Seo Local

> Example output: [examples/seo-local-sweetgreen-com-20260514/LOCAL-SEO-REPORT.md](../../examples/seo-local-sweetgreen-com-20260514/LOCAL-SEO-REPORT.md)

Local SEO

Score a local business's website against the signals that drive local-pack and "near me" visibility — GBP integration on the page, NAP consistency, on-page local intent, citation footprint on Tier-1 directories, review-platform presence, and local-pack rank for the business's primary keywords. Deliverable is one prioritised fix list, anchored in observable signals.

> Adapted from AgriciDaniel/claude-seo's seo-local skill (MIT). Concept and dimension structure mirror the upstream; backend rewired to SE Ranking + Firecrawl + Google APIs. DataForSEO Maps geo-grid and Business Listings checks from the upstream are dropped (no equivalent backend) — see "Limitations" in the deliverable.

Prerequisites

• SE Ranking MCP server connected (used for local-pack rank, on-page audit data, domain context).
• Claude's WebFetch tool available (used for sense-check fallback when Firecrawl is unavailable).
• User provides: (a) a target domain or homepage URL, (b) at least one primary local keyword (e.g. "dentist Brooklyn", "plumber near me"), (c) target country and ideally city/region for local-pack scoping. Optional: GBP listing URL, Yelp/Trustpilot URLs for review scraping.

Process

1. Validate target & preflight. See skills/seo-firecrawl/references/preflight.md for the canonical 3-stage preflight (credit balance, Firecrawl availability, Google APIs). Skill-specific notes:

• Normalise the target (strip protocol from domain; confirm homepage is fetchable). Confirm at least one local keyword was provided — if none, infer from ` + ` of the homepage; if still ambiguous, ask the user before continuing.
• Estimated SE Ranking cost for this skill: ~15–25 credits (1 audit re-check + 3–5 SERP queries + 1 domain overview).
• Firecrawl: optional with WebFetch fallback, ~6–9 Firecrawl credits if available (hard cap 12). When available, steps 4 (GBP-on-page audit), 5 (NAP extraction), and 7 (review scraping) run on the homepage + 5 sample pages + provided review URLs. Without Firecrawl those steps degrade to WebFetch-only — schema/JSON-LD detection and tel: / address element extraction become best-effort prose inspection. Pass --no-firecrawl to force WebFetch-only.
• Google APIs: tier 1 (GSC) unlocks step 8b (GSC local query performance) after the local-pack rank step; tier 2 (GA4) additionally unlocks step 8c (GA4 organic-by-landing-page enrichment). See skills/seo-google/references/cross-skill-integration.md for the full enrichment contract.

1. Business-type detection

• Read homepage + /contact + footer prose (WebFetch markdown is enough for this).
• Classify as one of:
• Brick-and-Mortar — visible street address, "Visit us at", embedded Maps iframe.
• Service Area Business (SAB) — no street address, "serving {region}", "we come to you", areaServed in schema without address.streetAddress.
• Hybrid — both signals present (e.g. showroom + service area).
• This determines which checks apply downstream. SAB skips embedded-map and physical-address consistency. Record in LOCAL-SEO-REPORT.md "Snapshot".

1. Industry-vertical detection

• From URL patterns (/menu, /practice-areas, /listings, /inventory), ``, page prose, infer one of: Restaurant / Healthcare / Legal / Home Services / Real Estate / Automotive / Generic.
• This routes citation-source recommendations and schema-subtype recommendations later — load references/local-citation-sources.md for the vertical's Tier-1 directories.

1. GBP signals on the page mcp__firecrawl-mcp__firecrawl_scrape (with formats: ["rawHtml"])

• Scrape homepage + /contact (or whichever page has the most local intent).
• From rawHtml extract:
• Embedded Google Maps iframe (``) — record place ID if present.
• Reviews widget / GBP rich snippet markup.
• aggregateRating JSON-LD block (presence is the strongest signal that the site wants stars in SERPs).
• Business hours visibility on page (open-at-search-time correlates with rank — Whitespark's #5 factor).
• Click-to-call: count of `` elements.
• GBP profile link: any ` to https://g.page/... or https://maps.app.goo.gl/... or https://www.google.com/maps/place/...`.
• If Firecrawl unavailable: WebFetch markdown can detect a tel: link in some renderings but loses iframes and JSON-LD. Mark Maps embed / aggregateRating / GBP profile-link detection as (skipped — Firecrawl required).

1. NAP consistency mcp__firecrawl-mcp__firecrawl_scrape on homepage + 5 sample pages

• Sample pages: homepage, /contact, /about, plus 2 service or location pages (pick from sitemap or top traffic pages).
• For each, extract:
• Visible NAP from rendered prose. Address pattern (street + city + region + postal), phone (tel: href + display format), business name (logo alt, footer, schema name).
• NAP from JSON-LD. Parse every ` block. Pull name, address.streetAddress, address.addressLocality, address.addressRegion, address.postalCode, telephone`.
• Compare across the 6 page samples + schema. Any divergence (different phone format on the contact page vs homepage; "Suite 200" missing from one footer; schema phone in international format while page shows local format) → record in nap-inconsistencies.csv.
• Brick-and-mortar only: if a Maps iframe is present, attempt to read the embedded address from the iframe URL (the place ID and address are URL-encoded). Compare to page/schema NAP. SAB skips this.
• If nap-inconsistencies.csv is empty after the scan, write nap-inconsistencies.csv as a one-line file with header only and note "NAP consistent across {n} pages and schema" in LOCAL-SEO-REPORT.md.

1. Local-pack rank tracking DATA_getSerpResults with country/region filters

• For each user-provided local keyword (or the 1–3 inferred from homepage):
• Call DATA_getSerpResults with the user's country and the most specific region/city the API supports (use DATA_getSerpLocations first to confirm a valid location code if the user supplied a city).
• Capture: top 10 organic, local-pack presence (yes/no), the 3 businesses in the local pack if shown (name, rating, review count), AIO presence.
• Cross-check: is the target domain in the top 10 organic? Is the target business name in the local pack?
• Save the parsed result per keyword to local-keywords.csv (columns: keyword,country,location,local_pack_present,target_in_pack,target_pack_position,target_organic_position,top_pack_competitor_1,top_pack_competitor_2,top_pack_competitor_3).
• Note the local-pack-ads caveat: the SE Ranking SERP returns the AI/ads-modified pack as Google serves it. If the local pack shows ads, record that — local-pack ad density jumped from 1% to 22% of mobile US local searches in 2025–2026 per Sterling Sky.

1. Reviews scraping mcp__firecrawl-mcp__firecrawl_scrape on user-provided review URLs

• Inputs (user-provided, optional). GBP listing URL (https://www.google.com/maps/place/...), Yelp business URL, Trustpilot business URL, BBB profile URL.
• For each provided URL: scrape with formats: ["rawHtml"]. From the parsed DOM, extract: total review count, average rating, date of most recent review (review velocity proxy), count of owner responses on the most recent 10 reviews.
• Aggregate signals:
• Velocity: ≥1 new review in last 18 days = healthy (Sterling Sky 18-day rule). >21 days since last = "review cliff" risk.
• Volume: 100 and average position >10, that's a local-pack reach gap — flag in LOCAL-SEO-REPORT.md "Top fixes" with the GSC numbers as supporting evidence.
• If property not verified for this account: surface "GSC: {target_domain} not verified — add it in Search Console" and continue.
• See skills/seo-google/references/cross-skill-integration.md for failure modes.

8c. GA4 organic by landing page (only if google-api.json is present, tier ≥ 2)

• Pull GA4 top organic landing pages, last 28 days:

python3 scripts/ga4_report.py --report top-pages --days 28 --json

• For multi-location sites, surface per-location-page sessions. If one location page captures 80%+ of organic traffic while peer location pages capture Snapshot dated {YYYY-MM-DD} · Country: {country} · Region: {region} · Primary keyword: "{keyword}"

Snapshot

• Business type: {Brick-and-Mortar | SAB | Hybrid}
• Industry vertical: {Restaurant | Healthcare | Legal | Home Services | Real Estate | Automotive | Generic}
• Pages sampled for NAP: {n}
• Local keywords tracked: {n}
• Local pack present on {n}/{m} keywords; target in pack on {p}/{m}
• Review platforms audited: {Google, Yelp, ... | not provided}
• GSC last 28d local-intent queries: {n} queries / {clicks} clicks / {impressions} impressions (or not configured)

Verdict: {STRONG | NEEDS WORK | WEAK}

{One-sentence summary anchored in dimension scores below}

Dimension scores (0–10)

| Dimension | Score | Top finding | |---|---|---| | GBP integration on page | {n}/10 | {one-line} | | NAP consistency | {n}/10 | {one-line} | | Local on-page (title/H1/contact/service pages) | {n}/10 | {one-line} | | Local-pack rank | {n}/10 | {one-line} | | Reviews & citations | {n}/10 | {one-line} | | Composite | {n}/10 | — |

Top fixes

Critical

1. {Specific fix anchored in a finding above. Example: "NAP discrepancy: footer shows '(212) 555-1234' but JSON-LD shows '+1-212-555-9999'. Pick one canonical phone, fix the wrong one." Cite the page/schema source.}

High

• {fix}

Medium

• {fix}

Low

• {fix}

Local-pack rank summary

• "{keyword 1}": local pack {present/absent}, target {in pack at #n / not in pack}, organic position {n}.
• "{keyword 2}": …
• (Full data: local-keywords.csv)

Reviews health (if audited)

• Google: {n} reviews, {rating} avg, last review {n} days ago, owner response rate {p}%.
• Yelp: …
• Trustpilot: …

Citation sample

• Google Business: {detected / not detected via site:google.com "..." search}
• Yelp: …
• Facebook: …
• BBB: …
• Apple Business Connect: …
• Bing Places: …
• {vertical-specific 1}: …
• {vertical-specific 2}: …

(Caveat: this is a sample, not a comprehensive citation audit. For definitive coverage, use Whitespark / BrightLocal / Yext.)

Schema status

• LocalBusiness JSON-LD: {present and valid / present but missing recommended properties / invalid / absent}
• Recommended next step: run seo-schema {homepage_url} for paste-ready LocalBusiness markup with the correct industry subtype.

Limitations

This skill could NOT assess:

• Geo-grid local-pack rank by lat/long. Requires a Maps API (e.g. DataForSEO Maps geo-grid endpoint) we don't have. Workaround: pay for Local Falcon, GMB Crush, or BrightLocal Local Search Grid.
• Comprehensive citation audit. WebSearch sampling covers ~8 directories; full audits cover 50+. Use Whitespark, BrightLocal, or Yext.
• GBP Insights data. Requires GBP API access scoped to the listing owner. Ask the listing owner to export Insights and share.
• Real-time local-pack rank tracking over time. This skill is a snapshot. Use SE Ranking's project-level rank tracker (PROJECT_runPositionCheck) or pair with seo-drift for diff snapshots.
• DataForSEO Business Listings. Not in our backend; if the user needs it, they'd need to subscribe to DataForSEO directly.

`local-keywords.csv` columns: `keyword,country,location,local_pack_present,target_in_pack,target_pack_position,target_organic_position,top_pack_competitor_1,top_pack_competitor_2,top_pack_competitor_3,aio_present`

`nap-inconsistencies.csv` columns: `source,name,address,phone,page_or_schema_path,canonical_value,divergence_note`

## Tips

- Respect SE Ranking Data API rate limit: 10 requests per second. Pace the per-keyword `DATA_getSerpResults` calls sequentially.
- Call `DATA_getCreditBalance` before running. ~15–25 SE Ranking credits typical, plus 6–12 Firecrawl credits when Firecrawl is installed.
- Verdict heuristic:
  - **STRONG**: composite ≥7/10, NAP consistent across all sampled pages, target in local pack on majority of keywords, valid LocalBusiness schema with industry-correct subtype, ≥10 Google reviews with healthy velocity.
  - **NEEDS WORK**: composite 4–6.9/10, OR 1+ NAP discrepancy, OR target out of local pack on majority of keywords. The "Top fixes" section is the deliverable here — most local-SEO audits land in this bucket.
  - **WEAK**: composite 5 locations, audit one location page per region rather than one per location — the local audit pattern repeats per location, so a sample establishes the baseline. If location-page quality variance is the suspected issue, pair with `seo-content-audit` on a sample of location pages.
- AI-search local context (ChatGPT, Perplexity, AI Overviews) is **not** this skill's job — pair with `seo-geo` (URL-level GEO) or `seo-ai-search-share-of-voice` (domain-level brand visibility) for AI-search local visibility.
- The 18-day review velocity rule (Sterling Sky) is the most actionable single number from review-platform analysis. If the most recent review is >21 days old, that's a leading indicator of upcoming local-pack rank drop — flag as Critical regardless of star rating.
- Citation directories per vertical: load `references/local-citation-sources.md`. The list is curated to the directories that move the needle (Tier 1 + vertical-specific), not the long tail.
- Pair with `seo-technical-audit` if site-wide technical-SEO issues surface in step 8 — this skill scopes to local-relevant findings, not the full audit.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [seranking](https://github.com/seranking)
- **Source:** [seranking/seo-skills](https://github.com/seranking/seo-skills)
- **License:** MIT
- **Homepage:** https://seranking.com

Install and usage instructions live in the source repository linked above.