local-landing-page-auditor

该 Skill 抓取多地点落地页与门店定位页，校验 LocalBusiness 结构化数据、地图嵌入、NAP 与 GBP 一致性以及跨页内容重复度，按页面价值排序，输出含缺失字段、相似度、评分及 pass/needs_work/fail 分类的 JSON；无需 API 密钥，通过 Python 脚本执行。

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最近更新 2026-10-18 21:42:15

分类：营销依赖：代码执行 / 第三方 API / 读写文件应用场景：市场分析 / 报告撰写 / 研究 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add seoskills.sh/local-landing-page-auditor

Metadata

name: local-landing-page-auditor
description: Crawls every location and store-locator page and validates LocalBusiness schema, embedded map, NAP parity with GBP, and content uniqueness across locations, flagging thin or duplicated pages, missing schema fields, and crawl issues. Use when the user has many location pages and wants a programmatic local landing-page audit at scale.
metadata: {"title":"Multi-Location Landing Page Auditor","category":"local-seo"}

Multi-Location Landing Page Auditor

AGENT ROLE: Autonomous local-page-audit agent. Evaluate each location page against the local on-page checklist, measure cross-page duplication, and emit the JSON in references/output.schema.json.

OBJECTIVE

Score every location landing page for local SEO completeness — valid LocalBusiness schema, embedded map, unique content, NAP presence — and flag the pages that are thin, duplicated, or missing critical elements, prioritized by location value.

INPUTS

location_pages (REQUIRED string[]) OR store_locator_url (crawl to discover them).
gbp_nap (OPTIONAL): map of page_url → { name, address, phone } (canonical GBP NAP) for parity checks.
page_value (OPTIONAL): map of page_url → weight (e.g., revenue/traffic) for prioritization.
min_unique_words (OPTIONAL, default 150): unique-content floor.

AUTHENTICATION / RUNTIME

No API key. Keyless HTTPS GET, UA seoskills-local-audit/1.0, honor robots.

EXPECTED TOOL CALLS

Run scripts/local_page_audit.py --pages pages.json [--gbp gbp.json].
Per page: GET; parse JSON-LD/microdata, detect map embeds, extract main text; then compute cross-page shingled duplication.

PROCEDURE (deterministic, per page)

STEP 1 — FETCH + PARSE. STEP 2 — SCHEMA: require a LocalBusiness (or subtype) JSON-LD with name, address (full PostalAddress), telephone, geo or hasMap, openingHours. Record each missing required field. STEP 3 — MAP: detect an embedded map (google.com/maps/embed, <iframe ... maps>, or geo+static-map). has_map = bool. STEP 4 — NAP PARITY (if gbp_nap): compare on-page NAP (schema-first) to GBP canonical → nap_parity: match|mismatch|missing. STEP 5 — UNIQUENESS: compute 5-gram shingle sets per page; max_similarity = highest Jaccard vs any OTHER location page. duplicate IF max_similarity >= 0.8; thin IF unique word count < min_unique_words. STEP 6 — SCORE page_score (0–100) from schema completeness, map, NAP parity, uniqueness; classify pass | needs_work | fail. Prioritize failures by page_value. EMIT.

RATE LIMITS & ERROR HANDLING

Crawl politeness: ≤ 5 concurrent, ≥ 150ms per host. Timeout 12s. HARD cap at 5000 discovered pages (hit_cap=true).
Per-page fetch failure (non-200, robots) → status="unreachable", continue.
IF store_locator_url yields 0 links THEN STOP error.code="NO_PAGES_FOUND".

MISSING / INSUFFICIENT DATA

IF gbp_nap absent THEN skip parity and set nap_parity="not_checked" (never a false mismatch).
A page with no extractable main text → thin=true with text_extractable=false rather than a uniqueness score.
Duplication is symmetric — report the specific most-similar sibling URL as evidence, not just a number.

OUTPUT

One JSON object per references/output.schema.json. No prose.

FILES

scripts/local_page_audit.py — crawl/parse, schema + map + NAP checks, shingled duplication, scoring.
references/output.schema.json — output contract.