Publisher: Corey Haines · Editorial profile by AI Tools Ratings · Review method
Not runtime tested; source-document review only. Referenced scripts were not executed or comprehensively audited. No security certification.
What task is this skill designed for?
Audit JSON-LD for an AI-tool profile using only facts supported by its visible content.
What input and output should you expect?
Input: Rendered page, existing JSON-LD, canonical URL, publisher evidence and the intended data consumer.
Output: A markup assessment or proposed JSON-LD with validation steps; expected, not independently tested. Valid markup does not establish ranking improvement.
What does it need to run?
An Agent Skills-capable client, page access and current official documentation for the target consumer. Inspect the source example reference before adapting a schema shape.
Publisher README declares support for Agent Skills clients including Claude Code, Codex and Cursor. This is author-declared, not independently runtime tested.
How should you approach installation?
- Inspect the pinned source, license and task-specific reference files before installing anything.
- Follow the publisher README guidance for your actual client; no installation command or cross-client compatibility was independently tested here.
- Try the illustrative prompt with synthetic input in a separate folder, inspect the output against the checklist and approve external actions separately.
Installation guide: discovery, dependencies and runtime checks
What files, accounts or data can it access?
Reads page content and code and can draft markup changes. External validators receive the tested URL or markup; use synthetic examples before submitting private staging content.
What does the skill cost?
The inspected source license is MIT. Agent subscriptions, model usage, dependencies and external services have separate costs and terms. No paid runtime was tested.
What are its limits and alternatives?
Unsupported ratings, prices and reviews create factual mismatches. Duplicate plugin graphs may conflict, and search-feature availability is a separate check from Schema.org syntax.
Use SEO Audit for wider crawling and content problems. Choose this workflow for a defined structured-data issue with current eligibility evidence.
What could you ask in a small trial?
Try this with a small sample:
Audit this synthetic tool page and JSON-LD. Remove unsupported ratings and stale prices, check canonical identity and separate vocabulary validation from search-feature eligibility.
How would you verify the result?
- Match marked-up entities and values to visible evidence.
- Inspect duplicate graphs and canonical identifiers.
- Check current official feature guidance before promising search appearances.
Use a baseline and record acceptance evidence before treating a successful task as a reliable workflow.
Which source and license were checked?
Read the reviewed SKILL.md at its pinned commit · Complete reviewed folder
Repository and current publisher guidance
Checked:
MIT — identified in LICENSE at the reviewed commit. Dependencies and third-party assets may have separate terms. License evidence
Version details and review scope
Source: coreyhaines31/marketingskills/skills/schema
Reviewed commit: e85f3cbc2e4a79bf33b2a12e9bdd9087d414735b
SKILL.md SHA-256: d298f5d6e1b33b6825f38ecaa05c4d33ba9e45e346b224bd86f9834e46f67a94
Source-document review. Source text and stated dependencies were inspected for this profile. Referenced code was not comprehensively audited, no runtime test was performed, and source files are not mirrored here.