{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_spatialzr","slug":"spatialzr","name":"Spatialzr","type":"product","url":"https://6t77uzbtln0h.umso.co","page_url":"https://unfragile.ai/spatialzr","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_spatialzr__cap_0","uri":"capability://data.processing.analysis.cre.specialized.location.scoring.with.multi.factor.weighting","name":"cre-specialized location scoring with multi-factor weighting","description":"Computes location desirability scores for commercial real estate sites by integrating proprietary weighting algorithms across demographic, economic, accessibility, and market condition factors specific to CRE use cases. The system likely ingests normalized data from multiple sources (census, commercial databases, transaction records) and applies domain-specific scoring models that differ from generic geospatial tools, enabling comparative site ranking without manual consultant analysis.","intents":["I need to quickly rank 50 potential retail locations by market viability without hiring a consultant","I want to understand which neighborhoods score highest for my specific tenant type (e.g., quick-service restaurant vs. medical office)","I need to justify site selection decisions to investors with quantitative scoring rather than intuition"],"best_for":["Commercial real estate brokers evaluating multiple sites simultaneously","Investment firms conducting portfolio site analysis","Corporate real estate teams site-selecting for expansion"],"limitations":["Scoring algorithm weights and methodology not publicly documented — difficult to audit or customize for niche markets","Likely requires minimum geographic coverage (e.g., US metro areas only) — international or rural markets may lack sufficient data","Historical performance of scoring model against actual lease/sale outcomes unknown — ROI validation difficult"],"requires":["Active Spatialzr subscription with API or web interface access","Geographic coordinates or address data for target locations","Tenant type or property category classification for weighted scoring"],"input_types":["structured location data (addresses, coordinates)","property type classification (retail, office, industrial, etc.)","optional: custom weighting parameters or tenant profile"],"output_types":["numeric location scores (likely 0-100 scale)","ranked location lists with score breakdowns","comparative score visualizations"],"categories":["data-processing-analysis","real-estate-analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_1","uri":"capability://image.visual.thematic.mapping.with.multi.layer.demographic.and.market.overlays","name":"thematic mapping with multi-layer demographic and market overlays","description":"Renders interactive choropleth and heat-map visualizations that overlay multiple thematic data layers (demographics, economic indicators, competitor locations, lease rates, foot traffic) on geographic boundaries (census tracts, ZIP codes, custom polygons). The system allows users to toggle layers on/off, adjust color scales, and correlate patterns across themes without requiring GIS expertise, likely using a web-based mapping engine (Mapbox, Google Maps, or proprietary) with server-side data aggregation.","intents":["I need to visualize where high-income demographics overlap with available retail space to identify premium tenant opportunities","I want to see competitor density heatmaps alongside foot traffic patterns to find underserved markets","I need to present market analysis to stakeholders with clear visual evidence of neighborhood trends"],"best_for":["Brokerage teams conducting market presentations to clients","Real estate investors evaluating portfolio diversification across geographies","Corporate site selection committees comparing multiple markets visually"],"limitations":["Map rendering performance may degrade with 10+ simultaneous layers or very large geographic areas — zoom/pan responsiveness unknown","Data freshness varies by source — some overlays may be annual census data while others are monthly; temporal misalignment not clearly disclosed","Custom polygon upload or boundary definition likely limited to predefined geographies (ZIP, census tract) — custom trade areas may require manual workaround"],"requires":["Web browser with WebGL support (Chrome, Firefox, Safari, Edge)","Spatialzr account with thematic mapping feature access","Geographic area of interest defined (city, region, or custom boundary)"],"input_types":["geographic boundaries (ZIP codes, census tracts, custom polygons)","theme selection (demographics, economics, competition, traffic, etc.)","optional: custom data upload for proprietary overlays"],"output_types":["interactive web-based choropleth maps","heat maps with color-coded intensity","layer toggle controls and legend","exportable map images or PDF reports"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_10","uri":"capability://automation.workflow.custom.report.generation.and.export.with.market.context","name":"custom report generation and export with market context","description":"Generates formatted market analysis reports combining location scores, thematic maps, demographic profiles, lease rate benchmarks, and competitive analysis into exportable documents (PDF, PowerPoint) with market context and recommendations. The system likely uses templated report generation with data-driven visualizations, enabling users to create professional market analysis deliverables without manual report writing.","intents":["I need to create a professional market analysis report for a client presentation in 30 minutes instead of 3 days","I want to export a thematic map with demographic overlays and lease rate data for a board presentation","I need to generate a competitive analysis report comparing three markets for an investment committee"],"best_for":["Brokers creating client presentations and market analysis reports","Investment teams documenting market analysis for investment decisions","Corporate real estate teams presenting site selection analysis to stakeholders"],"limitations":["Report templates may be limited or inflexible — difficult to customize for specific client needs or branding","Export formats limited to PDF/PowerPoint — no native integration with presentation software for live updates","Report generation may require manual data selection and ordering — no fully automated report creation","Data freshness in exported reports not timestamped — difficult to track when analysis was conducted"],"requires":["Spatialzr subscription with report generation feature","Analysis data (location scores, maps, demographics, etc.) already generated in platform","Optional: custom branding or template preferences"],"input_types":["selected analysis components (maps, scores, data tables)","report template selection","optional: custom branding or narrative text"],"output_types":["PDF market analysis reports","PowerPoint presentations with data visualizations","exportable data tables and charts","branded deliverables with market context"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_11","uri":"capability://tool.use.integration.saved.analysis.and.workspace.collaboration.for.teams","name":"saved analysis and workspace collaboration for teams","description":"Enables users to save analysis workspaces (filter criteria, map layers, selected properties, custom cohorts) and share them with team members for collaborative review and iteration. The system likely stores analysis state in a database and provides access controls for team-based sharing, enabling multiple users to build on previous analysis without recreating filters or selections.","intents":["I need to save my market analysis so my colleague can review it and add their insights","I want to share a filtered property list with my team so we can discuss opportunities together","I need to maintain a library of saved market analyses for future reference and comparison"],"best_for":["Brokerage teams collaborating on market analysis and site selection","Investment firms with multiple analysts reviewing the same markets","Corporate real estate teams building institutional knowledge of markets"],"limitations":["Collaboration features likely limited to view/comment — no real-time co-editing or simultaneous analysis","Saved analysis versioning not mentioned — difficult to track changes or revert to previous versions","Access controls may be limited to team-level sharing — no granular permission management","Saved analysis storage limits unknown — may have quotas on number of saved workspaces"],"requires":["Spatialzr subscription with team/collaboration features","Team member accounts with appropriate access permissions","Optional: workspace naming and organization conventions"],"input_types":["analysis workspace (filters, maps, selections)","team member email addresses for sharing","optional: comments or notes on analysis"],"output_types":["saved workspace links or identifiers","shared analysis access for team members","workspace history or version tracking","collaboration comments or annotations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_2","uri":"capability://data.processing.analysis.multi.source.data.consolidation.and.normalization.for.cre","name":"multi-source data consolidation and normalization for cre","description":"Ingests and harmonizes data from multiple commercial real estate sources (public records, MLS feeds, demographic databases, foot traffic providers, economic indicators) into a unified data model, handling schema mapping, temporal alignment, and geographic standardization. The platform abstracts away the complexity of maintaining separate subscriptions and API integrations, likely using ETL pipelines that normalize address formats, reconcile overlapping records, and resolve geographic mismatches across sources.","intents":["I want a single dashboard showing lease rates, foot traffic, and demographics without managing five separate data subscriptions","I need to cross-reference competitor locations from MLS with foot traffic data to identify market gaps","I want historical trend data across multiple sources without manually exporting and merging spreadsheets"],"best_for":["Brokerage firms managing multiple data subscriptions and seeking consolidation","Real estate investment teams conducting due diligence across multiple data dimensions","Market research teams needing unified data pipelines for analysis"],"limitations":["Data freshness varies by source — some feeds update daily, others monthly; no clear SLA on data currency disclosed","Reconciliation of overlapping records (e.g., same property listed in multiple sources) may introduce deduplication errors or false positives","API rate limits or data availability restrictions from upstream providers may create gaps in coverage or delayed updates","No transparent audit trail of data transformations — difficult to trace data lineage or identify source-specific biases"],"requires":["Spatialzr subscription with data consolidation feature","Geographic area with sufficient data coverage (major US metros likely well-covered; rural areas may have gaps)","Optional: API credentials for custom data source integration (if supported)"],"input_types":["geographic boundaries or property identifiers","data source selection (MLS, census, foot traffic, etc.)","optional: custom data feeds or proprietary datasets"],"output_types":["unified property records with cross-source attributes","consolidated market datasets (lease rates, demographics, traffic)","time-series data aggregations across sources","data export in standard formats (CSV, JSON)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_3","uri":"capability://data.processing.analysis.comparative.market.analysis.with.automated.trend.detection","name":"comparative market analysis with automated trend detection","description":"Analyzes historical and current market data across multiple geographies to identify trends, anomalies, and comparative metrics (e.g., lease rate growth, vacancy trends, demographic shifts) using time-series analysis and statistical comparison. The system likely applies pattern recognition algorithms to detect inflection points, seasonal patterns, and outliers, surfacing insights without requiring manual statistical modeling or spreadsheet analysis.","intents":["I need to identify which markets are experiencing rapid lease rate growth to prioritize investment","I want to compare vacancy trends across five markets to understand which is most competitive","I need to detect emerging neighborhoods before they become obvious to competitors"],"best_for":["Investment firms conducting market timing and portfolio allocation decisions","Brokers identifying emerging markets for client expansion","Market researchers tracking long-term CRE trends and cycles"],"limitations":["Trend detection algorithms may lag real-time market shifts by 1-3 months depending on data source update frequency","Anomaly detection thresholds not customizable — may flag false positives in volatile markets or miss subtle shifts in stable markets","Historical data depth varies by market — newer markets or rural areas may have insufficient history for reliable trend analysis","No clear explanation of statistical methods used — difficult to validate confidence intervals or understand model assumptions"],"requires":["Spatialzr subscription with trend analysis feature","Geographic markets with sufficient historical data coverage (typically 2+ years)","Optional: custom date ranges or comparison parameters"],"input_types":["geographic markets or property types for comparison","time period for analysis (e.g., last 12 months, 5-year trend)","optional: custom metrics or thresholds for anomaly detection"],"output_types":["trend charts with growth/decline indicators","comparative tables across markets","anomaly alerts or inflection point notifications","statistical summaries (growth rates, volatility measures)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_4","uri":"capability://data.processing.analysis.interactive.property.level.filtering.and.cohort.analysis","name":"interactive property-level filtering and cohort analysis","description":"Enables users to define complex filter criteria across multiple dimensions (property type, size, lease rate range, demographic profile, proximity to competitors) to create custom property cohorts, then analyze aggregate metrics across the filtered set. The system likely uses a columnar database or in-memory analytics engine to support rapid filtering and aggregation across millions of property records without requiring SQL knowledge.","intents":["I need to find all retail properties between 5,000-10,000 sq ft in high-income ZIP codes with lease rates under $30/sq ft","I want to analyze average foot traffic for all quick-service restaurants within 2 miles of transit hubs","I need to compare lease rate trends for Class A office buildings vs. Class B in the same market"],"best_for":["Brokers conducting targeted property searches for specific tenant profiles","Investment teams analyzing cohort performance (e.g., all properties in a specific submarket)","Market researchers comparing property segments without SQL expertise"],"limitations":["Filter performance may degrade with 10+ simultaneous criteria or very large property datasets — query latency unknown","Custom filter combinations not saved by default — users may need to recreate complex filters repeatedly","Cohort size thresholds not disclosed — very small cohorts (e.g., 3 properties) may produce unreliable aggregate statistics","No built-in statistical significance testing — users may draw conclusions from small sample sizes"],"requires":["Spatialzr subscription with filtering/analysis feature","Property dataset with sufficient coverage in target geography","Optional: saved filter templates or custom filter definitions"],"input_types":["filter criteria (property type, size, price, location, demographics)","aggregation metrics (average lease rate, median foot traffic, etc.)","optional: custom date ranges or comparison periods"],"output_types":["filtered property lists with detailed attributes","aggregate statistics (mean, median, distribution) for cohorts","comparative charts across cohorts","exportable property lists or analysis reports"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_5","uri":"capability://data.processing.analysis.foot.traffic.and.pedestrian.activity.visualization.and.analysis","name":"foot traffic and pedestrian activity visualization and analysis","description":"Integrates foot traffic data from mobile location providers or sensor networks to visualize pedestrian activity patterns, peak hours, and traffic flows around properties. The system likely aggregates anonymized foot traffic signals (from location services, WiFi, or foot traffic sensors) and displays them as heat maps, time-series charts, or comparative metrics, enabling users to understand real-world activity without conducting manual foot traffic studies.","intents":["I need to validate that a retail location has sufficient foot traffic before recommending it to a tenant","I want to understand peak traffic hours and patterns to advise on staffing or operational hours","I need to compare foot traffic between competing retail locations to justify lease rate differences"],"best_for":["Retail brokers evaluating location viability for foot-traffic-dependent tenants","Retailers conducting site selection for new store locations","Landlords justifying rental rates based on actual foot traffic metrics"],"limitations":["Foot traffic data accuracy and coverage varies by location — urban areas well-covered, rural areas may lack data","Data source (mobile location, WiFi, sensors) not disclosed — methodology affects reliability and potential biases (e.g., mobile-only data misses foot traffic from non-smartphone users)","Historical foot traffic data depth unknown — may be limited to recent months rather than multi-year trends","Foot traffic spikes from temporary events (holidays, promotions) may distort baseline patterns without clear anomaly flagging"],"requires":["Spatialzr subscription with foot traffic data access","Property location with foot traffic data coverage (typically major metros)","Optional: custom time periods or comparison parameters"],"input_types":["property address or geographic area","time period for analysis (daily, weekly, monthly)","optional: comparison properties or benchmarks"],"output_types":["foot traffic heat maps (time and location)","time-series charts showing traffic patterns","peak hour and traffic volume metrics","comparative foot traffic rankings"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_6","uri":"capability://data.processing.analysis.tenant.mix.and.competitive.landscape.mapping","name":"tenant mix and competitive landscape mapping","description":"Identifies and visualizes existing tenant types, competitor locations, and market saturation across geographies using data from commercial databases and MLS feeds. The system likely classifies tenants by category (quick-service restaurant, medical office, etc.), calculates saturation metrics (competitors per capita), and displays competitor locations on maps, enabling users to understand competitive dynamics without manual research.","intents":["I need to understand the competitive landscape for a quick-service restaurant location before recommending it to a tenant","I want to identify submarkets with low medical office density to find underserved markets","I need to visualize all competing retailers within 1 mile of a target property to assess cannibalization risk"],"best_for":["Brokers advising tenants on site selection and competitive positioning","Retailers evaluating cannibalization risk from nearby competitors","Landlords understanding tenant mix optimization for mixed-use properties"],"limitations":["Tenant classification accuracy depends on data source quality — small or new tenants may be misclassified or missing","Competitor identification limited to properties in commercial databases — informal or unlisted competitors may be missed","Saturation metrics not normalized for market size or demographics — raw competitor counts may be misleading without context","Tenant data freshness varies — recent openings/closures may not be reflected immediately"],"requires":["Spatialzr subscription with tenant/competitor data access","Property location with sufficient commercial database coverage","Optional: custom tenant type definitions or competitor filters"],"input_types":["property address or geographic area","tenant type or competitor category","optional: search radius or custom filters"],"output_types":["competitor location maps with tenant type classification","saturation metrics (competitors per capita, market share)","tenant mix summaries for areas","competitive analysis reports"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_7","uri":"capability://data.processing.analysis.demographic.profile.matching.and.targeting","name":"demographic profile matching and targeting","description":"Analyzes demographic characteristics (age, income, education, household composition) of geographic areas and matches them against tenant or investor profiles to identify compatible locations. The system likely ingests census data, consumer surveys, and proprietary demographic databases, then applies matching algorithms to surface locations with demographic profiles matching specified criteria.","intents":["I need to find neighborhoods with high concentrations of affluent 35-55 year-olds for a luxury retail tenant","I want to identify areas with growing young professional populations for a co-working space","I need to understand demographic trends to predict which markets will support premium retail in 5 years"],"best_for":["Brokers matching tenants to demographically compatible locations","Retailers conducting site selection based on target customer demographics","Investors identifying emerging demographic trends for long-term portfolio positioning"],"limitations":["Demographic data freshness limited to census cycles (typically 5-10 years old) — recent demographic shifts may not be captured","Census data aggregated to geographic units (census tract, ZIP code) — may mask micro-demographic variations within neighborhoods","Demographic matching algorithms not transparent — unclear how weights are assigned to different demographic factors","No predictive demographic modeling — difficult to forecast demographic changes beyond historical trends"],"requires":["Spatialzr subscription with demographic data access","Geographic area with census data coverage (typically US only)","Optional: custom demographic profile definitions or weighting"],"input_types":["geographic area or property location","demographic criteria (age, income, education, household type)","optional: custom demographic profiles or comparison benchmarks"],"output_types":["demographic profile summaries for areas","demographic matching scores or compatibility rankings","demographic trend charts and comparisons","demographic reports with visualizations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_8","uri":"capability://data.processing.analysis.lease.rate.and.pricing.intelligence.with.historical.trends","name":"lease rate and pricing intelligence with historical trends","description":"Aggregates commercial lease rate data from MLS feeds, transaction records, and broker reports to provide current pricing benchmarks and historical trend analysis by property type, size, and location. The system likely normalizes lease rates across different structures (gross, triple-net, modified) and adjusts for temporal and geographic variations, enabling users to understand market pricing without manual rate sheet collection.","intents":["I need to know the current market lease rate for Class A office space in downtown to advise a tenant on fair pricing","I want to understand lease rate trends over the past 5 years to forecast future pricing","I need to compare lease rates across similar properties to identify outliers or opportunities"],"best_for":["Brokers advising tenants and landlords on fair market lease rates","Landlords setting rental rates based on market comparables","Investors forecasting cash flow based on market lease rate trends"],"limitations":["Lease rate data may be incomplete or delayed — not all transactions reported to MLS or databases","Lease rate normalization across different structures (gross, triple-net) may introduce errors if not transparent","Outlier transactions (distressed sales, long-term leases at below-market rates) may skew averages without clear flagging","Lease rate forecasting not provided — users must manually extrapolate trends"],"requires":["Spatialzr subscription with lease rate data access","Property type and location for rate comparison","Optional: custom date ranges or property size filters"],"input_types":["property type (office, retail, industrial, etc.)","geographic area or specific property","optional: property size, lease structure, or date range"],"output_types":["current lease rate benchmarks (average, median, range)","historical lease rate trend charts","comparable property lease rates","lease rate reports with market context"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_spatialzr__cap_9","uri":"capability://data.processing.analysis.accessibility.and.transportation.connectivity.analysis","name":"accessibility and transportation connectivity analysis","description":"Evaluates property accessibility by analyzing proximity to transit (public transportation, highways), walkability scores, and transportation infrastructure. The system likely ingests transit network data, road networks, and walkability metrics, then calculates accessibility scores and visualizes transportation connectivity, enabling users to understand location convenience without manual transportation research.","intents":["I need to evaluate how accessible a property is to public transit for a transit-dependent tenant","I want to understand walkability scores to advise a retail tenant on foot traffic potential","I need to compare transportation connectivity across multiple locations to identify the most accessible option"],"best_for":["Brokers advising tenants on location accessibility and convenience","Urban retailers evaluating walkability and transit access","Investors assessing long-term location viability based on transportation infrastructure"],"limitations":["Walkability scores based on static street network data — may not reflect actual pedestrian experience or safety","Transit accessibility limited to current transit networks — future transit expansion not forecasted","Accessibility metrics may not account for terrain, weather, or other real-world factors affecting actual accessibility","No integration with commute time or traffic pattern data — accessibility measured by proximity only"],"requires":["Spatialzr subscription with accessibility/transportation data","Property location with transit and street network coverage (typically urban areas)","Optional: custom accessibility criteria or weighting"],"input_types":["property address or geographic area","optional: transit type filters (public transit, highways, etc.)"],"output_types":["accessibility scores (walkability, transit proximity)","transit network visualizations","proximity metrics to transit stations or highways","accessibility comparison reports"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Active Spatialzr subscription with API or web interface access","Geographic coordinates or address data for target locations","Tenant type or property category classification for weighted scoring","Web browser with WebGL support (Chrome, Firefox, Safari, Edge)","Spatialzr account with thematic mapping feature access","Geographic area of interest defined (city, region, or custom boundary)","Spatialzr subscription with report generation feature","Analysis data (location scores, maps, demographics, etc.) already generated in platform","Optional: custom branding or template preferences","Spatialzr subscription with team/collaboration features"],"failure_modes":["Scoring algorithm weights and methodology not publicly documented — difficult to audit or customize for niche markets","Likely requires minimum geographic coverage (e.g., US metro areas only) — international or rural markets may lack sufficient data","Historical performance of scoring model against actual lease/sale outcomes unknown — ROI validation difficult","Map rendering performance may degrade with 10+ simultaneous layers or very large geographic areas — zoom/pan responsiveness unknown","Data freshness varies by source — some overlays may be annual census data while others are monthly; temporal misalignment not clearly disclosed","Custom polygon upload or boundary definition likely limited to predefined geographies (ZIP, census tract) — custom trade areas may require manual workaround","Report templates may be limited or inflexible — difficult to customize for specific client needs or branding","Export formats limited to PDF/PowerPoint — no native integration with presentation software for live updates","Report generation may require manual data selection and ordering — no fully automated report creation","Data freshness in exported reports not timestamped — difficult to track when analysis was conducted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.096Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=spatialzr","compare_url":"https://unfragile.ai/compare?artifact=spatialzr"}},"signature":"NxoFoklMddIkv+CAQc2HLmyO2v8yEHuMVlv5f9GWxAdmtmTlbXyjykDr9WDJAB56JF++FOLidEVZZhZYvcmOBw==","signedAt":"2026-06-20T15:56:08.102Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/spatialzr","artifact":"https://unfragile.ai/spatialzr","verify":"https://unfragile.ai/api/v1/verify?slug=spatialzr","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}