our-claude-skills/custom-skills/16-seo-schema-validator/SKILL.md

name, description, version, author, environment

name	description	version	author	environment
16-seo-schema-validator	Validates an AUTHORED JSON-LD schema dataset (pre-deployment QA) and audits live structured data (post-deployment). Runs a 5-layer offline validation pipeline (coverage, syntax, vocabulary, Google rich-result requirements, business-logic/consistency) and emits a severity-ranked defect log, a gate decision, and a Markdown report. Fills the "16-seo-schema-validator" slot referenced by seo-comprehensive-audit. Triggers: schema validation, JSON-LD QA, structured data check, schema 검수, 스키마 유효성 검증, 구조화 데이터 검토, rich result eligibility, schema 오류 점검.	1.0	OurDigital / D.intelligence	Code

SEO Schema Validator (16)

Quality-assure structured data at scale. Built for the kind of failure where a client review of hundreds of authored entries surfaces "too many errors" — by moving the cheap, machine-checkable errors OUT of the human review and INTO an automated gate that runs first.

Two modes

Mode	When	Input	Adds
A — Dataset QA (default)	Before deployment, while authoring/reviewing	An authored dataset: .xlsx / .csv (one row per entry, a JSON-LD column), .jsonl, .json, or a directory of .json/.jsonld	Layer 0 coverage vs the canonical URL list
B — Live audit	After deployment, or feeding seo-comprehensive-audit	Live URLs (extract embedded JSON-LD first, then validate)	Layer 5 rendering-reality (schema present in rendered HTML, matches content)

This skill's primary job is Mode A: catch errors before the client sees them.

The 5 validation layers

#	Layer	Catches	Default severity
L0	Coverage	URLs with no entry; entries whose URL isn't in the inventory	P1 / P2
L1	Syntax	invalid JSON, missing/wrong @context, no @type, encoding corruption	P0 / P1
L2	Vocabulary	unknown type, property not valid for type, bad value formats (URL/date/lang/currency/number)	P1 / P2
L3	Rich-result	Google required missing (blocks rich result); recommended absent	P0 / P2
L4	Consistency	NAP mismatch across a property, @id dupes/dangling refs, swapped geo, placeholder text, duplicate descriptions	P0 / P1

Full rationale and the type→requirement matrix: references/validation-methodology.md. Severity + category codes: references/defect-taxonomy.md. Hotel page-type → schema-type map: references/hotel-type-map.md. Client-facing report + P1 decision log: templates/client-qa-report-template.md, templates/decision-log.md.

Stage gates (aligned to the project lifecycle 설계→개발→테스트→안정화→런칭 후)

• G1 설계 — Lock the schema spec and the page-type→type map (hotel-type-map.md). Approve the entry template. DoD: every page template has an assigned schema type and a required-property list.
• G2 개발 — Authors produce entries. Run the validator with --strict. DoD (gate): zero P0, JSON parses for 100% of entries. Entries failing this NEVER reach client review.
• G3 테스트 — Re-run; triage P1 in defect_log.csv (assign owner/status). Client reviews ONLY the clean, P0-free entries, against a defect report — not raw JSON. DoD: P1 triaged, decisions logged in templates/decision-log.md.
• G4 안정화 — Fix → re-run → confirm no regressions. Spot-check a sample in Google Rich Results Test (online, outside this runtime). DoD: P0=0, P1 accepted/closed, online validator green on sample.
• G5 런칭 후 — Mode B live audit + GSC "Rich results" report monitoring. DoD: deployed schema matches authored dataset; no new GSC structured-data errors.

How to run

# Mode A — validate an authored dataset (the common case)
python scripts/validate_schema.py path/to/schema_dataset.xlsx \
    --url-list path/to/URLlist.xlsx \
    --out schema_qa_out

# Highest signal for the pre-review gate (unexpected props -> P1, drop optional recommended)
python scripts/validate_schema.py dataset.csv --strict --no-recommended --out qa_strict

# Try it on the bundled flawed fixture first
python scripts/make_sample.py
python scripts/validate_schema.py fixtures/sample_schema.csv --out demo_out

Input expectations (Mode A tabular): the loader auto-detects a JSON-LD column (jsonld, schema, structured_data, 스키마, …) plus optional url/메뉴 URL, lang/언어코드, device/PC/MOBILE, page_type columns. Multi-sheet .xlsx is supported (each sheet with a JSON-LD column is read). No JSON-LD column → clear error.

Reading the output

• report.md — counts, gate decision (PASS = zero P0), defects-by-category, top P0 entries, next step.
• defect_log.csv — one row per finding with status/owner/note columns ready for triage. This is the client-facing artifact (open issues, not raw schema).
• results.json — full machine-readable results for dashboards / CI.

The rule: an entry advances to client review only when it has zero P0. P1 = triage backlog (fix before launch). P2 = optimization backlog (recommended props, style).

Limits & honesty

• Offline by design — the runtime can't reach schema.org or Google. The bundled rule set (scripts/schema_rules.json) is a curated hotel-focused subset; unknown types/properties degrade to warnings (never hard errors) to avoid false positives.
• Authoritative rich-result eligibility still requires Google's online testers on a sample at G4. This skill makes that sample small and clean, not redundant.
• Adding a new schema type or tightening a rule = edit schema_rules.json only.

Integration

seo-comprehensive-audit calls this skill as pipeline stage 4 ("Schema Validation"). For that orchestrator, run Mode B on a sample of live URLs and return the score (100 − weighted defect penalty) and the issue list. For day-to-day client work, run Mode A on the authored dataset.