{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-maneetio-devpal","slug":"devpal-ai-developer-assistant-chat-code-lab","name":"DevPal - AI Developer Assistant, Chat & Code Lab","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=Maneetio.DevPal","page_url":"https://unfragile.ai/devpal-ai-developer-assistant-chat-code-lab","categories":["code-editors","testing-quality"],"tags":["ai","ai-tools","algorithms","analysis","anomaly","artificial intelligence","assessment","audit","autocorrect","automated review","c#","c++","chatgpt","clean code","code analysis","code generator","code integrity","code quality","code review","complexity","compliance","correctness","coverage","css","cybersecurity","data science","debugging","deep learning","deployment","detection","devops","documentation","efficiency","engineering","error detection","ethics","golang","gpt","gpt3","gpt4","integration","integrity","java","javascript","kotlin","languages","lua","machine learning","maintenance","management","modelling","monitoring","neural networks","nlp","optimization","perl","php","practices","prediction","python","r","refactoring","reliability","ruby","rust","scala","scalability","sdlc","security","semantics","shell","software quality","sql","standardization","static analysis","swift","syntax","testing","typescript","vulnerabilities","vulnerability scanner"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-maneetio-devpal__cap_0","uri":"capability://code.generation.editing.ai.powered.bug.detection.and.fixing.with.vulnerability.scanning","name":"ai-powered bug detection and fixing with vulnerability scanning","description":"Analyzes selected code or entire files by sending them to OpenAI's API (GPT-3.5/GPT-4) to identify bugs, security vulnerabilities, performance issues, and logical errors. The extension receives structured feedback from the model and presents findings in the sidebar panel with click-to-paste fixes directly into the editor. Works by tokenizing code within OpenAI's context window limits and leveraging the model's training on common vulnerability patterns and code anti-patterns.","intents":["I want to automatically scan my code for security vulnerabilities before committing","I need to identify bugs in my code without manually reviewing every line","I want to find performance bottlenecks and logical errors in my functions","I need to check for code integrity issues across different programming languages"],"best_for":["solo developers building applications without dedicated security review","teams wanting lightweight automated code scanning without enterprise tools","developers learning secure coding practices through AI-guided feedback"],"limitations":["Analysis limited to code that fits within OpenAI API token limits (~4,000-8,000 tokens depending on model), making it unsuitable for analyzing large monolithic files","Vulnerability detection quality depends entirely on OpenAI model version and training data; may miss novel or context-specific security issues","No persistent vulnerability tracking or historical trend analysis across commits","Requires internet connectivity and active OpenAI API quota; analysis latency depends on API response times (typically 2-10 seconds)","No support for custom vulnerability rules or organization-specific security policies"],"requires":["Visual Studio Code (version unknown, likely 1.60+)","Valid OpenAI API key with active billing","Internet connectivity to OpenAI endpoints","Code file in a language supported by OpenAI models (Python, JavaScript, Java, C++, Go, Rust, etc.)"],"input_types":["source code (text)","code selection (partial file)","entire file content"],"output_types":["structured bug/vulnerability list (text)","suggested fixes (code)","severity classifications (text)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_1","uri":"capability://code.generation.editing.automated.unit.test.generation.with.framework.customization","name":"automated unit test generation with framework customization","description":"Generates unit tests for selected functions or entire files by submitting code to OpenAI's API with a prompt specifying the preferred testing framework (Jest, pytest, JUnit, etc.). The model generates test cases covering happy paths, edge cases, and error conditions, which are returned as formatted code ready to paste into test files. Implementation uses prompt engineering to guide the model toward framework-specific syntax and best practices.","intents":["I want to generate unit tests quickly without writing boilerplate test code manually","I need test coverage for legacy code that lacks tests","I want to generate tests in a specific framework my team uses (Jest, pytest, Mocha, etc.)","I need to create edge case tests for complex functions"],"best_for":["developers working on projects with low test coverage who need rapid test generation","teams adopting test-driven development and needing scaffolding","developers learning testing best practices through AI-generated examples"],"limitations":["Generated tests may not cover all business logic edge cases; require manual review and refinement","Test quality depends on code clarity and function documentation; poorly documented functions produce weaker tests","No integration with test runners; generated tests must be manually copied into test files and executed separately","Framework support limited to what OpenAI model knows; obscure or very new testing frameworks may not be recognized","Cannot generate tests for code with external dependencies or complex mocking requirements without manual setup","No persistent test history or tracking of which functions have generated tests"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Source code file in a supported language (JavaScript/TypeScript, Python, Java, C#, Go, etc.)","Knowledge of which testing framework to specify (Jest, pytest, JUnit, Mocha, etc.)"],"input_types":["source code (function or file)","testing framework preference (text parameter)"],"output_types":["test code (formatted for specified framework)","test cases with assertions (code)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_10","uri":"capability://code.generation.editing.context.aware.code.completion.with.multi.file.awareness","name":"context-aware code completion with multi-file awareness","description":"Provides intelligent code completion suggestions by analyzing the current file context and optionally project context. When a user starts typing, the extension sends the current file (or selection) to OpenAI's API along with the incomplete code, and the model suggests completions that match the code style and logic flow. Implementation uses prompt engineering to guide the model toward contextually appropriate suggestions.","intents":["I want code completion suggestions that understand my code style and project conventions","I need to complete complex logic that depends on surrounding code context","I want suggestions for function implementations based on function signatures","I need to complete code that requires understanding of imported libraries and dependencies"],"best_for":["developers working in unfamiliar codebases who need style-aware completions","teams with consistent code patterns that benefit from context-aware suggestions","developers working in languages with less mature IDE support"],"limitations":["Completion latency depends on OpenAI API response time (typically 1-3 seconds), making it slower than local completion engines","Suggestions may not understand project-specific libraries or custom frameworks without additional context","No integration with IDE's native completion UI; suggestions appear in chat or sidebar rather than inline","Cannot complete code that requires understanding of runtime behavior or external data","Large files may exceed token limits, resulting in incomplete context for suggestions"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Code file open in editor with cursor position"],"input_types":["current file context (text)","incomplete code (text)","optional project context (text)"],"output_types":["completion suggestions (code)","multiple alternatives (code)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_11","uri":"capability://text.generation.language.error.message.explanation.and.debugging.assistance","name":"error message explanation and debugging assistance","description":"Analyzes error messages and stack traces by submitting them to OpenAI's API along with relevant code context. The model explains what caused the error, why it occurred, and provides step-by-step debugging suggestions or fixes. Works by parsing error output and correlating it with source code to provide targeted explanations and remediation steps.","intents":["I want to understand what an error message means and why my code is failing","I need debugging suggestions for a runtime error without manually tracing the code","I want to understand stack traces and identify the root cause of failures","I need help fixing common errors like null pointer exceptions, type mismatches, etc."],"best_for":["junior developers learning debugging techniques","developers working with unfamiliar frameworks or languages","teams reducing debugging time by getting AI-assisted explanations"],"limitations":["Error explanations depend on error message clarity; cryptic or obfuscated errors may not be explained accurately","Debugging suggestions may not identify root causes that require runtime inspection or external data","Cannot execute code or inspect runtime state; suggestions are based on static analysis only","Error context limited to visible stack trace; may miss environmental factors or configuration issues","No learning from user feedback; each error is analyzed independently"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Error message or stack trace (text)","Relevant source code context (optional but recommended)"],"input_types":["error message (text)","stack trace (text)","source code context (optional, text)"],"output_types":["error explanation (text)","root cause analysis (text)","debugging steps (text)","suggested fixes (code)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_2","uri":"capability://code.generation.editing.code.refactoring.and.optimization.with.language.agnostic.transformation","name":"code refactoring and optimization with language-agnostic transformation","description":"Accepts selected code or entire files and submits them to OpenAI's API with refactoring directives (simplify, optimize for performance, improve readability, reduce complexity). The model returns refactored code applying design patterns, reducing duplication, improving variable naming, and optimizing algorithms. Works by leveraging the LLM's understanding of code idioms across 40+ programming languages without requiring language-specific parsers.","intents":["I want to simplify complex or convoluted code to improve readability","I need to optimize code for performance without rewriting it manually","I want to reduce code duplication and apply DRY principles","I need to refactor legacy code to modern language idioms and best practices"],"best_for":["developers maintaining legacy codebases needing modernization","teams standardizing code style across projects in multiple languages","individual developers wanting quick refactoring suggestions without manual analysis"],"limitations":["Refactored code may change behavior subtly; requires thorough testing before deployment","Cannot preserve all original comments and documentation; may lose context-specific explanations","Optimization suggestions may not be optimal for specific runtime environments or hardware constraints","Large files may be truncated due to token limits, resulting in incomplete refactoring","No awareness of project-specific conventions, architectural patterns, or team coding standards","Cannot refactor code with unresolved external dependencies or complex type systems without additional context"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Source code in a language supported by OpenAI models","Clear understanding of refactoring goals (simplify, optimize, modernize, etc.)"],"input_types":["source code (function, class, or file)","refactoring directive (text: 'simplify', 'optimize', 'modernize', etc.)"],"output_types":["refactored code (text)","explanation of changes (text)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_3","uri":"capability://text.generation.language.interactive.code.review.and.explanation.via.chat.interface","name":"interactive code review and explanation via chat interface","description":"Provides a sidebar chat panel where developers can ask questions about code, request explanations of complex logic, and receive line-by-line analysis. The chat maintains context of the current file or selection and sends code snippets to OpenAI's API along with natural language questions. Responses are streamed back and displayed in the chat UI, enabling iterative code review without switching contexts.","intents":["I want to understand what a complex function does without reading every line","I need to ask questions about code logic and get detailed explanations","I want to discuss code quality issues with an AI reviewer in real-time","I need to understand how to improve specific code sections"],"best_for":["junior developers learning from code reviews and explanations","developers onboarding to unfamiliar codebases needing rapid understanding","teams using AI as a supplementary code reviewer for knowledge sharing"],"limitations":["Chat context limited to current file or selection; cannot maintain conversation history across multiple files or sessions","Explanations may be verbose or overly technical depending on OpenAI model behavior; no tuning for audience level","No ability to ask follow-up questions that reference previous code snippets without re-pasting them","Chat history not persisted; closing the panel loses conversation context","Cannot ask questions about code behavior that requires runtime execution or external data"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Code file open in editor with selection or full file context"],"input_types":["natural language question (text)","code context (current file or selection)","follow-up questions (text)"],"output_types":["explanation (text)","code analysis (text)","improvement suggestions (text)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_4","uri":"capability://tool.use.integration.intelligent.terminal.command.assistance.and.suggestion","name":"intelligent terminal command assistance and suggestion","description":"Monitors terminal activity and suggests commands based on user intent or error messages. When a user types a partial command or encounters an error, the extension can suggest the correct command syntax or explain what went wrong. Implementation sends terminal input/error context to OpenAI's API to generate contextual command suggestions, which are displayed as inline suggestions or in the chat panel.","intents":["I want suggestions for the correct command syntax when I get a terminal error","I need help remembering the exact flags for a complex CLI tool","I want to understand what a terminal error message means and how to fix it","I need to translate a command from one shell to another (bash to PowerShell, etc.)"],"best_for":["developers working with unfamiliar CLI tools or command-line interfaces","DevOps engineers managing complex command sequences","developers switching between different shells or operating systems"],"limitations":["Terminal context limited to visible output; cannot access full command history or environment variables","Suggestions may be incorrect for environment-specific commands or custom scripts","No ability to execute suggested commands automatically; users must manually copy and run them","Error message interpretation depends on OpenAI model's knowledge of specific tools; obscure tools may not be recognized","No learning from user corrections; each session starts fresh without remembering which suggestions were helpful"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","VS Code integrated terminal open and active"],"input_types":["terminal error message (text)","partial command input (text)","user intent description (text)"],"output_types":["command suggestion (text)","error explanation (text)","alternative commands (text)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_5","uri":"capability://code.generation.editing.code.documentation.and.comment.generation","name":"code documentation and comment generation","description":"Generates documentation strings, inline comments, and README sections for code by submitting functions or files to OpenAI's API. The model produces JSDoc/Docstring-formatted comments explaining parameters, return types, and behavior, as well as high-level documentation describing the code's purpose. Works by analyzing code structure and generating documentation in the appropriate format for the detected language.","intents":["I want to automatically generate JSDoc/Docstring comments for my functions","I need to document legacy code that has no comments or documentation","I want to generate README sections describing my code modules","I need to create API documentation for my functions"],"best_for":["developers documenting existing codebases without documentation","teams enforcing documentation standards across projects","open-source maintainers needing to improve code documentation"],"limitations":["Generated documentation may be generic or miss domain-specific context; requires manual review and refinement","Cannot generate documentation for code with unclear intent or poor naming; quality depends on code clarity","Documentation format may not match team conventions or style guides without manual editing","No awareness of existing documentation in other files; may duplicate or contradict existing docs","Cannot generate documentation for complex algorithms without additional context or mathematical notation"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Source code file in a supported language"],"input_types":["source code (function, class, or file)","documentation style preference (JSDoc, Docstring, Markdown, etc.)"],"output_types":["formatted documentation (text)","inline comments (code)","README sections (Markdown)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_6","uri":"capability://code.generation.editing.code.language.translation.and.conversion","name":"code language translation and conversion","description":"Converts code from one programming language to another by submitting source code to OpenAI's API with a target language specification. The model translates logic, syntax, and idioms to the target language while preserving functionality. Works by leveraging the LLM's understanding of language semantics and common translation patterns across 40+ languages.","intents":["I want to convert a Python script to JavaScript for a web application","I need to migrate code from Java to Go for better performance","I want to translate a code snippet to a language I'm less familiar with","I need to convert code to a different language for a specific platform or framework"],"best_for":["developers migrating codebases between languages","teams adopting new languages and needing to translate existing logic","developers learning new languages by seeing translations of familiar code"],"limitations":["Translated code may not be idiomatic to the target language; requires review and refactoring by someone familiar with the target language","Language-specific libraries and frameworks may not have direct equivalents; translation may require significant manual adjustment","Performance characteristics may differ between languages; translated code may need optimization for the target language","Type systems and memory models differ between languages; translations may lose or add type safety","Large codebases may exceed token limits; translation works best for individual functions or small modules"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Source code in a supported language","Target language specification (Python, JavaScript, Go, Rust, etc.)"],"input_types":["source code (function, class, or file)","target language (text parameter)"],"output_types":["translated code (text)","translation notes (text)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_7","uri":"capability://data.processing.analysis.code.complexity.analysis.and.metrics.reporting","name":"code complexity analysis and metrics reporting","description":"Analyzes code complexity by submitting files to OpenAI's API and requesting metrics such as cyclomatic complexity, cognitive complexity, maintainability index, and code smell detection. The model examines code structure and returns a structured analysis with specific complexity scores and recommendations for simplification. Implementation uses prompt engineering to guide the model toward producing quantifiable metrics.","intents":["I want to measure the complexity of my code to identify refactoring candidates","I need to track code quality metrics over time to monitor technical debt","I want to identify functions that are too complex and need simplification","I need to report on code maintainability to stakeholders"],"best_for":["teams tracking technical debt and code quality metrics","developers identifying refactoring priorities in large codebases","engineering managers monitoring code quality trends"],"limitations":["Complexity metrics are estimates based on LLM analysis, not precise measurements from static analysis tools","No historical tracking of metrics over time; each analysis is independent","Metrics may not align with industry-standard tools (SonarQube, Code Climate); useful for relative comparison only","Cannot analyze code with unresolved dependencies or external context","Large files may be truncated, resulting in incomplete complexity analysis"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Source code file in a supported language"],"input_types":["source code (function, class, or file)"],"output_types":["complexity metrics (structured data: cyclomatic complexity, cognitive complexity, etc.)","maintainability index (numeric)","code smell detection (text)","simplification recommendations (text)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_8","uri":"capability://tool.use.integration.multi.model.selection.and.api.configuration","name":"multi-model selection and api configuration","description":"Allows users to select from multiple OpenAI models (GPT-3.5-turbo, GPT-4, etc.) and configure API parameters such as temperature, max tokens, and request timeout. The extension stores API key and model preferences in VS Code settings, enabling per-project or global configuration. Implementation uses VS Code's settings API to persist configuration and dynamically routes requests to the selected model.","intents":["I want to use GPT-4 for complex code analysis and GPT-3.5 for simple tasks to optimize costs","I need to configure API parameters like temperature for more deterministic or creative responses","I want to set different models for different projects based on their complexity","I need to manage my OpenAI API quota by controlling token usage per request"],"best_for":["developers optimizing costs by selecting appropriate models for different tasks","teams managing multiple OpenAI API keys across projects","users experimenting with different models to find optimal quality/cost tradeoffs"],"limitations":["Model selection limited to OpenAI models; no support for other LLM providers (Anthropic, Cohere, etc.)","Configuration changes require VS Code restart or manual refresh to take effect","No validation of API key validity before attempting requests; errors only appear at runtime","No cost estimation or quota tracking; users must monitor OpenAI dashboard separately","Settings stored in plaintext in VS Code settings file; API key security depends on file system permissions"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key","Knowledge of available OpenAI models and their capabilities"],"input_types":["model selection (dropdown: GPT-3.5-turbo, GPT-4, etc.)","API parameters (temperature: 0-2, max_tokens: number, timeout: number)"],"output_types":["configuration saved to VS Code settings (JSON)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-maneetio-devpal__cap_9","uri":"capability://safety.moderation.code.compliance.and.standards.checking","name":"code compliance and standards checking","description":"Analyzes code against specified coding standards, style guides, and compliance requirements by submitting code to OpenAI's API with a standards specification. The model checks for violations of naming conventions, architectural patterns, security best practices, and organizational policies. Returns a list of violations with explanations and suggested fixes. Works by encoding standards as prompts and leveraging the LLM's understanding of code patterns.","intents":["I want to check if my code follows our team's coding standards and style guide","I need to ensure code complies with security policies before deployment","I want to verify code follows architectural patterns defined in our organization","I need to check for compliance with regulatory requirements (GDPR, HIPAA, etc.)"],"best_for":["teams enforcing consistent coding standards across projects","organizations with strict compliance requirements","developers learning team standards through automated feedback"],"limitations":["Standards checking depends on clear specification in prompts; ambiguous standards produce inconsistent results","Cannot enforce standards automatically; violations are reported but not auto-fixed","No integration with linters or formatters; requires manual remediation","Standards must be re-specified for each analysis; no persistent standards library","Cannot check for compliance with external regulatory frameworks without detailed specification"],"requires":["Visual Studio Code (version unknown)","Valid OpenAI API key with active billing","Clear specification of coding standards or compliance requirements","Source code file in a supported language"],"input_types":["source code (function, class, or file)","standards specification (text: style guide, naming conventions, architectural patterns, etc.)"],"output_types":["compliance violations (structured list)","violation explanations (text)","suggested fixes (code)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (version unknown, likely 1.60+)","Valid OpenAI API key with active billing","Internet connectivity to OpenAI endpoints","Code file in a language supported by OpenAI models (Python, JavaScript, Java, C++, Go, Rust, etc.)","Visual Studio Code (version unknown)","Source code file in a supported language (JavaScript/TypeScript, Python, Java, C#, Go, etc.)","Knowledge of which testing framework to specify (Jest, pytest, JUnit, Mocha, etc.)","Code file open in editor with cursor position","Error message or stack trace (text)","Relevant source code context (optional but recommended)"],"failure_modes":["Analysis limited to code that fits within OpenAI API token limits (~4,000-8,000 tokens depending on model), making it unsuitable for analyzing large monolithic files","Vulnerability detection quality depends entirely on OpenAI model version and training data; may miss novel or context-specific security issues","No persistent vulnerability tracking or historical trend analysis across commits","Requires internet connectivity and active OpenAI API quota; analysis latency depends on API response times (typically 2-10 seconds)","No support for custom vulnerability rules or organization-specific security policies","Generated tests may not cover all business logic edge cases; require manual review and refinement","Test quality depends on code clarity and function documentation; poorly documented functions produce weaker tests","No integration with test runners; generated tests must be manually copied into test files and executed separately","Framework support limited to what OpenAI model knows; obscure or very new testing frameworks may not be recognized","Cannot generate tests for code with external dependencies or complex mocking requirements without manual setup","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3,"quality":0.34,"ecosystem":0.45,"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":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:31.090Z","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=devpal-ai-developer-assistant-chat-code-lab","compare_url":"https://unfragile.ai/compare?artifact=devpal-ai-developer-assistant-chat-code-lab"}},"signature":"abW8xv7MNHMgAL8nATQg1WZF+2gyY5By6iSUg2UkK0caVuAxa7bcx50YRYuDh4LOVFzyCTgdDfBjFEALpPyXAw==","signedAt":"2026-06-21T12:38:13.321Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/devpal-ai-developer-assistant-chat-code-lab","artifact":"https://unfragile.ai/devpal-ai-developer-assistant-chat-code-lab","verify":"https://unfragile.ai/api/v1/verify?slug=devpal-ai-developer-assistant-chat-code-lab","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"}}