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AB geo-site-audit

Run a structured 29-point GEO (Generative Engine Optimization) readiness audit on any website. Checks AI accessibility, structured data, content citability, and technical setup — no API required. Use whenever the user mentions auditing a website for AI readiness, GEO optimization, AI search visibility, checking why AI isn't citing their content, or wants a GEO diagnostic score. Also trigger for requests about llms.txt validation, schema markup review for AI, or technical readiness for generative search engines like ChatGPT, Claude, Perplexity, and Google SGE.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 627 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Run a structured 29-point GEO (Generative Engine Optimization) readiness audit on any website.

As a process B 73/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
94
Run on models
none yet
Process rating
B
73/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Failures and branches w 10
50
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration exfil-webhook-url references/integrations.md:60
      Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
      "url": "https://hooks.slack.com/services/...",
      placeholder

    Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 73/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 627 tokens
    • 100Running it twice. No mutating operations
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 565: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 15 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (3 of 4)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.