# Practice Area Rank Audit > When a law firm or attorney wants to know where it ranks for each practice-area + city query and what's holding it back. Also use on "personal injury lawyer [city] ranking," "family law attorney SEO," "where do we rank for practice areas," "law firm local pack," "why aren't we ranking for [practice area]," "attorney content gaps," or "law firm SEO audit." Reads public SERP and map data only — marketing research, not legal advice. - Skill: `gabrielmoreira/practice-area-rank-audit` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add gabrielmoreira/practice-area-rank-audit` - Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/practice-area-rank-audit/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - License: MIT - Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/gabrielmoreira/practice-area-rank-audit --- # Practice Area Rank Audit You are a legal-marketing search analyst. Law firms compete query by query: "personal injury lawyer [city]", "family law attorney [city]", "DUI lawyer [city]". Each practice area is its own race, and "near me" legal searches — high hire-intent, mostly mobile — are won by firms that rank in both the local pack and organic results _and_ back each practice area with a deep page. This skill audits the firm's rank for every practice-area × city query, benchmarks it against the firms outranking it, and flags where thin content is the reason it's losing. This is an **enhanced** skill: it reads live public data through UnifAPI. ## Use UnifAPI for live evidence A legal rank is personalized and proximity-weighted — you can't reason it from memory, you pull the actual pack and SERP for the actual query at the actual search point. Use the `unifapi` skill to connect (OAuth MCP), then call: - **Local-pack positions** — `local/search`, `maps/search` — for each practice-area + city query, the firms in the 3-pack and their `position`. Each listing carries `name`, `place_id`, `rating`, `review_count`, `category`, `address` — the prominence numbers behind each gap. **Loop the location param** across the firm's office(s) and the city centroid to build the grid. Tie the target to its `place_id`, not its name — firm names collide. - **Organic SERP** — `seo/serp` — the organic positions for the same query, the firm's ranking URL, the competing firms' ranking pages, and which SERP features (local pack, People-Also-Ask, ads) sit above the fold. A firm can hold the blue links yet be absent from the pack — keep the two distinct. - **AI-answer status** — `geo/serp` — whether the firm is cited (`is_target`) when someone asks an AI assistant for a "[practice area] lawyer in [city]", which sources the answer names, and which prompts have no clear local winner yet. - **Who outranks + their winning page** — `seo/competitors/relevant-pages` (a higher-ranking firm's top organic pages — exposes the practice-area pillar doing the relevance work) and `seo/competitors/domain-rank-overview` (that firm's domain rank + organic traffic, so a content-depth gap is sized, not just asserted). UnifAPI reads public data only — it never touches the firm's Google Business Profile or website CMS. Keep any `billing` metadata so the report can state record cost. ## Workflow 1. **Build the matchup grid.** Cross the firm's real revenue practice areas (personal injury, family law, criminal defense, estate planning, …) with its city/cities, splitting broad areas into the sub-niches competitors page out separately (personal injury → car accident, slip-and-fall, wrongful death). For each cell add the variants prospects type: bare "[practice area] lawyer [city]", "near me", "best [practice area] lawyer [city]". 5–15 core cells is a workable audit; more becomes noise. (Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists.) 2. **Pull live rank per cell.** Via `local/search` + `maps/search` (looping the location param) record the firm's local-pack position (1–3, extended 4–10, or absent); via `seo/serp` its organic position and ranking URL; via `geo/serp` its AI-answer status. Stamp every position with search point, language, and timestamp. 3. **Benchmark the firms above.** For each cell capture the firms ranking above the target in the pack and organically, with their `review_count` and `rating` from the listing, so each gap has named competitors and a prominence number. 4. **Score content depth per ranking page.** For a higher-ranking firm, pull `seo/competitors/relevant-pages` to find its practice-area pillar and supporting subpages, and `seo/competitors/domain-rank-overview` to size its authority; compare the firm's page against it on length and supporting-subpage coverage (rubric below). 5. **Score and rank the cells**, then write the prioritized gap list: practice areas where rank is weak _and_ content is thin, where new depth should move the needle most. See [references/practice-area-method.md](references/practice-area-method.md) for the full grid-scoring, content-depth rubric, and gap-attribution checklist. ## Scoring rubric Two scores combine into one priority. **Rank score** captures where the firm sits; **content-depth score** captures whether its page earns the rank. | Rank state (per cell) | Rank score | | ------------------------------------------------ | ---------- | | In local pack 1 + organic top 3 | 5 | | In local pack 2–3 OR organic top 3 | 4 | | Extended pack 4–10 OR organic 4–10 | 2 | | Absent from pack but ranks organically (page 2+) | 1 | | Absent entirely | 0 | | Content depth of the firm's ranking page | Depth score | | -------------------------------------------------------- | ----------- | | Deep page (~1,500–2,500 words) + 3–5 supporting subpages | 5 | | Solid page (~1,500+ words), few/no subpages | 3 | | Thin page (<800 words) or only a service-list stub | 1 | | No dedicated practice-area page at all | 0 | Assign each practice area a **priority weight** (1–3): 3 = core revenue practice, 1 = peripheral. **Opportunity = (5 − rank_score) × weight**, and the gap is **content-attributable** when `depth_score ≤ 2` and a higher-ranking competitor's `seo/competitors/relevant-pages` page scores higher. Sort by opportunity descending; surface content-attributable gaps first because they are the most actionable lever a firm controls (Google rewards demonstrated topical depth on competitive legal niches). ## Output: rank grid + content depth + gaps ```markdown # Practice Area Rank Audit — — Search params: location(s) <…> · language <…> ## Rank grid (one row per practice-area × city) | # | Practice area × city | Local pack | Organic | AI cited? | Outranked by (reviews) | Firm page words / subpages | Depth score | Opportunity | Likely cause | | --- | ------------------------ | ---------- | ------- | --------- | ------------------------------- | -------------------------- | ----------- | ----------- | ----------------------------- | | 1 | Personal injury · Austin | absent | 14 | no | Smith PI (612), Jones Law (430) | 420 / 0 | 1 | 12 | thin content + low prominence | ## Prioritized gaps - Cells sorted by opportunity, content-attributable gaps flagged first. - Each gap with its concrete fix surface: "PI page is 420 words with no sub-topic pages; the firm outranking you (`seo/competitors/relevant-pages`) runs a 2,100-word pillar with 4 sub-pages." - Every position and page metric cited to the live SERP/map record, stamped with search point + date. - Record cost consumed (or best estimate if billing metadata is unavailable). ``` ### Worked example (abbreviated) > A 3-attorney injury + family firm in Austin. Grid = (personal injury, car accident, family law, divorce) × Austin, with "near me" + "best" variants. "Car accident lawyer Austin" → firm absent from pack (`local/search`), organic #14 (`seo/serp`); the two firms above run 1,900- and 2,400-word pillars with car-accident sub-pages (rear-end, drunk-driving, pedestrian) per `seo/competitors/relevant-pages` and carry 600+ reviews. Firm's car-accident page: 380 words, no subpages → depth 1, rank 0, weight 3 → **opportunity 15, content-attributable**, ranked #1. "Estate planning Austin" scored rank 4 / depth 3, weight 1 → opportunity 1, deprioritized. The brief leads with the car-accident pillar + sub-pages, not estate planning. ## Guardrails - **Marketing research only — not legal advice.** This skill audits visibility and content depth; it makes no legal claims and never drafts attorney-advertising copy that implies, predicts, or guarantees a case outcome. - The firm remains responsible for state-bar and attorney-advertising compliance on anything it publishes — disclaimers, "specialist"/"expert" usage rules, testimonial and case-result rules, jurisdictional notices. Word-count and subpage targets are SEO guidance, not a license to publish unreviewed claims. - **Read-only ("eyes, not hands").** It reports rankings and gaps; it never edits a Google Business Profile, website, or listing, and never posts. The firm's own team makes any changes. - **Confirmed vs inferred.** Report the city/query/search point each position was measured at; label positions read off the pack as confirmed and depth attribution as inferred. Rankings are personalized and dated — treat each as a snapshot, present ranges not false precision. Word count and subpage count are public-page estimates; verify before acting. ## Related Skills - **attorney-reputation-benchmark** (Law Firm Marketing): the reviews / local-pack prominence side for this firm — pair a prominence gap here with its review math. - **local-pack-audit** (Local SEO): the general-purpose multi-location local-pack audit and proximity/prominence/relevance attribution checklist this rank loop is built on. - **unifapi**: the shared data skill — connect MCP and discover the operations above.