AC trirank-geo-audit
Audit any website for AI search visibility (GEO / AEO). Use when the user asks to "audit my site for AI search", "check if AI can cite my site", "GEO audit", "AEO audit", "AI SEO check", "can ChatGPT / Perplexity / Claude see my site", "llms.txt check", or "why doesn't AI recommend my brand". Fetches the site's homepage, robots.txt, sitemap.xml and llms.txt, runs a 14-point weighted checklist (indexability, metadata, structured data, AI-crawler readiness), and outputs a scored gap report with a concrete fix for every failed check.
Audit any website for AI search visibility (GEO / AEO).
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. 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 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 24 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2187 tokens
- low The response is described with custom markup (10 tags): a typed call is more reliable
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +3Description length 536: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 24 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.