io.github.2016judea/small-business-intelligence

Tools

• broker_diligence_prep

  Pre-diligence framework for a business broker or buyer evaluating a target: SDE framing (why the discretionary-earnings figure, not net income or raw EBITDA, is the relevant number, and what typically gets added back), a category multiple range the model must research fresh and date-stamp (never a hardcoded table), a public-signal red-flag checklist run before any financials are shared, and a prioritized seller-question list built from the specific gaps the research actually surfaces. Example invocations: - "Prep me for diligence on a brewery taproom listed in Minneapolis, MN" - "What questions should I ask the seller of a hair salon in Wichita, KS before I make an offer?" - "This restaurant is asking $650K — what red flags should I check before taking that seriously?" - "I'm looking at a nail salon in Tampa, FL asking $310K — sanity-check that against category multiples before I meet the seller"

  shallow
• business_teardown

  Full structured teardown of ONE named small business: digital presence, review signal, competitive position, pricing posture, visibility gaps, and prioritized, evidence-cited recommendations. The flagship tool — start here for any single-business question. Example invocations: - "Run a teardown of Mucci's Italian in Saint Paul, MN" - "Tear down The Gray Duck Tavern (bar) in Minneapolis and tell me what's actually broken" - "I'm thinking about buying Sunrise Nails in Denver, CO — give me a teardown before I look deeper"

  shallow
• competitor_landscape

  Maps the local competitive set for a category + metro: true competitors vs. adjacent players, a positioning matrix, and saturation signals. Example invocations: - "Map the competitive landscape for coffee shops in Saint Paul, MN" - "How saturated is the nail salon market in Aurora, CO?" - "Who are the real competitors to a new brewery taproom opening in the North Loop, Minneapolis?"

  shallow
• compose_report

  Assembles the outputs of any prior Small Business Intelligence tool calls into one polished, client-ready report: section order, executive-summary rules, evidence-citation standards, and tone guidance matched to the audience. This is what makes a multi-tool session feel like a finished product, not a pile of separate answers. Example invocations: - "I've run a teardown and a review-intelligence pass on this restaurant — compose it into a report for the owner" - "Assemble everything we've found on this brewery into a broker-facing diligence report" - "Turn the teardown and competitor landscape into a report I can hand an investor"

  shallow
• data_source_atlas

  Given a real question about a local market or a specific property, returns a source-first RESEARCH PLAN: which public record actually settles the question, how to reach it directly (county parcel GIS, Census CBP/ACS/permits, BLS series, state registries, licences, inspections), what the answer will be worth, and what the public record cannot answer at all. Use this BEFORE researching a local market — it is the difference between reading whatever a search engine surfaced and pulling the administrative record that settles it. Example invocations: - "Where would I actually find what 1420 Grand Ave in Saint Paul last sold for?" - "I want to know if Wichita has room for another dog daycare — what should I pull?" - "How do I find out who really owns this building and what else they own?" - "What public data would tell me if this neighborhood is actually growing?"

  shallow
• local_visibility_audit

  Audits a business's local search presence: map-pack factors, listing consistency, category selection, site fundamentals — what to check, and in what order — returned as a scored checklist. Example invocations: - "Run a local visibility audit on Fern & Fig Nail Bar in Cedar Rapids, IA" - "Why doesn't Steel Toe Brewing show up when someone searches 'brewery near me' in Louisville?" - "Give me a scored GBP/NAP checklist for a hair salon in Aurora, CO before I redo their listing"

  shallow
• market_opportunity_scan

  Gap analysis for a category x metro: detects underserved demand, oversaturation, and genuine whitespace using only public signals — for someone deciding whether/where to open, expand, or invest. Example invocations: - "Is there whitespace for a new brewery taproom in the North Loop, Minneapolis?" - "Scan the nail salon market in Aurora, CO for underserved demand" - "Where in Wichita, KS is full-service restaurant demand outrunning supply?"

  shallow
• pricing_benchmark

  Builds a defensible local pricing comparison within a category: how to normalize across differing service bundles, and what to do when competitors don't publish prices at all. Example invocations: - "Benchmark gel manicure pricing across nail salons in Denver, CO" - "Is this brewery's pint pricing in line with the Twin Cities taproom market?" - "Build a pricing comparison for full-service restaurants in Wichita, KS when most don't list prices online"

  shallow
• review_intelligence

  Mines public reviews for signal: a complaint taxonomy, theme extraction, sentiment trajectory over time, the differentiators customers actually cite, and red flags for a buyer. Example invocations: - "Mine the reviews for Al's Breakfast in Minneapolis for real patterns, not just a star rating" - "Perfect Image Salon in Wichita has a 4.6 average — check whether that's stable or masking a bad last 90 days" - "I'm evaluating The Anchor Room (bar) in Saint Paul, MN as a buyer — what do the reviews show about staffing turnover or an ownership change that the rating alone doesn't?"

  shallow
• twin_cities_datasets

  Lists the public-records datasets Brick & Mortar publishes for the seven-county Minneapolis-St. Paul metro, with real row counts, column names, the filtered cuts available, and the counties each one actually covers. Free, no account. Call this FIRST to learn what can be answered, then call twin_cities_records to ask it. These are joined county and federal records — parcels and lot lines, recorded sale prices, owners, rental licences, contamination files, business counts by trade, census tracts. Example invocations: - "What Twin Cities property data do you have access to?" - "Is there anything on contamination or storage tanks in Minneapolis?" - "What columns are in the recorded-sales dataset?"

  shallow
• twin_cities_records

  Answers a question about the Minneapolis-St. Paul metro from joined public records — what a property sold for and when, who owns it and what else they hold, what shares its lot line, whether it has a contamination or storage-tank file, who is licensed to trade there, how the neighbourhood's census tract compares. Give an `address` to answer about one property and its surroundings; omit it to ask about the whole market cut. Returns the true matching row count, up to six example rows, and a link to the complete file. Example invocations: - "What did 1420 Grand Ave, Saint Paul last sell for?" - "What commercial property sold within half a mile of 2900 Hennepin Ave, Minneapolis?" - "Does 500 Washington Ave S have a contamination file, and who owns it?"

  shallow

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