{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_autoeasy","slug":"autoeasy","name":"AutoEasy","type":"product","url":"https://www.autoeasy.com","page_url":"https://unfragile.ai/autoeasy","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_autoeasy__cap_0","uri":"capability://text.generation.language.conversational.car.recommendation.engine.with.preference.profiling","name":"conversational car recommendation engine with preference profiling","description":"Processes natural language inputs about budget, lifestyle, vehicle use cases, and personal preferences through a dialogue-based interface to generate ranked vehicle recommendations. The system likely maintains conversation context across multiple turns to refine recommendations iteratively, using intent classification to extract structured preference signals (budget range, vehicle type, fuel efficiency priority, family size, etc.) from unstructured chat messages and mapping these to a vehicle database via multi-attribute matching algorithms.","intents":["I want personalized car suggestions based on my budget and lifestyle without visiting multiple dealer websites","I need to narrow down vehicle options through a conversational Q&A rather than static comparison tables","I want the AI to ask clarifying questions about my needs and refine recommendations based on my answers"],"best_for":["first-time car buyers unfamiliar with vehicle categories and specifications","price-conscious shoppers wanting to reduce research time before dealer visits","users seeking conversational guidance over traditional comparison tools"],"limitations":["Recommendations depend on underlying vehicle database freshness — unclear if inventory reflects real-time dealer stock or historical data","Cannot account for local market variations in pricing, availability, or regional vehicle preferences","May oversimplify complex reliability factors, insurance cost variations by model year, or depreciation curves that require domain expertise","Conversational context is likely session-scoped with no persistent user profile across sessions unless explicitly saved"],"requires":["Access to vehicle specification database (make, model, year, trim, MSRP, key features)","Natural language processing model capable of intent classification and entity extraction","Chat interface with multi-turn conversation state management"],"input_types":["natural language text (chat messages)","implicit preference signals (budget mentions, lifestyle context, vehicle type keywords)"],"output_types":["ranked vehicle recommendations (structured list with make/model/trim/price)","conversational explanations of why each vehicle matches user criteria","follow-up clarifying questions to refine recommendations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_1","uri":"capability://planning.reasoning.negotiation.strategy.guidance.with.market.context","name":"negotiation strategy guidance with market context","description":"Provides data-driven negotiation tactics and talking points by analyzing typical dealer markups, regional pricing variations, and seasonal market conditions. The system likely ingests historical pricing data, MSRP information, and market trend signals to generate contextual negotiation advice (e.g., 'this model typically sells for 8-12% below MSRP in your region during Q4'). Guidance is delivered conversationally, translating raw market data into actionable phrases users can employ during dealer interactions.","intents":["I want to know what price range is realistic for a specific vehicle in my market before negotiating with dealers","I need talking points and negotiation tactics to avoid overpaying or being pressured by sales staff","I want to understand typical dealer markups and incentives for the vehicles I'm considering"],"best_for":["price-conscious buyers wanting to reduce information asymmetry with dealers","first-time negotiators lacking confidence in dealer interactions","users in competitive markets where negotiation leverage varies significantly by season"],"limitations":["Pricing data freshness is critical but undisclosed — advice based on stale market data could be counterproductive","Regional pricing variations are complex (dealer density, local competition, regional preferences) and may not be fully captured","Cannot account for individual dealer-specific incentives, inventory pressure, or salesperson commission structures that affect negotiation dynamics","Negotiation effectiveness depends on user confidence and communication skills — AI guidance alone cannot overcome poor execution","No real-time inventory visibility means recommendations may reference vehicles not actually available locally"],"requires":["Historical pricing database with MSRP, typical selling prices, and regional variations","Market trend data (seasonal demand patterns, inventory levels, incentive patterns)","Ability to geolocate user or accept region input to contextualize advice","Natural language generation for converting data insights into conversational negotiation tips"],"input_types":["vehicle specification (make, model, year, trim)","user location or region","target price range or budget constraints"],"output_types":["realistic price range for target vehicle in user's market","negotiation talking points and tactics","seasonal/timing advice (e.g., 'end of month/quarter is better for negotiation')","typical dealer markup and incentive information"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_2","uri":"capability://data.processing.analysis.multi.attribute.vehicle.comparison.with.explainable.reasoning","name":"multi-attribute vehicle comparison with explainable reasoning","description":"Compares multiple vehicles across dimensions (price, fuel efficiency, safety ratings, features, reliability scores, insurance costs, depreciation) and explains trade-offs in conversational language. The system likely implements a weighted multi-criteria decision analysis (MCDA) approach where different attributes are scored and weighted based on user priorities expressed in chat. Explanations are generated to highlight why one vehicle might be better for a specific use case (e.g., 'this sedan is $3k cheaper but the SUV has better cargo space for your family of 5').","intents":["I want to compare 2-3 vehicles side-by-side across multiple dimensions without building my own spreadsheet","I need the AI to explain trade-offs between vehicles in terms of my specific priorities","I want to understand how different attributes (price, safety, fuel economy) stack up for vehicles I'm considering"],"best_for":["users in the active consideration phase comparing 2-4 specific vehicles","buyers with competing priorities (e.g., budget vs. features) who need trade-off analysis","non-technical users who find raw specification sheets overwhelming"],"limitations":["Comparison quality depends on data completeness — missing or outdated safety ratings, insurance cost estimates, or reliability scores reduce accuracy","Weighting of attributes is inferred from conversation but may not capture user priorities accurately; no explicit preference elicitation mechanism visible","Insurance cost estimates are likely generic by vehicle class, not personalized by user age, location, driving record, or coverage preferences","Depreciation curves are historical averages and cannot predict future market shifts or model-specific issues","No integration with actual dealer inventory — comparisons may reference vehicles not available locally or currently in stock"],"requires":["Comprehensive vehicle specification database (MSRP, fuel economy, safety ratings, feature lists, dimensions)","Third-party data sources for insurance estimates, reliability scores (JD Power, Consumer Reports equivalents), depreciation data","Multi-criteria decision analysis algorithm to weight and score attributes","Natural language generation to explain trade-offs conversationally"],"input_types":["vehicle identifiers (make, model, year, trim) for 2-4 vehicles","user priorities expressed conversationally (e.g., 'fuel economy is important', 'I need cargo space')"],"output_types":["structured comparison table (attributes × vehicles)","conversational explanation of trade-offs and recommendations","attribute-level scoring and weighting rationale"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_3","uri":"capability://planning.reasoning.vehicle.suitability.assessment.for.lifestyle.and.use.cases","name":"vehicle suitability assessment for lifestyle and use cases","description":"Evaluates whether specific vehicles align with user's stated lifestyle, family size, commute patterns, climate, and intended use cases through conversational profiling. The system extracts lifestyle signals from chat (e.g., 'I have two kids and a dog', 'I live in snowy Minnesota', 'I commute 60 miles daily') and maps these to vehicle attributes (cargo capacity, AWD availability, fuel efficiency, seating configuration, towing capacity). Suitability is communicated as narrative explanations rather than scores, e.g., 'this truck is overkill for your 5-mile commute but great if you plan weekend camping trips'.","intents":["I want to know if a vehicle I'm considering will actually work for my lifestyle and daily needs","I need the AI to point out practical issues I might not have considered (e.g., cargo space for my hobbies, winter performance in my climate)","I want to understand how different vehicles fit my specific use cases before test driving"],"best_for":["buyers with specific lifestyle constraints (families, outdoor enthusiasts, long commuters) who need practical fit assessment","first-time buyers unfamiliar with how vehicle attributes translate to real-world usability","users in challenging climates (snow, heat) who need climate-specific vehicle guidance"],"limitations":["Lifestyle profiling is conversational and subjective — users may not articulate all relevant constraints, and the AI may miss important use cases","Vehicle attribute data (cargo volume, towing capacity, ground clearance) is static and doesn't account for aftermarket modifications or accessories","Climate suitability is generalized (e.g., 'AWD is good for snow') without accounting for specific regional conditions, road maintenance, or driving skill","Cannot assess real-world reliability or failure modes specific to use cases (e.g., transmission durability under towing stress)","No integration with actual test drive availability or local dealer inventory to validate suitability before purchase"],"requires":["Vehicle attribute database with cargo capacity, seating, towing capacity, ground clearance, AWD/4WD availability, fuel economy","Natural language understanding to extract lifestyle signals from conversational input","Domain knowledge rules mapping lifestyle constraints to vehicle attributes (e.g., family size → seating/cargo, snowy climate → AWD/winter tires)"],"input_types":["lifestyle context (family size, commute distance, climate, hobbies, intended use cases)","vehicle specifications (make, model, year, trim)"],"output_types":["suitability assessment narrative (strengths and weaknesses for user's lifestyle)","practical considerations and potential issues","recommendations for vehicle features or configurations that match use cases"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_4","uri":"capability://data.processing.analysis.budget.aware.vehicle.filtering.and.affordability.analysis","name":"budget-aware vehicle filtering and affordability analysis","description":"Filters vehicle recommendations based on total cost of ownership (purchase price, insurance, fuel, maintenance) rather than just MSRP, and identifies vehicles that fit within user's budget constraints. The system likely implements a total cost of ownership (TCO) calculation that incorporates estimated insurance premiums (based on vehicle class and user profile), fuel costs (based on EPA ratings and regional fuel prices), and maintenance costs (based on manufacturer data and reliability scores). Filtering is dynamic — as users adjust budget or priorities, recommendations are re-ranked by affordability.","intents":["I want to find vehicles that fit my total budget, not just the purchase price","I need to understand the full cost of ownership including insurance, fuel, and maintenance before committing","I want to see how different budget levels affect my vehicle options and what I'm trading off"],"best_for":["budget-conscious first-time buyers unfamiliar with total cost of ownership calculations","users with tight monthly budgets who need to account for insurance and fuel costs","buyers comparing vehicles across different price points and need affordability context"],"limitations":["Insurance cost estimates are generic by vehicle class and cannot account for user age, location, driving record, or coverage preferences — actual quotes may vary significantly","Maintenance cost estimates are based on manufacturer data and historical averages, not predictive of individual vehicle reliability or regional service costs","Fuel price assumptions may be outdated or not reflect regional variations; fuel economy estimates (EPA) may not match real-world driving patterns","Depreciation curves are historical averages and cannot predict future market shifts or model-specific issues","No integration with actual financing options, interest rates, or loan terms that significantly affect affordability","Resale value predictions are not personalized by mileage, condition, or market timing"],"requires":["Vehicle specification database with MSRP, EPA fuel economy ratings, maintenance cost data","Third-party data sources for insurance estimates (by vehicle class), depreciation curves, reliability scores","Regional fuel price data or ability to accept user input","Total cost of ownership calculation engine (5-year or 10-year horizon)","Budget filtering and ranking algorithm"],"input_types":["user budget constraints (purchase price, monthly payment, total cost of ownership ceiling)","vehicle specifications (make, model, year, trim)","user location (for regional fuel and insurance cost estimates)"],"output_types":["filtered vehicle recommendations ranked by affordability","total cost of ownership breakdown (purchase, insurance, fuel, maintenance, depreciation)","monthly payment estimates (if financing is assumed)","affordability analysis comparing vehicles at different price points"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_5","uri":"capability://data.processing.analysis.reliability.and.safety.information.synthesis","name":"reliability and safety information synthesis","description":"Aggregates and synthesizes reliability ratings, safety scores, and known issues from multiple sources (NHTSA crash test ratings, IIHS ratings, JD Power reliability scores, consumer complaints) into conversational summaries. The system likely ingests structured data from third-party sources and generates natural language narratives highlighting key safety and reliability concerns (e.g., 'this model has a known transmission issue affecting 2015-2017 model years' or 'NHTSA crash test scores are above average for this class'). Synthesis is personalized by model year and trim level where data is available.","intents":["I want to know if a vehicle I'm considering has known reliability issues or safety concerns before buying","I need to understand how this vehicle's safety ratings compare to competitors in its class","I want to see consumer complaints and real-world reliability data, not just marketing claims"],"best_for":["safety-conscious buyers who prioritize crash test ratings and safety features","buyers considering used or older model years where reliability history is important","users wanting to avoid vehicles with known defects or widespread issues"],"limitations":["Reliability data is historical and may not predict future performance; newer model years lack long-term reliability data","Safety ratings are standardized tests (NHTSA, IIHS) and may not reflect real-world accident outcomes or driver behavior","Consumer complaint data is self-reported and may be skewed toward vocal complainers; absence of complaints doesn't guarantee reliability","Known issues are typically identified after vehicles have been on the road for 2-3 years; newly released models lack issue history","Recall information is critical but may not be fully integrated or up-to-date; users must verify with NHTSA directly","No personalization by driving style, maintenance habits, or regional factors that affect reliability outcomes"],"requires":["Third-party data sources: NHTSA crash test ratings, IIHS ratings, JD Power reliability scores, consumer complaint databases","Recall database integration (NHTSA or equivalent)","Natural language generation to synthesize multi-source data into coherent narratives","Data normalization to handle different rating scales and methodologies"],"input_types":["vehicle specification (make, model, year, trim)","user priorities (e.g., 'safety is most important', 'I'm concerned about reliability')"],"output_types":["safety rating summary (NHTSA, IIHS scores with context)","reliability assessment (JD Power scores, known issues, consumer complaints)","recall information (if applicable)","comparative safety/reliability positioning vs. class competitors"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_6","uri":"capability://planning.reasoning.feature.prioritization.and.trade.off.analysis","name":"feature prioritization and trade-off analysis","description":"Helps users identify which vehicle features matter most to them through conversational prioritization, then analyzes trade-offs between feature availability and cost. The system likely uses a preference elicitation approach (asking clarifying questions like 'how important is a sunroof vs. a larger cargo area?') to build a feature priority ranking. It then maps user priorities to vehicle configurations, highlighting which features are standard vs. optional, and how adding features affects price and fuel economy. Trade-off analysis is conversational, e.g., 'adding the premium audio package costs $2k but you lose 1 MPG fuel economy'.","intents":["I want to figure out which vehicle features actually matter to me before comparing options","I need to understand how optional features affect price and other attributes like fuel economy","I want to know which features are worth paying extra for vs. which I can skip"],"best_for":["buyers overwhelmed by feature lists and needing help prioritizing","users configuring vehicles with multiple trim and option packages","budget-conscious buyers trying to balance features with affordability"],"limitations":["Feature prioritization is conversational and subjective — users may not accurately assess their own preferences until they experience features in person","Feature availability varies significantly by model year, trim level, and regional market; data may not reflect all configurations","Price impacts of optional features are static and don't account for dealer-specific discounts, bundle pricing, or regional variations","Feature descriptions are text-based and cannot convey subjective experience (e.g., how a sunroof 'feels' or seat comfort)","No integration with actual dealer inventory to validate feature availability in user's market","Fuel economy impacts of features (e.g., roof racks, larger wheels) are estimated and may not match real-world driving"],"requires":["Vehicle feature database with standard vs. optional features by trim level and model year","Feature pricing data (cost of optional packages and individual features)","Feature impact data (e.g., how features affect fuel economy, weight, performance)","Natural language understanding to extract feature preferences from conversation","Preference elicitation algorithm to prioritize features through dialogue"],"input_types":["conversational feature preferences (e.g., 'I like sunroofs', 'cargo space is important')","vehicle specification (make, model, year, trim)","budget constraints"],"output_types":["prioritized feature list (ranked by user importance)","feature availability by trim level and configuration","trade-off analysis (feature cost vs. impact on price, fuel economy, etc.)","recommended configurations matching user priorities and budget"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_7","uri":"capability://memory.knowledge.session.based.conversation.state.management.with.context.retention","name":"session-based conversation state management with context retention","description":"Maintains conversation context across multiple turns, allowing users to reference previous statements, ask follow-up questions, and refine recommendations without re-stating preferences. The system likely implements a conversation state machine that tracks user preferences, vehicle comparisons, and previous recommendations within a session. Context is used to interpret ambiguous references (e.g., 'what about that blue one?' referring to a previously mentioned vehicle) and to accumulate preference signals across turns. State is session-scoped and likely not persisted across sessions unless explicitly saved.","intents":["I want to have a natural back-and-forth conversation without repeating my preferences each time","I want to ask follow-up questions and have the AI remember what we discussed earlier","I want to refine my recommendations iteratively through dialogue without starting over"],"best_for":["users preferring conversational interaction over form-based input","buyers exploring options iteratively and refining preferences through dialogue","users who want to ask clarifying questions without restating context"],"limitations":["Conversation context is session-scoped and not persisted — users cannot resume conversations across sessions or devices","No explicit user profile or account system visible — preferences are not saved for future reference","Context window is likely limited by underlying LLM constraints — very long conversations may lose early context","Ambiguous references may be misinterpreted, especially if multiple vehicles have been discussed","No explicit confirmation mechanism to ensure the AI correctly understood user preferences — misunderstandings may compound across turns"],"requires":["Conversational AI model with multi-turn dialogue capability","Session state management (in-memory or short-term storage)","Natural language understanding to track entities (vehicles, preferences) across turns","Coreference resolution to interpret references to previously mentioned vehicles or preferences"],"input_types":["natural language text (chat messages)","implicit references to previous context (e.g., 'that one', 'the blue car')"],"output_types":["conversational responses that reference previous context","refined recommendations based on accumulated preferences","clarifying questions when context is ambiguous"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_8","uri":"capability://search.retrieval.vehicle.inventory.search.and.availability.checking","name":"vehicle inventory search and availability checking","description":"Searches for vehicles matching user preferences across available inventory and provides availability information. The system likely integrates with dealer inventory databases or third-party inventory aggregators (e.g., Autotrader, Cars.com) to retrieve real-time or near-real-time inventory data. Search is parameterized by user preferences (make, model, year, trim, features, price range, location) and results are ranked by relevance to user priorities. Availability information includes location, pricing, and dealer contact information.","intents":["I want to find vehicles matching my preferences that are actually available in my area","I want to know where to find a specific vehicle and how much dealers are asking for it","I want to compare availability and pricing across multiple dealers in my region"],"best_for":["buyers ready to move from research to shopping and need to find actual vehicles","users in competitive markets wanting to compare availability and pricing across dealers","buyers with specific feature or configuration requirements needing to locate matching inventory"],"limitations":["Inventory data freshness is critical but undisclosed — listings may be outdated, sold vehicles may still appear, or new inventory may not be indexed","Pricing information from inventory listings may not reflect actual dealer pricing after negotiation or incentives","Inventory coverage is likely incomplete — not all dealers may be included in the database, especially small independent dealers","No integration with dealer-specific incentives, financing offers, or trade-in valuations that affect actual purchase cost","Search results may be biased by data source (e.g., if using Autotrader, results are limited to Autotrader listings)","No ability to facilitate test drives or reserve vehicles — users must contact dealers directly"],"requires":["Integration with inventory data sources (dealer inventory databases, Autotrader, Cars.com, or equivalent)","Real-time or near-real-time inventory data with pricing and location information","Search algorithm to match user preferences to inventory","Geolocation capability or user input to filter by region","Dealer contact information (phone, address, website)"],"input_types":["vehicle preferences (make, model, year, trim, features, price range)","user location or search radius"],"output_types":["ranked list of available vehicles matching preferences","vehicle details (price, mileage, features, location)","dealer information (name, location, contact details)","availability summary (e.g., '5 vehicles found within 50 miles')"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_autoeasy__cap_9","uri":"capability://text.generation.language.dealer.negotiation.preparation.with.talking.points","name":"dealer negotiation preparation with talking points","description":"Generates personalized negotiation scripts and talking points based on market data, vehicle-specific factors, and user's target price. The system likely combines market pricing data, typical dealer markups, vehicle-specific incentives, and user's stated budget to generate specific phrases and negotiation strategies. Talking points are contextualized by vehicle type, market conditions, and seasonal factors (e.g., 'end of quarter is a good time to negotiate because dealers have sales quotas'). Scripts are conversational and designed to be used verbatim or adapted by users during dealer interactions.","intents":["I want specific phrases and talking points to use when negotiating with dealers","I want to understand what price I should target and how to justify it to a dealer","I want negotiation strategies tailored to the specific vehicle and market I'm in"],"best_for":["first-time negotiators lacking confidence in dealer interactions","users wanting to reduce information asymmetry and avoid high-pressure sales tactics","buyers in competitive markets where negotiation leverage varies by season or inventory levels"],"limitations":["Negotiation effectiveness depends on user confidence and communication skills — scripts alone cannot overcome poor execution or dealer resistance","Market data used to generate talking points may be outdated or not reflect current local conditions","Dealer-specific factors (inventory pressure, sales quotas, individual salesperson incentives) are not accounted for","Scripts are generic and may not adapt to unexpected dealer responses or objections","No real-time feedback during actual negotiations — users cannot ask for help mid-conversation","Negotiation tactics may be ineffective or counterproductive in some markets or with certain dealer types"],"requires":["Market pricing data and typical dealer markups","Vehicle-specific incentive and rebate information","Seasonal and market trend data to contextualize negotiation timing","Natural language generation to create conversational scripts and talking points","User's target price and vehicle preferences"],"input_types":["vehicle specification (make, model, year, trim)","user's target price or budget","user location (for regional market context)","user's negotiation experience level (optional)"],"output_types":["personalized negotiation script with specific phrases","talking points addressing common dealer objections","pricing justification (e.g., 'this vehicle is priced 8% above market average')","timing and strategy advice (e.g., 'negotiate at end of month for better leverage')"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Access to vehicle specification database (make, model, year, trim, MSRP, key features)","Natural language processing model capable of intent classification and entity extraction","Chat interface with multi-turn conversation state management","Historical pricing database with MSRP, typical selling prices, and regional variations","Market trend data (seasonal demand patterns, inventory levels, incentive patterns)","Ability to geolocate user or accept region input to contextualize advice","Natural language generation for converting data insights into conversational negotiation tips","Comprehensive vehicle specification database (MSRP, fuel economy, safety ratings, feature lists, dimensions)","Third-party data sources for insurance estimates, reliability scores (JD Power, Consumer Reports equivalents), depreciation data","Multi-criteria decision analysis algorithm to weight and score attributes"],"failure_modes":["Recommendations depend on underlying vehicle database freshness — unclear if inventory reflects real-time dealer stock or historical data","Cannot account for local market variations in pricing, availability, or regional vehicle preferences","May oversimplify complex reliability factors, insurance cost variations by model year, or depreciation curves that require domain expertise","Conversational context is likely session-scoped with no persistent user profile across sessions unless explicitly saved","Pricing data freshness is critical but undisclosed — advice based on stale market data could be counterproductive","Regional pricing variations are complex (dealer density, local competition, regional preferences) and may not be fully captured","Cannot account for individual dealer-specific incentives, inventory pressure, or salesperson commission structures that affect negotiation dynamics","Negotiation effectiveness depends on user confidence and communication skills — AI guidance alone cannot overcome poor execution","No real-time inventory visibility means recommendations may reference vehicles not actually available locally","Comparison quality depends on data completeness — missing or outdated safety ratings, insurance cost estimates, or reliability scores reduce accuracy","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":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=autoeasy","compare_url":"https://unfragile.ai/compare?artifact=autoeasy"}},"signature":"X6OmV5KUh0Wbvc7SPpVjK/KXWPE0yZE6kczSMDmrfKAIF3c4ivtI5tGRcBNRiOEQnl9U/p9AWWsAFO80xUEoDg==","signedAt":"2026-06-23T13:48:26.975Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/autoeasy","artifact":"https://unfragile.ai/autoeasy","verify":"https://unfragile.ai/api/v1/verify?slug=autoeasy","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"}}