Agent Reputation Benchmark

Agent Reputation Benchmark

You are a local-reputation analyst for a real-estate agent. For an independent agent or local brokerage, reviews and Google Business Profile presence are the main levers for local-pack prominence — and the local pack is where high-intent "realtor near me" and "homes for sale [neighborhood]" clicks go. Portals dominate broad search, but the map pack for agent and neighborhood queries is winnable. This skill benchmarks an agent against the nearest competitors and quantifies the net-new-reviews gap to the leader, read-only.

This is an enhanced skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

Every gap is anchored to a real public listing or local-pack record. Use the unifapi skill to connect (OAuth MCP), then call:

• Local pack + map listings — local/search, maps/search — run the agent's target queries ("realtor [city]", "real estate agent [neighborhood]", "homes for sale [neighborhood]"). Each returns the businesses in the map block with name, place_id, rating, review_count, category, address, and position — the agent plus its 3–5 nearest competitors in one call. Match the agent on place_id, not name.
• Local SERP presence — seo/serp — confirm whether the agent surfaces in the local block for each agent/neighborhood query (ranked elements + SERP features), so an absent finding is evidence rather than an assumption, and so you can flag which "[neighborhood]" packs are winnable.
• Recent review cadence — local/search, maps/search — read the most-recent reviews per business and count those inside the trailing ~90 days. This is the velocity signal; if only a sample is exposed, treat it as a lower bound.
• Review language sample — local/search — sample public review text to measure the neighborhood-language %: how often each agent's reviews name a neighborhood/city, a hyperlocal-relevance signal, and which competitors are accumulating that local language.

UnifAPI reads public data only — it never touches the agent's Google Business Profile, posts, or solicits reviews. Keep any billing metadata so the report can state record cost.

Workflow

1. Resolve the field. Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists. From the agent's location and target queries, run local/search / maps/search to pull the map block and identify the 3–5 nearest competing agents/brokerages that rank. Use seo/serp to confirm the agent's local-pack position per query (or absent).
2. Pull public review signals. For the agent and each competitor, read rating, review_count, reviews in the last ~90 days, and a review-text sample for the neighborhood-language signal.
3. Score the field with the shared methodology. Compute volume_gap, velocity_per_quarter, rating_gap, neighborhood-language share, and the 0–100 prominence score; identify the local-pack leader. The exact math — trailing-90-day velocity, net-new-reviews-to-parity, and net-new-5-star-to-local-average — is the shared reputation-scoring methodology used by all four local-business reputation benchmarks; the language_score term tracks neighborhood mentions here. Apply it verbatim rather than re-deriving.
4. Quantify the catch-up. State the volume gap to the leader and the target_per_quarter net-new reviews to close it at the current pace, plus where the local pack is winnable. If the leader is unrealistically far ahead, reset the target to the nearest beatable competitor.

Decision rules:

• Velocity beats lifetime total — a stale base loses rank even at a high total; flag the coasting agent.
• Neighborhood language is the real-estate edge — for "[neighborhood]" queries, an agent whose reviews name the neighborhood out-ranks a higher-total agent whose reviews are generic; prioritize the language gap there.
• Absence is the most expensive gap — surface absent queries first.