{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-amazon-q","slug":"amazon-q","name":"Amazon Q","type":"product","url":"https://aws.amazon.com/q/","page_url":"https://unfragile.ai/amazon-q","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-amazon-q__cap_0","uri":"capability://code.generation.editing.aws.context.aware.code.generation.and.completion","name":"aws-context-aware code generation and completion","description":"Generates code snippets, functions, and multi-file implementations with awareness of AWS service APIs, SDKs, and best practices. Integrates with IDE environments to analyze local codebase context and suggest completions that align with existing code patterns, AWS resource configurations, and deployment targets. Uses retrieval of AWS documentation and service-specific examples to ground suggestions in current AWS APIs.","intents":["I need to write a Lambda function that integrates with DynamoDB and S3 without looking up the SDK syntax","Generate boilerplate code for an AWS service I've never used before","Complete this code snippet in a way that follows AWS best practices for this use case","Refactor my existing code to use AWS services more efficiently"],"best_for":["AWS developers building serverless applications","Teams standardizing on AWS infrastructure","Developers new to specific AWS services wanting faster onboarding"],"limitations":["Context window size unknown — may struggle with very large codebases or complex multi-service architectures","Accuracy of AWS API suggestions depends on training data freshness — newer AWS service features may not be reflected","No explicit guarantee on generated code security or compliance with organizational policies","IDE integration scope unknown — may not support all development environments equally"],"requires":["AWS account (for accessing Amazon Q service)","Supported IDE or AWS console access","Code context available in IDE or uploaded to Amazon Q"],"input_types":["natural language description of desired functionality","partial code snippets","AWS service names or resource types","code context from open files"],"output_types":["code snippets in multiple languages (Python, JavaScript, Java, Go, etc.)","complete function implementations","multi-file code structures","code explanations"],"categories":["code-generation-editing","aws-native"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_1","uri":"capability://code.generation.editing.automated.test.case.generation.from.code.context","name":"automated test case generation from code context","description":"Analyzes code functions, classes, and modules to automatically generate unit test cases, integration tests, and edge case scenarios. Understands code logic flow and dependencies to propose test cases covering normal paths, error conditions, and boundary cases. Integrates with testing frameworks (pytest, Jest, JUnit, etc.) to generate tests in the appropriate syntax for the detected language.","intents":["Generate unit tests for this function so I don't have to write them manually","Create test cases that cover edge cases I might have missed","Generate integration tests for this Lambda function and its dependencies","Ensure my code has adequate test coverage before deployment"],"best_for":["Teams under time pressure to increase test coverage quickly","Developers new to testing practices wanting scaffolding","CI/CD pipelines requiring automated test generation as part of code review"],"limitations":["Test quality depends on code clarity — poorly documented or overly complex code may generate weak tests","Cannot understand business logic intent from code alone — may miss domain-specific edge cases","Generated tests may have false positives or miss actual bugs if code has subtle logic errors","No guarantee that generated tests align with organizational testing standards or patterns"],"requires":["Code written in supported language (Python, JavaScript, Java, Go, etc.)","Testing framework installed in project (pytest, Jest, JUnit, etc.)","AWS account with Amazon Q Developer access"],"input_types":["source code files or code snippets","function/method signatures","code context from IDE"],"output_types":["test code in framework-specific syntax","test case descriptions","coverage reports (estimated)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_10","uri":"capability://text.generation.language.contact.center.agent.assistance.and.customer.support.automation","name":"contact center agent assistance and customer support automation","description":"Provides real-time assistance to contact center agents during customer interactions via Amazon Connect. Suggests responses, retrieves relevant knowledge base articles, and provides context about customer history and issues. Can handle simple customer inquiries autonomously or escalate to human agents when needed. Integrates with CRM and ticketing systems to provide unified customer context.","intents":["Help my support agents respond faster by suggesting answers during calls","Automatically handle simple customer inquiries without human intervention","Provide agents with relevant knowledge base articles during conversations","Route complex issues to appropriate specialists based on customer context"],"best_for":["Contact centers and customer support teams","Companies wanting to reduce average handle time (AHT)","Organizations automating simple customer inquiries"],"limitations":["Scope of agent assistance features not fully documented","Autonomous handling capability unclear — may be limited to very simple inquiries","Customer satisfaction impact unknown — may frustrate customers if suggestions are irrelevant","Integration with CRM systems not fully specified","Escalation logic and criteria unknown","No mention of multi-language support or regional compliance"],"requires":["AWS account with Amazon Connect access","Contact center infrastructure set up in Connect","Knowledge base or documentation source","CRM or ticketing system integration (optional)"],"input_types":["customer inquiries (text or speech)","customer history and context","knowledge base articles","previous interaction records"],"output_types":["suggested agent responses","relevant knowledge base articles","customer context summaries","escalation recommendations","automated responses for simple inquiries"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_11","uri":"capability://data.processing.analysis.supply.chain.visibility.and.optimization.recommendations","name":"supply chain visibility and optimization recommendations","description":"Analyzes supply chain data to provide visibility into inventory, shipments, and supplier performance. Identifies bottlenecks, predicts disruptions, and suggests optimization actions (e.g., reorder points, supplier diversification). Integrates with supply chain systems to provide real-time insights and automated alerts.","intents":["Identify bottlenecks in our supply chain that are causing delays","Predict potential supply disruptions before they impact operations","Optimize inventory levels to reduce carrying costs while maintaining service levels","Find alternative suppliers for critical components"],"best_for":["Supply chain managers and procurement teams","Operations teams managing inventory and logistics","Companies with complex multi-tier supply chains"],"limitations":["Supply chain optimization features not fully documented in provided materials","Prediction accuracy depends on historical data quality and completeness","External factors (geopolitical events, natural disasters) may not be accounted for","Supplier recommendations may not account for relationship history or contract terms","Real-time integration with supply chain systems unclear"],"requires":["AWS account with Amazon Q access","Supply chain data sources (ERP, WMS, supplier systems)","Historical data for trend analysis"],"input_types":["inventory data","shipment tracking information","supplier performance metrics","demand forecasts","cost data"],"output_types":["supply chain bottleneck analysis","disruption predictions","optimization recommendations","inventory reorder suggestions","supplier performance reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_12","uri":"capability://code.generation.editing.codebase.aware.refactoring.and.code.quality.improvements","name":"codebase-aware refactoring and code quality improvements","description":"Analyzes entire codebases to identify code quality issues, anti-patterns, and refactoring opportunities. Understands code structure and dependencies to suggest safe refactorings that maintain functionality. Generates refactored code that improves readability, performance, and maintainability. Integrates with version control to track changes and enable gradual rollout.","intents":["Identify code quality issues across our entire codebase","Refactor this legacy code to modern patterns without breaking functionality","Reduce code duplication and improve maintainability","Improve code performance by identifying inefficient patterns"],"best_for":["Development teams managing large codebases","Teams paying down technical debt","Code review processes wanting automated quality checks"],"limitations":["Refactoring suggestions may not account for implicit dependencies or side effects","Performance improvements are heuristic-based — actual impact depends on runtime characteristics","Large codebases may exceed analysis capacity or timeout","No guarantee that refactored code maintains exact behavioral equivalence","Integration with version control and CI/CD not fully specified"],"requires":["Source code in supported language","Codebase size within analysis limits (unknown)","Amazon Q Developer access"],"input_types":["source code files","code metrics and quality reports","performance profiling data","test coverage data"],"output_types":["code quality reports","refactoring suggestions with priority","refactored code samples","performance improvement estimates","test coverage impact analysis"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_2","uri":"capability://safety.moderation.security.vulnerability.scanning.and.automated.remediation","name":"security vulnerability scanning and automated remediation","description":"Scans code for security vulnerabilities including OWASP Top 10, AWS IAM misconfigurations, hardcoded secrets, dependency vulnerabilities, and insecure API usage patterns. Provides detailed explanations of each vulnerability and generates code fixes that remediate the issue while maintaining functionality. Integrates with CI/CD pipelines to block deployments with critical vulnerabilities.","intents":["Find security vulnerabilities in my code before it reaches production","Automatically fix common security issues like hardcoded credentials or SQL injection risks","Ensure my AWS IAM policies follow least-privilege principles","Scan dependencies for known vulnerabilities and get upgrade recommendations"],"best_for":["DevSecOps teams automating security scanning in CI/CD","Developers building AWS applications who need compliance (SOC 2, PCI-DSS, HIPAA)","Teams lacking dedicated security engineers wanting automated guardrails"],"limitations":["Scope of scanning unknown — documentation mentions 'security scanning and fixes' but doesn't specify SAST vs DAST vs SCA coverage","May not detect logic-based vulnerabilities or business logic flaws that require domain understanding","False positive rate unknown — may flag secure patterns as vulnerable if they don't match expected patterns","Remediation suggestions may not align with organizational security policies or architecture constraints","No mention of real-time threat intelligence integration or zero-day vulnerability detection"],"requires":["Code in supported language","AWS account with Amazon Q Developer access","CI/CD pipeline integration (optional but recommended)"],"input_types":["source code files","dependency manifests (package.json, requirements.txt, pom.xml, etc.)","CloudFormation/IaC templates","AWS IAM policies"],"output_types":["vulnerability reports with severity levels","remediated code snippets","security explanations and best practices","compliance mapping (OWASP, CWE, etc.)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_3","uri":"capability://planning.reasoning.application.modernization.guidance.and.code.transformation","name":"application modernization guidance and code transformation","description":"Analyzes legacy or monolithic applications and provides step-by-step guidance for modernizing them to cloud-native architectures. Suggests refactoring patterns (e.g., monolith-to-microservices, on-premises-to-serverless), generates code transformations, and identifies AWS services that can replace legacy components. Provides cost-benefit analysis and migration roadmaps.","intents":["I have a legacy Java monolith — what's the best way to migrate it to AWS?","Refactor this on-premises application to use serverless AWS services","Break down this monolithic codebase into microservices with guidance","Understand which AWS services can replace our existing infrastructure"],"best_for":["Enterprise teams planning cloud migrations","Architects evaluating modernization strategies","Development teams executing multi-phase migration projects"],"limitations":["Guidance quality depends on code analysis depth — very large or undocumented codebases may receive generic recommendations","Cannot account for organizational constraints (legacy vendor dependencies, compliance requirements, team skills) without explicit context","Refactoring suggestions may require significant manual effort to implement despite AI guidance","No real-time cost estimation — recommendations may not reflect current AWS pricing or organizational discount structures","Risk assessment for breaking changes unknown — may not adequately warn about migration complexity"],"requires":["Access to source code or architecture documentation","AWS account for cost estimation and service exploration","Amazon Q Developer access"],"input_types":["source code in legacy languages (COBOL, Fortran, older Java/C#, etc.)","architecture diagrams or documentation","infrastructure-as-code templates","dependency manifests","performance metrics and logs"],"output_types":["modernization roadmaps with phases","refactored code samples","AWS service recommendations","cost-benefit analysis","migration risk assessments"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_4","uri":"capability://data.processing.analysis.aws.resource.optimization.and.cost.reduction.recommendations","name":"aws resource optimization and cost reduction recommendations","description":"Analyzes AWS infrastructure (EC2 instances, RDS databases, Lambda functions, storage, etc.) and identifies optimization opportunities to reduce costs and improve performance. Suggests right-sizing instances, switching to more cost-effective services, identifying unused resources, and optimizing data transfer patterns. Provides estimated cost savings and implementation complexity for each recommendation.","intents":["Find ways to reduce our AWS bill without sacrificing performance","Identify oversized or underutilized resources in our infrastructure","Get recommendations on switching to more cost-effective AWS services","Understand the trade-offs between cost and performance for our workloads"],"best_for":["FinOps teams managing AWS cost optimization","DevOps engineers looking to reduce infrastructure costs","Startups and small teams with limited budgets"],"limitations":["Recommendations based on historical metrics — may not account for seasonal traffic spikes or planned growth","Cost savings estimates are approximations — actual savings depend on implementation and usage patterns","Cannot recommend organizational changes (e.g., Reserved Instances, Savings Plans) without access to usage history and commitment tolerance","Performance impact of optimizations unknown — may suggest changes that degrade user experience if not carefully validated","No real-time integration with AWS Cost Explorer or billing data — recommendations may be based on stale information"],"requires":["AWS account with CloudWatch metrics and billing data accessible","Amazon Q Developer access","Sufficient historical data (typically 2+ weeks) for accurate analysis"],"input_types":["AWS CloudWatch metrics","AWS billing and cost data","infrastructure-as-code templates","application performance metrics","traffic patterns and usage data"],"output_types":["optimization recommendations with priority","estimated cost savings (monthly/annual)","implementation complexity assessment","code/configuration changes needed","performance impact analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_5","uri":"capability://code.generation.editing.data.engineering.pipeline.generation.and.optimization","name":"data engineering pipeline generation and optimization","description":"Generates data engineering pipelines for ETL/ELT workflows using AWS services (Glue, Lambda, Step Functions, Kinesis, etc.). Analyzes data requirements and suggests appropriate services and patterns. Generates code for data transformation, validation, and orchestration. Optimizes pipelines for cost, latency, and scalability.","intents":["Generate a data pipeline that ingests data from multiple sources and loads it into a data warehouse","Create an ETL job using AWS Glue that transforms and cleans data","Build a real-time data streaming pipeline using Kinesis and Lambda","Optimize my existing data pipeline for cost and performance"],"best_for":["Data engineers building AWS data platforms","Analytics teams automating data workflows","Data scientists needing reproducible data pipelines"],"limitations":["Generated pipelines may not handle all edge cases or data quality issues without manual refinement","Scalability assumptions unknown — recommendations may not account for future data volume growth","No mention of data governance or lineage tracking capabilities","Error handling and retry logic in generated pipelines may be generic rather than optimized for specific failure modes","Data validation rules must be explicitly specified — cannot infer business rules from data alone"],"requires":["AWS account with Glue, Lambda, or Step Functions access","Data source specifications (schema, format, location)","Amazon Q Developer access"],"input_types":["data source specifications (CSV, JSON, Parquet, databases, APIs)","target data warehouse schema","transformation requirements in natural language","data quality rules"],"output_types":["AWS Glue job code (PySpark)","Lambda function code for serverless ETL","Step Functions state machines for orchestration","data validation and quality checks","cost and performance estimates"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_6","uri":"capability://code.generation.editing.ai.ml.application.scaffolding.and.model.integration","name":"ai/ml application scaffolding and model integration","description":"Generates boilerplate code and architecture guidance for building AI/ML applications on AWS. Suggests appropriate services (SageMaker, Bedrock, Lambda, etc.), generates code for model training, inference, and deployment. Handles model versioning, A/B testing setup, and monitoring integration. Supports multiple ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.).","intents":["Set up a machine learning pipeline on AWS from data preparation to model deployment","Generate code to train and deploy a model using SageMaker","Integrate a pre-trained model into my application using Amazon Bedrock","Build an ML inference API with auto-scaling and monitoring"],"best_for":["Data scientists building production ML systems on AWS","ML engineers setting up model training and deployment infrastructure","Teams integrating foundation models into applications"],"limitations":["Generated code assumes standard ML workflows — may not handle specialized use cases (e.g., federated learning, edge ML)","Model selection and hyperparameter tuning guidance is generic — requires domain expertise to validate","No mention of model explainability or bias detection capabilities","Cost estimation for training and inference may be inaccurate without actual workload profiling","Data privacy and compliance considerations must be manually addressed"],"requires":["AWS account with SageMaker or Bedrock access","Training data in supported formats","ML framework knowledge (TensorFlow, PyTorch, etc.)","Amazon Q Developer access"],"input_types":["training data specifications","model type and framework preferences","inference requirements (latency, throughput)","existing model artifacts or code"],"output_types":["SageMaker training job code","model deployment configurations","inference endpoint code","monitoring and logging setup","A/B testing and canary deployment templates"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_7","uri":"capability://planning.reasoning.troubleshooting.and.debugging.assistance.with.aws.context","name":"troubleshooting and debugging assistance with aws context","description":"Analyzes error messages, logs, and code to diagnose issues in AWS applications. Provides root cause analysis, suggests fixes, and explains why problems occurred. Integrates with CloudWatch logs, X-Ray traces, and application logs to correlate errors across services. Generates debugging code and monitoring improvements.","intents":["My Lambda function is timing out — help me understand why and fix it","Analyze these CloudWatch logs to find the root cause of the error","This API is returning 500 errors intermittently — what could be wrong?","Help me debug this distributed system issue across multiple AWS services"],"best_for":["DevOps and SRE teams managing AWS infrastructure","Developers debugging production issues in AWS applications","On-call engineers needing rapid incident diagnosis"],"limitations":["Diagnosis accuracy depends on log completeness and detail — sparse logs may lead to incorrect conclusions","Cannot access live AWS resources directly — requires logs and metrics to be provided","May suggest fixes that don't address root cause if symptoms are misleading","Performance debugging requires detailed metrics — generic recommendations may not solve specific bottlenecks","Security issues in logs may be exposed to Amazon Q — requires careful handling of sensitive data"],"requires":["Error messages, stack traces, or logs from the application","CloudWatch logs or X-Ray traces (optional but helpful)","Code context for the failing component","Amazon Q Developer access"],"input_types":["error messages and stack traces","application logs","CloudWatch logs and metrics","X-Ray traces","source code snippets","AWS service configuration"],"output_types":["root cause analysis","suggested fixes with code examples","monitoring and alerting recommendations","debugging steps and commands","prevention strategies"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_8","uri":"capability://search.retrieval.natural.language.q.a.against.enterprise.data.and.systems","name":"natural language q&a against enterprise data and systems","description":"Enables non-technical users to ask natural language questions against company data, documents, and connected systems (Salesforce, ServiceNow, Jira, etc.). Retrieves relevant information from multiple sources, synthesizes answers, and can execute actions on user's behalf (e.g., create tickets, update records). Uses semantic search and context understanding to find relevant information even with imprecise queries.","intents":["What's the status of this customer's account without logging into Salesforce?","Find all open support tickets related to this issue across our systems","Create a new Jira ticket with details from this customer conversation","Summarize the latest updates on this project from all our connected tools"],"best_for":["Non-technical business users (sales, support, operations) needing data access","Customer support teams needing rapid access to customer information","Executives wanting quick insights without IT involvement"],"limitations":["Connected systems and data sources not fully documented — unclear which integrations are supported","Action execution mechanism unknown — scope of 'execute actions on user's behalf' is unclear and may have security implications","Answer accuracy depends on data quality in connected systems — garbage in, garbage out","No mention of access control or data governance — unclear how sensitive data is protected","Hallucination risk unknown — may generate plausible-sounding but incorrect answers if data is ambiguous","Real-time data freshness unknown — may provide stale information if data sources aren't synchronized"],"requires":["AWS account with Amazon Q Business access","Connected data sources (Salesforce, ServiceNow, Jira, etc.) with API access","User authentication and authorization setup","Data indexing and synchronization configured"],"input_types":["natural language questions","follow-up clarifications","action requests (create, update, delete)"],"output_types":["natural language answers","synthesized information from multiple sources","action confirmations","source citations and confidence scores"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-amazon-q__cap_9","uri":"capability://image.visual.interactive.bi.dashboard.and.visualization.generation","name":"interactive bi dashboard and visualization generation","description":"Generates business intelligence dashboards and visualizations in Amazon QuickSight from natural language descriptions. Understands data relationships and automatically creates appropriate chart types, calculations, and multi-visual layouts. Supports complex aggregations, time-series analysis, and trend discovery. Enables non-technical users to create executive dashboards without SQL or data modeling knowledge.","intents":["Create a dashboard showing sales trends by region and product without writing SQL","Generate a visualization of customer churn rates with drill-down capability","Build an executive summary dashboard with KPIs and trend analysis","Create a real-time monitoring dashboard for operational metrics"],"best_for":["Business analysts and non-technical users creating dashboards","Executives needing self-service BI without IT dependency","Teams needing rapid dashboard prototyping"],"limitations":["Dashboard quality depends on data quality and schema clarity — poorly structured data may result in suboptimal visualizations","Complex business logic or custom calculations may require manual refinement","Performance with very large datasets unknown — may struggle with billions of rows","Visualization suggestions are heuristic-based — may not match user's exact intent without iteration","No mention of real-time data refresh rates or latency guarantees"],"requires":["AWS account with Amazon QuickSight access","Data source connected to QuickSight (S3, RDS, Redshift, etc.)","Data schema understanding or documentation"],"input_types":["natural language dashboard descriptions","data source specifications","metric and dimension definitions","time period and filtering requirements"],"output_types":["QuickSight dashboards","visualizations (bar charts, line charts, scatter plots, heatmaps, etc.)","calculated fields and aggregations","drill-down and filtering configurations","executive summaries and insights"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["AWS account (for accessing Amazon Q service)","Supported IDE or AWS console access","Code context available in IDE or uploaded to Amazon Q","Code written in supported language (Python, JavaScript, Java, Go, etc.)","Testing framework installed in project (pytest, Jest, JUnit, etc.)","AWS account with Amazon Q Developer access","AWS account with Amazon Connect access","Contact center infrastructure set up in Connect","Knowledge base or documentation source","CRM or ticketing system integration (optional)"],"failure_modes":["Context window size unknown — may struggle with very large codebases or complex multi-service architectures","Accuracy of AWS API suggestions depends on training data freshness — newer AWS service features may not be reflected","No explicit guarantee on generated code security or compliance with organizational policies","IDE integration scope unknown — may not support all development environments equally","Test quality depends on code clarity — poorly documented or overly complex code may generate weak tests","Cannot understand business logic intent from code alone — may miss domain-specific edge cases","Generated tests may have false positives or miss actual bugs if code has subtle logic errors","No guarantee that generated tests align with organizational testing standards or patterns","Scope of agent assistance features not fully documented","Autonomous handling capability unclear — may be limited to very simple inquiries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.25,"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":"2026-06-17T09:51:02.370Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=amazon-q","compare_url":"https://unfragile.ai/compare?artifact=amazon-q"}},"signature":"Pwp6tc0rMR9nbGxDwf20vP16p+H7UHX7rllP0E6boa1YcCVkjVpt3bSyxjsH1v/GFCjMux1BCc46QE2kEs75Dw==","signedAt":"2026-06-20T06:38:16.677Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/amazon-q","artifact":"https://unfragile.ai/amazon-q","verify":"https://unfragile.ai/api/v1/verify?slug=amazon-q","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"}}