AB geo-schema-gen
Generate complete, validated Schema.org JSON-LD markup for any content type to boost AI citation rates. Creates structured data for Organization, FAQPage, Article, BlogPosting, Product, HowTo, BreadcrumbList, WebSite, VideoObject, and ImageObject schemas. Use whenever the user mentions adding schema markup, generating structured data, creating JSON-LD, implementing Schema.org, optimizing for rich snippets, or wants to improve how AI understands and cites their content. Also trigger for requests about Organization schema, FAQ schema, Article markup, Product schema, or any structured data implementation.
As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, running it twice
How to improve
- 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: 9. 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 65/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 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. 19 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1256 tokens
- low 10 top-level sections: this looks like several domains in one skill
- medium 6 test cases, all positive: not one "should refuse" or "should ask first"
- low No test case covers injection arriving through data
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)
- -216 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 609: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 19 items
- +3Output format is stated explicitly
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.