawler access (GPTBot, ClaudeBot, etc.)
- llms.txt file presence
- Citability for AI Overviews
- Content structure for AI extraction
Output Format
The skill outputs a health score from 0-100 along with a prioritized action plan categorized as Critical, High, Medium, or Low priority items.
The skill is available at: https://www.machina.directory/skills/seo-audit
📖 Read the full source: r/openclaw
👀 See Also

MCP Server: Comparing Local and Cloud LLMs with Debate Feature
The MCP server enables developers to query local models via Ollama alongside various cloud LLMs, offering features like side-by-side comparison and a structured debate function.

Six GitHub Repositories for Claude Code Development
A Reddit user tested and shared six GitHub repositories designed to improve Claude Code projects, including tools for structured development, UI generation, task management, memory, ecosystem exploration, and workflow automation.

DebugBase: A Collective Error Knowledge Base for AI Coding Agents via MCP
DebugBase is an MCP-compatible tool that provides a shared knowledge base where AI coding agents can check for known fixes to common errors like Next.js hydration mismatches or TypeScript resolution issues. It includes 11 MCP tools and comes pre-seeded with 58 error/fix pairs from real agent sessions.

Orion: Bypassing CoreML to Run and Train LLMs Directly on Apple Neural Engine
Orion is an open-source Objective-C system that bypasses Apple's CoreML to run and train LLMs directly on the Apple Neural Engine (ANE), achieving 170+ tokens/s for GPT-2 124M decode and stable multi-step training on a 110M parameter transformer.