{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-appland-appmap","slug":"appmap","name":"AppMap","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=appland.appmap","page_url":"https://unfragile.ai/appmap","categories":["code-editors"],"tags":["ai","anthropic","architecture","autocomplete","chatgpt","claude","code analysis","code completion","code explanation","code recommendation","coding assistant","dependency","diagram","diff","documentation","gpt","java","javascript","llm","map","mistral","mixtral","monitoring","node","node.js","observability","ollama","openai","python","refactor","ruby","security","snippets","software design","technical debt","testing","tracing","typescript","uml","visualization"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-appland-appmap__cap_0","uri":"capability://data.processing.analysis.runtime.execution.trace.capture.and.visualization","name":"runtime-execution-trace-capture-and-visualization","description":"Captures real-time execution traces of running code including HTTP calls, SQL queries, exceptions, I/O operations, and data flow, then visualizes this data as sequence diagrams, flame graphs, dependency maps, and trace views. Works by instrumenting code execution within the VS Code environment without requiring code modifications, storing AppMap data snapshots that feed into AI analysis. The extension integrates with the debugger/test runner to passively record application behavior during development and testing sessions.","intents":["I want to understand what my code actually does at runtime without adding logging statements","I need to visualize the flow of data and function calls to debug a complex issue","I want to see all HTTP requests and database queries my application makes during a test run","I need to identify performance bottlenecks by analyzing execution flame graphs"],"best_for":["developers debugging complex multi-layer applications","teams performing code reviews with runtime context","developers building microservices who need to understand inter-service communication"],"limitations":["Recording must be explicitly triggered or requested via chat — not automatic for all code execution","Only captures traces from code executed within VS Code environment; cannot trace production systems or external processes","Runtime overhead of execution tracing not quantified in documentation; may impact performance of traced applications","Trace capture scope limited to development/testing sessions; no persistent historical trace storage documented"],"requires":["VS Code (minimum version unknown)","Application code must be executable within VS Code environment","For language support: Java, JavaScript, Node.js, Python, Ruby, or TypeScript"],"input_types":["running application code","test execution","HTTP requests","database queries"],"output_types":["sequence diagrams (Mermaid format)","flame graphs","dependency maps","trace views","structured execution data"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_1","uri":"capability://text.generation.language.context.aware.code.explanation.with.runtime.data","name":"context-aware-code-explanation-with-runtime-data","description":"Provides AI-generated explanations of code behavior by combining static code analysis with captured runtime execution traces. Activated via the `@explain` chat mode, this capability uses the Navie AI assistant (after authentication) to answer questions about what code does, why it behaves a certain way, and what data flows through it. The AI synthesizes information from the current file, project scope, git branch context, and recorded AppMap execution data to generate contextually accurate explanations.","intents":["I need to understand what this unfamiliar code does without reading every line","I want to know why my code is behaving unexpectedly based on actual runtime behavior","I need to explain a complex function to a team member quickly","I want to understand the data flow through my application during a specific operation"],"best_for":["developers onboarding to unfamiliar codebases","teams conducting code reviews and knowledge sharing","developers debugging issues by understanding actual vs expected behavior"],"limitations":["Requires authentication (email, GitHub, or GitLab) to enable Navie AI assistant","Explanation quality depends on availability of runtime traces; static analysis alone may be insufficient for complex behavior","No offline mode documented; requires connectivity to LLM provider (AppMap endpoint, OpenAI, Anthropic, etc.)","Explanation accuracy limited by the underlying LLM's training data and reasoning capabilities"],"requires":["VS Code AppMap extension installed","User authentication (email, GitHub, or GitLab account)","API key for selected LLM provider (OpenAI, Anthropic, Google Gemini, GitHub Copilot, or AppMap's built-in endpoint)","Optionally: recorded AppMap execution traces for runtime context"],"input_types":["source code (current file or project)","natural language questions","execution traces (optional)"],"output_types":["natural language explanations","code snippets","references to specific functions or files"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_10","uri":"capability://safety.moderation.security.vulnerability.detection.in.code.analysis","name":"security-vulnerability-detection-in-code-analysis","description":"Identifies security vulnerabilities and issues in code through the `@review` mode and general code analysis, leveraging the LLM's security knowledge combined with codebase context. The AI analyzes code patterns, dependencies, and data flows to detect common vulnerabilities such as injection attacks, insecure authentication, exposed credentials, and unsafe data handling. Results are presented as actionable security findings with context about where issues occur in the codebase.","intents":["I want to identify security vulnerabilities in my code before deploying","I need to check if my code changes introduce security risks","I want to ensure my authentication and data handling are secure","I need to find exposed credentials or insecure configurations in my codebase"],"best_for":["security-conscious development teams","developers implementing security-critical features","teams conducting security reviews before deployment"],"limitations":["Security detection depends on LLM's training data and reasoning; may miss novel or complex vulnerabilities","No integration with dedicated security scanning tools (SAST, DAST); provides AI-based analysis only","Cannot detect runtime security issues without execution traces; static analysis alone is limited","No documented support for security standards or compliance frameworks (OWASP, CWE, etc.)","Results are suggestions from the LLM; not authoritative security assessments"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider","Source code context available in the workspace"],"input_types":["source code","code changes (diffs)","execution traces (optional)"],"output_types":["security findings","vulnerability descriptions","remediation suggestions","code references"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_11","uri":"capability://data.processing.analysis.performance.bottleneck.identification.via.execution.analysis","name":"performance-bottleneck-identification-via-execution-analysis","description":"Identifies performance bottlenecks and optimization opportunities by analyzing recorded execution traces (flame graphs, execution timings) combined with code analysis. The AI examines where code spends the most time, identifies inefficient patterns, and suggests optimizations. This capability is enhanced when AppMap execution traces are available, providing concrete data about actual performance characteristics rather than theoretical analysis.","intents":["I want to find the slowest parts of my application","I need to identify inefficient database queries or API calls","I want to understand why my application is slow based on actual execution data","I need suggestions for optimizing specific performance bottlenecks"],"best_for":["developers optimizing application performance","teams investigating performance regressions","developers working on latency-sensitive applications"],"limitations":["Performance analysis without execution traces is limited to static code patterns; requires AppMap data for concrete insights","Optimization suggestions depend on LLM's knowledge of performance best practices; may not be optimal for specific use cases","No integration with profiling tools or APM platforms; analysis is based on AppMap traces only","Cannot detect performance issues in external systems (databases, APIs) beyond what is captured in traces"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider","Recorded AppMap execution traces with timing data (flame graphs)"],"input_types":["execution traces (flame graphs, timings)","source code","application metrics"],"output_types":["performance bottleneck identification","optimization suggestions","code references to slow operations"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_12","uri":"capability://code.generation.editing.maintainability.and.technical.debt.assessment","name":"maintainability-and-technical-debt-assessment","description":"Evaluates code maintainability and identifies technical debt through code analysis and review workflows. The AI examines code complexity, duplication, adherence to design patterns, test coverage, and documentation completeness to assess maintainability. Technical debt is identified through patterns like overly complex functions, missing abstractions, inconsistent naming, and insufficient testing. Results are presented with specific recommendations for improvement.","intents":["I want to assess the maintainability of my codebase","I need to identify technical debt in my project","I want to know if my code is too complex or needs refactoring","I need recommendations for improving code quality and maintainability"],"best_for":["teams managing long-lived codebases","developers planning refactoring efforts","teams assessing code quality before major changes"],"limitations":["Maintainability assessment depends on LLM's understanding of code quality principles; subjective and may vary","No integration with code metrics tools (cyclomatic complexity, code coverage, etc.); analysis is LLM-based","Cannot assess maintainability of external dependencies or third-party code","Recommendations are suggestions; implementation effort and impact not quantified"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider","Source code context available in the workspace"],"input_types":["source code","code structure","test coverage information (if available)"],"output_types":["maintainability assessment","technical debt identification","refactoring recommendations","code quality suggestions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_13","uri":"capability://tool.use.integration.authentication.and.workspace.context.management","name":"authentication-and-workspace-context-management","description":"Manages user authentication and workspace context to enable personalized AI assistance. Users can sign in via email, GitHub, or GitLab credentials to unlock the Navie AI assistant and access personalized features. The extension maintains workspace context including the current file, project scope, git branch information, and recorded AppMap traces. Authentication state and workspace context are used to customize AI responses and enable features like branch-aware code review.","intents":["I want to sign in to AppMap to enable AI assistance","I need to maintain my workspace context across multiple chat sessions","I want AppMap to understand my current git branch for code review","I need to manage my API keys for different LLM providers"],"best_for":["individual developers using AppMap","teams managing shared workspaces","developers using multiple LLM providers"],"limitations":["Authentication mechanism and credential storage not documented; security implications unknown","No documented support for team-level authentication or workspace sharing","API key management mechanism not documented; unclear how keys are stored and secured","Workspace context is local to VS Code; no synchronization across machines or team members"],"requires":["VS Code AppMap extension installed","Email, GitHub, or GitLab account for authentication","API keys for selected LLM providers (stored locally)"],"input_types":["authentication credentials","API keys","workspace configuration"],"output_types":["authentication state","workspace context","personalized AI responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_2","uri":"capability://code.generation.editing.branch.aware.code.review.with.diff.analysis","name":"branch-aware-code-review-with-diff-analysis","description":"Performs AI-driven code reviews by analyzing differences between the current branch and base branch, using the `@review` chat mode to identify security issues, maintainability concerns, logic errors, and performance problems. The extension accesses git context to compare code changes and applies the selected LLM to generate review feedback. Reviews can be enhanced with runtime execution traces if AppMap data has been recorded for the changed code.","intents":["I want an AI-powered code review of my pull request before submitting it","I need to identify security vulnerabilities in my code changes","I want to check if my changes introduce performance regressions","I need to ensure my code changes follow maintainability best practices"],"best_for":["developers performing self-review before creating pull requests","teams using AI to augment code review processes","security-conscious teams wanting automated vulnerability scanning"],"limitations":["Review quality depends on the underlying LLM's security and performance knowledge","Cannot access code in uncommitted state or untracked files; only analyzes committed changes","No integration with GitHub/GitLab PR APIs documented; reviews are local to VS Code only","Runtime-enhanced reviews require pre-recorded AppMap traces; cannot dynamically execute code during review"],"requires":["VS Code AppMap extension installed","Git repository with at least one base branch for comparison","API key for selected LLM provider","Code changes committed to current branch"],"input_types":["git diff (current branch vs base branch)","source code files","execution traces (optional)"],"output_types":["natural language review feedback","security findings","performance recommendations","maintainability suggestions"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_3","uri":"capability://planning.reasoning.step.by.step.implementation.planning","name":"step-by-step-implementation-planning","description":"Generates detailed implementation plans for coding tasks using the `@plan` chat mode, which breaks down requirements into actionable steps. The AI analyzes the current codebase context, project structure, and existing patterns to create plans that align with the application's architecture. Plans are generated as structured text that developers can follow sequentially to implement features or refactor code.","intents":["I need to break down a complex feature into manageable implementation steps","I want to understand the best approach to refactor a large module","I need a step-by-step guide to implement a new feature that fits with existing code patterns","I want to plan a migration or architectural change before starting implementation"],"best_for":["developers planning complex features or refactorings","teams coordinating implementation across multiple developers","developers new to a codebase who need guidance on implementation approach"],"limitations":["Plans are generated by LLM without execution; developers must manually implement each step","Plan quality depends on the LLM's understanding of the codebase context provided","No automatic validation that generated plans are feasible or optimal","Plans may not account for external dependencies or constraints outside the codebase"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider","Source code context available in the workspace"],"input_types":["natural language task description","source code (project context)","existing code patterns"],"output_types":["structured implementation plans (text)","step-by-step instructions","code references and file paths"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_4","uri":"capability://code.generation.editing.ai.powered.code.generation.with.context","name":"ai-powered-code-generation-with-context","description":"Generates code snippets and implementations using the `@generate` chat mode, which synthesizes new code based on the current codebase context, existing patterns, and project architecture. The AI produces patch-ready code that developers can directly integrate into their files. Generation can be enhanced with runtime execution traces to ensure generated code aligns with actual application behavior and data flows.","intents":["I need to generate a function that follows the patterns used in my codebase","I want to generate code that integrates seamlessly with my existing architecture","I need to quickly generate boilerplate code that matches my project's style","I want to generate code that handles the data flows I've observed in my application"],"best_for":["developers accelerating implementation of well-defined features","teams with consistent code patterns and architecture","developers working in languages with strong pattern conventions (Java, TypeScript)"],"limitations":["Generated code quality depends on LLM's ability to understand codebase patterns from provided context","No automatic testing or validation of generated code; developers must review and test","Generation may not handle edge cases or complex error scenarios","Context window limitations may prevent the LLM from seeing the full codebase for very large projects"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider","Source code context available in the workspace","Clear specification of what code to generate"],"input_types":["natural language code generation requests","source code (project context and patterns)","execution traces (optional)"],"output_types":["source code snippets","complete function implementations","patch-ready code"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_5","uri":"capability://code.generation.editing.automated.test.generation.with.coverage.awareness","name":"automated-test-generation-with-coverage-awareness","description":"Generates unit and integration test cases using the `@test` chat mode, which analyzes the code structure and existing test patterns to create tests that align with the project's testing conventions. The AI can generate tests based on static code analysis or enhanced with runtime execution traces to create tests that cover observed code paths and data flows. Generated tests are provided as patch-ready code.","intents":["I need to generate unit tests for a function I just wrote","I want to generate integration tests that cover the data flows I've observed in my application","I need to increase test coverage for a module quickly","I want to generate tests that follow my project's testing patterns and conventions"],"best_for":["developers improving test coverage on existing code","teams with established testing frameworks and patterns","developers working in languages with mature testing ecosystems (Java, JavaScript, Python)"],"limitations":["Generated tests may not cover all edge cases or error scenarios","Test quality depends on the LLM's understanding of the code and testing best practices","No automatic execution or validation of generated tests; developers must run and verify","Runtime-enhanced test generation requires pre-recorded AppMap traces; cannot dynamically execute code"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider","Source code context available in the workspace","Testing framework installed (Jest, JUnit, pytest, etc.)","Optionally: recorded AppMap execution traces for coverage-aware test generation"],"input_types":["source code (functions, classes, modules)","existing test patterns","execution traces (optional)"],"output_types":["unit test code","integration test code","test fixtures and mocks"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_6","uri":"capability://image.visual.mermaid.diagram.generation.for.architecture.visualization","name":"mermaid-diagram-generation-for-architecture-visualization","description":"Generates Mermaid diagrams (sequence diagrams, UML diagrams, dependency graphs) using the `@diagram` chat mode, which visualizes code structure, architecture, and data flows. The AI can generate diagrams from static code analysis or enhanced with runtime execution traces to show actual application behavior. Generated diagrams are rendered in the Mermaid Live Editor and can be exported or embedded in documentation.","intents":["I need to visualize the architecture of my application for documentation","I want to create a sequence diagram showing how my microservices communicate","I need to understand the dependency structure of my codebase","I want to visualize the actual data flow through my application during a specific operation"],"best_for":["teams documenting architecture and design","developers understanding complex system interactions","teams communicating architecture to stakeholders"],"limitations":["Diagram complexity is limited by Mermaid syntax capabilities; very large systems may be difficult to visualize","Static analysis diagrams may not reflect actual runtime behavior; runtime-enhanced diagrams require pre-recorded traces","Diagram generation depends on LLM's ability to understand code structure and architecture","Generated diagrams are static; no interactive exploration or drill-down capabilities documented"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider","Source code context available in the workspace","Optionally: recorded AppMap execution traces for runtime-enhanced diagrams"],"input_types":["source code (architecture context)","execution traces (optional)","natural language diagram requests"],"output_types":["Mermaid diagram syntax","rendered diagrams (via Mermaid Live Editor)","exportable diagram images"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_7","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.configurable.models","name":"multi-provider-llm-integration-with-configurable-models","description":"Provides a unified chat interface that supports multiple LLM providers including OpenAI, Anthropic Claude, Google Gemini, GitHub Copilot, Mistral, Mixtral, Ollama (local), and AppMap's built-in endpoint. Users can configure their preferred LLM by providing API keys, and the extension routes all chat queries through the selected provider. The architecture abstracts provider-specific APIs behind a common chat interface, allowing seamless switching between models.","intents":["I want to use my preferred LLM provider (OpenAI, Anthropic, etc.) with AppMap","I need to run AppMap locally without external API calls using Ollama","I want to switch between different LLM providers based on task requirements","I need to use GitHub Copilot as my LLM provider within AppMap"],"best_for":["developers with existing LLM provider subscriptions","teams with security requirements for local-only LLM execution","developers wanting to experiment with different LLM models","enterprises using GitHub Copilot for standardized AI tooling"],"limitations":["Exact mechanism for LLM provider selection and configuration not documented; assumed to be via settings UI","API key management and storage mechanism not documented; security implications unknown","No documented support for custom/self-hosted LLM endpoints beyond Ollama","Switching between providers requires manual configuration; no automatic fallback or load balancing","Each provider has different rate limits, costs, and model capabilities; users must manage these independently"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider (OpenAI, Anthropic, Google Gemini, GitHub Copilot, Mistral, Mixtral)","OR: Ollama installed and running locally for local LLM execution","OR: AppMap account for built-in endpoint (free tier available)"],"input_types":["API keys or authentication credentials","LLM provider selection"],"output_types":["chat responses from selected LLM","code generation outputs","analysis results"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_8","uri":"capability://text.generation.language.chat.mode.based.interaction.with.command.prefixes","name":"chat-mode-based-interaction-with-command-prefixes","description":"Implements a chat-based interaction model where users invoke different AI capabilities by prefixing queries with `@` commands (`@explain`, `@plan`, `@generate`, `@test`, `@diagram`, `@review`). Each mode routes the query to a specialized AI workflow optimized for that task. The chat interface maintains conversation history and context across multiple turns, allowing follow-up questions and iterative refinement of results.","intents":["I want to switch between different AI tasks (explanation, planning, generation) within a single chat interface","I need to ask follow-up questions to refine AI-generated code or plans","I want a consistent, discoverable interface for all AppMap AI capabilities","I need to maintain context across multiple related queries"],"best_for":["developers preferring chat-based interaction over menu-driven interfaces","teams wanting a unified AI assistant interface within the IDE","developers working on multiple related tasks that benefit from conversation context"],"limitations":["Mode selection via text prefix may be less discoverable than menu-based UI for new users","No documented support for custom modes or extensible command system","Chat history persistence and context window management not documented","No documented support for multi-turn conversations with state management across sessions"],"requires":["VS Code AppMap extension installed","API key for selected LLM provider"],"input_types":["natural language queries with `@` prefix commands","follow-up questions","code context (implicit from current file/project)"],"output_types":["chat responses","code snippets","structured outputs (plans, diagrams)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-appland-appmap__cap_9","uri":"capability://memory.knowledge.codebase.context.injection.for.ai.queries","name":"codebase-context-injection-for-ai-queries","description":"Automatically injects codebase context into LLM queries by analyzing the current file, project scope, git branch information, and optionally recorded AppMap execution traces. The extension builds a context representation that includes code structure, existing patterns, dependencies, and runtime behavior, then includes this context in prompts sent to the LLM. This enables AI responses that are tailored to the specific codebase rather than generic.","intents":["I want AI-generated code that follows my project's patterns and conventions","I need explanations that reference my actual codebase, not generic examples","I want code reviews that understand my architecture and design decisions","I need AI assistance that is aware of my project's dependencies and constraints"],"best_for":["developers working in established codebases with consistent patterns","teams wanting AI assistance that respects architectural decisions","projects with domain-specific code patterns or conventions"],"limitations":["Context window limitations may prevent full codebase context for very large projects","Context selection algorithm not documented; may miss important patterns or include irrelevant code","No documented control over what context is included in prompts; users cannot customize context selection","Runtime trace context requires pre-recorded AppMap data; cannot dynamically analyze code during query"],"requires":["VS Code AppMap extension installed","Source code available in the workspace","API key for selected LLM provider with sufficient context window"],"input_types":["current file content","project structure","git branch information","execution traces (optional)"],"output_types":["context-aware AI responses","codebase-specific code generation","architecture-aligned recommendations"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["VS Code (minimum version unknown)","Application code must be executable within VS Code environment","For language support: Java, JavaScript, Node.js, Python, Ruby, or TypeScript","VS Code AppMap extension installed","User authentication (email, GitHub, or GitLab account)","API key for selected LLM provider (OpenAI, Anthropic, Google Gemini, GitHub Copilot, or AppMap's built-in endpoint)","Optionally: recorded AppMap execution traces for runtime context","API key for selected LLM provider","Source code context available in the workspace","Recorded AppMap execution traces with timing data (flame graphs)"],"failure_modes":["Recording must be explicitly triggered or requested via chat — not automatic for all code execution","Only captures traces from code executed within VS Code environment; cannot trace production systems or external processes","Runtime overhead of execution tracing not quantified in documentation; may impact performance of traced applications","Trace capture scope limited to development/testing sessions; no persistent historical trace storage documented","Requires authentication (email, GitHub, or GitLab) to enable Navie AI assistant","Explanation quality depends on availability of runtime traces; static analysis alone may be insufficient for complex behavior","No offline mode documented; requires connectivity to LLM provider (AppMap endpoint, OpenAI, Anthropic, etc.)","Explanation accuracy limited by the underlying LLM's training data and reasoning capabilities","Security detection depends on LLM's training data and reasoning; may miss novel or complex vulnerabilities","No integration with dedicated security scanning tools (SAST, DAST); provides AI-based analysis only","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.56,"quality":0.5,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","last_scraped_at":"2026-05-03T15:20:33.198Z","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=appmap","compare_url":"https://unfragile.ai/compare?artifact=appmap"}},"signature":"7guNI2mHMicMgFl1/vEfRLSXvTp08ZMxzeMJV+Ev7LdMY32LssZxDziNANE9fYObkd/oXG2AmF1Kc9km/Ls6AQ==","signedAt":"2026-06-20T17:53:40.973Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/appmap","artifact":"https://unfragile.ai/appmap","verify":"https://unfragile.ai/api/v1/verify?slug=appmap","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"}}