{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-monicaim-monica-code","slug":"monica-code","name":"Monica Code","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=MonicaIM.monica-code","page_url":"https://unfragile.ai/monica-code","categories":["code-editors"],"tags":["ai","chatgpt","claude","codegpt","codestral","copilot","github","json","jsonc","keybindings","llama","mistral","sonnet"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-monicaim-monica-code__cap_0","uri":"capability://code.generation.editing.cursor.aware.real.time.code.completion","name":"cursor-aware real-time code completion","description":"Generates contextual code suggestions as the developer types by analyzing cursor position, surrounding code context, and inline comments. The extension monitors keystroke events in the active editor and sends the current file buffer plus cursor offset to the configured AI model (GPT-4o, Claude 3.5 Sonnet, or ChatGPT API), returning completions that respect language syntax and project conventions. Completion suggestions appear inline without blocking editor interaction.","intents":["Get intelligent code suggestions while typing without breaking flow","Autocomplete function signatures and boilerplate code patterns","Receive context-aware suggestions based on comments and variable names"],"best_for":["Solo developers and small teams using VS Code","Developers working in 20+ supported languages (Python, JavaScript, TypeScript, Java, Go, C++, etc.)","Teams wanting AI completion without GitHub Copilot licensing"],"limitations":["Completion latency depends on API response time — no local fallback for offline coding","Single-file context only; does not analyze cross-file imports or dependency graphs","No token budget enforcement — may consume API quota rapidly on large files","Completion quality varies by model; ChatGPT API baseline may be lower than GPT-4o"],"requires":["VS Code (minimum version unknown, likely 1.60+)","API key for OpenAI (ChatGPT/GPT-4o) or Anthropic (Claude 3.5 Sonnet)","Active internet connection for API calls","File must be in a supported language (20+ languages documented)"],"input_types":["source code (current file buffer)","cursor position (line and column offset)","inline comments (used as context hints)"],"output_types":["code completion suggestions (inline text)","multi-line code blocks"],"categories":["code-generation-editing","editor-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_1","uri":"capability://code.generation.editing.selection.based.code.editing.and.refactoring","name":"selection-based code editing and refactoring","description":"Enables developers to select any code snippet in the editor and apply AI-driven transformations via natural language prompts. The extension captures the selected text range, sends it along with the user's instruction to the AI model, and replaces the selection with the generated output. This pattern supports inline refactoring, function rewriting, code style normalization, and bug fixes without leaving the editor context.","intents":["Refactor a function or class by describing desired changes in plain English","Rewrite code to follow a different style or pattern (e.g., async/await conversion)","Fix bugs or improve code quality by selecting problematic code and requesting fixes","Transform code between paradigms (e.g., callback-based to Promise-based)"],"best_for":["Developers performing ad-hoc code refactoring without external tools","Teams standardizing code style across legacy codebases","Developers learning new patterns and wanting AI-assisted rewrites"],"limitations":["Selection-based scope means no automatic multi-file refactoring — each file must be edited separately","No undo/version history for generated code — relies on VS Code's undo stack","AI may generate syntactically valid but semantically incorrect code — requires manual review","Large selections (>4000 tokens) may exceed API context limits or incur high costs"],"requires":["VS Code with Monica Code extension installed","API credentials for OpenAI or Anthropic","Selected code snippet (minimum 1 character, practical limit ~4000 tokens)"],"input_types":["source code (selected text range)","natural language instruction (user prompt)","implicit context (file language, surrounding code)"],"output_types":["refactored source code (replaces selection)","code with applied transformations"],"categories":["code-generation-editing","editor-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_10","uri":"capability://code.generation.editing.test.case.generation.from.code.and.requirements","name":"test case generation from code and requirements","description":"Generates unit test cases, integration tests, or end-to-end test scenarios based on selected code or natural language requirements. The extension sends code (or requirements) to the AI model with a test generation prompt, specifying the testing framework (Jest, pytest, JUnit, etc.), and returns test code ready to be added to the project. This capability reduces boilerplate test writing and helps developers achieve higher code coverage without manual effort.","intents":["Generate unit tests for functions or classes without writing test boilerplate","Create test cases covering edge cases and error scenarios","Generate integration tests from API specifications or requirements","Increase code coverage by automatically generating tests for untested code"],"best_for":["Teams enforcing code coverage standards (e.g., >80% coverage)","Developers practicing test-driven development (TDD) wanting faster test generation","Teams with tight deadlines needing to increase test coverage quickly","Projects with complex business logic requiring comprehensive test scenarios"],"limitations":["Generated tests may have false positives or miss edge cases — require manual review and adjustment","Test quality depends on code clarity — poorly-written code may generate ineffective tests","No automatic test execution or validation — generated tests must be run manually to verify correctness","Testing framework must be specified in the prompt — no automatic framework detection","Generated tests may not cover domain-specific requirements or business logic edge cases"],"requires":["VS Code with Monica Code extension","Code snippet or requirements description","API credentials for OpenAI or Anthropic","Testing framework installed (Jest, pytest, JUnit, etc.)","Optional: existing test files as examples for style consistency"],"input_types":["source code (function, class, or module)","natural language requirements (for test scenarios)","testing framework specification (Jest, pytest, etc.)","implicit context (language, project structure)"],"output_types":["test code (in the specified framework)","test cases (multiple test functions or methods)","assertions and expected values"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_11","uri":"capability://text.generation.language.error.diagnosis.and.fix.suggestion.with.stack.trace.analysis","name":"error diagnosis and fix suggestion with stack trace analysis","description":"Analyzes error messages, stack traces, and logs provided by the developer (via text input or screenshot) and suggests root causes and fixes. The extension sends the error context to the AI model along with relevant code snippets (if available in the editor), and returns diagnostic suggestions with code fixes. This capability leverages the AI model's knowledge of common error patterns and debugging techniques to accelerate troubleshooting.","intents":["Understand cryptic error messages and stack traces without searching Stack Overflow","Get suggested fixes for runtime errors, compilation errors, or test failures","Debug issues in unfamiliar libraries or frameworks by analyzing error context","Accelerate troubleshooting by getting multiple hypotheses for error causes"],"best_for":["Developers debugging production issues or test failures","Teams working with unfamiliar libraries or frameworks","Solo developers without access to experienced mentors","Non-native English speakers struggling with error message comprehension"],"limitations":["Diagnosis accuracy depends on error message clarity — truncated or obfuscated errors may be misinterpreted","Suggested fixes may be incorrect if the root cause is not in the provided context (e.g., configuration issue, environment problem)","No automatic fix application — developers must manually apply suggested changes and verify correctness","Stack trace analysis may miss domain-specific context (e.g., business logic errors)","Large stack traces (>5000 tokens) may exceed API context limits"],"requires":["VS Code with Monica Code extension","Error message, stack trace, or log output (text or screenshot)","Optional: relevant code snippet from the editor","API credentials for OpenAI or Anthropic"],"input_types":["error message (text)","stack trace (text or screenshot via OCR)","log output (text)","relevant code snippet (optional, from editor)"],"output_types":["diagnostic suggestions (text with explanations)","root cause analysis (text)","suggested fixes (code snippets or descriptions)"],"categories":["text-generation-language","image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_2","uri":"capability://text.generation.language.multimodal.codebase.aware.chat.with.screenshot.debugging","name":"multimodal codebase-aware chat with screenshot debugging","description":"Provides a chat interface (sidebar panel) where developers can ask natural language questions about their codebase, with the extension indexing project files and making them available as context. The chat supports visual debugging by allowing developers to attach screenshots of error messages, logs, or UI bugs, which the AI model analyzes alongside code context to suggest fixes. The implementation likely uses vector embeddings or keyword indexing to retrieve relevant files from the workspace and constructs a context window combining retrieved code, chat history, and screenshot analysis.","intents":["Ask questions about how the codebase works without reading entire files","Debug runtime errors by sharing screenshots of error messages and stack traces","Understand architectural patterns and design decisions in unfamiliar code","Get explanations of specific functions or modules in natural language"],"best_for":["Developers onboarding to new codebases and needing rapid context","Teams debugging production issues with visual error context","Solo developers maintaining legacy code without documentation","Non-native speakers preferring natural language explanations over code reading"],"limitations":["Indexing depth unknown — may not include node_modules, build artifacts, or .gitignore'd files, limiting context completeness","Screenshot analysis depends on image quality and OCR accuracy — blurry or complex UI may not be parsed correctly","Codebase indexing is one-time or periodic — changes to code may not be reflected in chat context until re-indexing","No persistent chat history across sessions — each VS Code restart may lose conversation context","Large projects (>10K files) may exceed indexing capacity or API context window limits"],"requires":["VS Code with Monica Code extension","API credentials for OpenAI (GPT-4o recommended for vision) or Anthropic (Claude 3.5 Sonnet with vision)","Project workspace open in VS Code","For screenshot debugging: ability to capture and paste images into chat"],"input_types":["natural language questions (text)","source code files (indexed from workspace)","screenshots (PNG/JPG for visual debugging)","chat history (multi-turn conversation)"],"output_types":["natural language explanations (text)","code snippets (referenced from codebase)","debugging suggestions (text with code examples)","architectural insights (text)"],"categories":["text-generation-language","memory-knowledge","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_3","uri":"capability://code.generation.editing.multi.file.composer.with.version.navigation","name":"multi-file composer with version navigation","description":"Provides an interface (likely modal or sidebar panel) for creating and editing multiple files simultaneously as part of a single AI-driven composition task. Developers can request the AI to generate or modify multiple files (e.g., creating a new feature across controller, service, and test files), and the composer displays each file with version history navigation, allowing rollback to previous generations. The implementation likely maintains a version tree per file and uses the AI model to generate file contents based on a single prompt describing the desired outcome.","intents":["Generate boilerplate for new features across multiple files (e.g., CRUD endpoints with models, services, tests)","Refactor code that spans multiple files (e.g., renaming a class used across the codebase)","Create project scaffolding (e.g., new module with all necessary files)","Iterate on multi-file changes by navigating versions and selecting the best output"],"best_for":["Teams generating new features or modules with consistent structure","Developers scaffolding new projects or components","Teams wanting to enforce architectural patterns across multiple files","Developers iterating on complex changes that require coordinated edits"],"limitations":["Version navigation is limited to generated versions — no merge conflict resolution if manual edits are made between generations","No automatic dependency resolution — generated files may have import/require mismatches if AI doesn't understand project structure","File creation is not atomic — if some files are accepted and others rejected, manual cleanup may be needed","Context window limits may prevent generating many files at once (e.g., >10 files may exceed token limits)","No built-in validation — generated code may have syntax errors or logical bugs requiring manual review"],"requires":["VS Code with Monica Code extension","API credentials for OpenAI (GPT-4o) or Anthropic (Claude 3.5 Sonnet)","Project workspace open in VS Code","Clear prompt describing desired multi-file outcome"],"input_types":["natural language prompt (describing desired files and structure)","implicit project context (language, framework, existing files)","user selections (which generated versions to accept)"],"output_types":["multiple source code files (generated content)","version history per file (for navigation and rollback)","file metadata (names, paths, language)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_4","uri":"capability://text.generation.language.git.integrated.commit.message.generation","name":"git-integrated commit message generation","description":"Analyzes staged or uncommitted changes in the Git repository and automatically generates descriptive commit messages using the AI model. The extension accesses Git diff information (via VS Code's Git extension or direct Git CLI calls), sends the diff to the AI model with a configurable prompt template, and returns a formatted commit message. The prompt template is stored in a `config.json` file, allowing teams to enforce commit message conventions (e.g., conventional commits format).","intents":["Generate descriptive commit messages from code changes without manual typing","Enforce commit message conventions (e.g., 'feat:', 'fix:', 'refactor:') across the team","Summarize complex multi-file changes in a single message","Reduce time spent writing commit messages during rapid development"],"best_for":["Teams enforcing conventional commit standards","Solo developers wanting to maintain clean Git history without manual effort","Teams with non-native English speakers who struggle with commit message phrasing","High-velocity teams making many commits per day"],"limitations":["Commit message quality depends on diff clarity — large, unfocused diffs may generate vague messages","No semantic understanding of code intent — AI may misinterpret the purpose of changes","Configurable prompt is file-based (`config.json`) — changes require extension reload or restart","No validation of generated messages against team standards — poorly formatted messages may be generated if prompt is weak","Requires Git repository — does not work in non-Git projects or detached HEAD states"],"requires":["VS Code with Monica Code extension","Git repository initialized in the workspace","Staged or uncommitted changes in Git","API credentials for OpenAI or Anthropic","Optional: `config.json` file in workspace root with custom commit prompt"],"input_types":["Git diff (staged or uncommitted changes)","configurable prompt template (from config.json)","implicit context (repository name, branch)"],"output_types":["commit message (text, single or multi-line)","formatted message (respecting conventional commits if configured)"],"categories":["text-generation-language","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_5","uri":"capability://tool.use.integration.multi.model.backend.switching.with.configurable.api.routing","name":"multi-model backend switching with configurable api routing","description":"Allows developers to configure which AI model backend (OpenAI GPT-4o, ChatGPT API, Anthropic Claude 3.5 Sonnet) powers each capability, with API keys and model selection stored in VS Code settings or a configuration file. The extension abstracts the underlying API differences (request/response formats, token limits, vision capabilities) and routes prompts to the selected model. This enables cost optimization (using cheaper ChatGPT API for simple tasks, GPT-4o for complex reasoning) and model experimentation without code changes.","intents":["Switch between AI models to optimize cost vs. quality tradeoff","Use different models for different capabilities (e.g., Claude for code review, GPT-4o for completion)","Experiment with new models (e.g., Mistral, Llama) as they become available","Maintain API key security by storing credentials in VS Code's secrets API"],"best_for":["Teams managing AI API costs across multiple developers","Developers wanting to experiment with multiple AI models","Organizations with existing OpenAI or Anthropic contracts","Teams requiring model flexibility for compliance or performance reasons"],"limitations":["Model switching is global or per-capability — no per-file or per-project model selection","API key management is manual — no built-in key rotation or expiration handling","Model-specific features (e.g., vision in Claude 3.5 Sonnet) may not be available for all capabilities","Switching models mid-session may cause context incompatibilities (e.g., token limits differ)","No automatic fallback if primary model API is unavailable — requires manual reconfiguration"],"requires":["VS Code with Monica Code extension","API key for at least one supported model (OpenAI, Anthropic, or both)","Configuration access (VS Code settings or config.json)","Knowledge of model capabilities and pricing to make informed choices"],"input_types":["model selection (dropdown or config file)","API key (stored in VS Code secrets or plaintext config)","capability-specific configuration (prompt templates, model parameters)"],"output_types":["routed API requests (to selected model backend)","parsed responses (normalized across model differences)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_6","uri":"capability://code.generation.editing.language.agnostic.code.understanding.across.20.languages","name":"language-agnostic code understanding across 20+ languages","description":"Supports code generation, completion, and analysis across 20+ programming languages (Python, JavaScript, TypeScript, Java, Go, C++, Rust, C#, PHP, Ruby, Swift, Kotlin, Scala, Groovy, Clojure, Elixir, Erlang, Haskell, Lisp, Scheme, and others) by leveraging the AI model's inherent language knowledge rather than language-specific parsers. The extension detects the file language from the VS Code language mode and sends language context to the AI model, which adapts its output format and conventions accordingly. This approach trades off language-specific optimization (e.g., AST-based refactoring) for broad language coverage.","intents":["Get code suggestions and completions in any supported language without extension configuration","Refactor code in less common languages (e.g., Rust, Kotlin) using AI instead of language-specific tools","Understand code patterns across multiple languages in a polyglot project","Migrate code between languages with AI-assisted translation"],"best_for":["Polyglot teams working across multiple languages","Developers learning new languages and wanting AI-assisted coding","Teams using less common languages (Rust, Kotlin, Elixir) without mature IDE support","Organizations migrating between languages and needing translation assistance"],"limitations":["Language detection relies on VS Code's language mode — may be incorrect for ambiguous file types (e.g., .js could be Node.js or browser)","AI model knowledge of less common languages may be outdated or incomplete (e.g., Rust 2021 edition features)","No language-specific semantic analysis — refactoring may not respect language idioms or best practices","Syntax errors in generated code are more likely for niche languages due to lower training data","No built-in linting or type checking — generated code requires manual validation"],"requires":["VS Code with Monica Code extension","File in a supported language (20+ languages documented)","VS Code language mode correctly set for the file type","API credentials for OpenAI or Anthropic"],"input_types":["source code (any supported language)","language context (detected from VS Code mode)","user prompts (language-agnostic instructions)"],"output_types":["code suggestions (in the same language as input)","refactored code (preserving language conventions)","explanations (language-agnostic or language-specific)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_7","uri":"capability://tool.use.integration.freemium.trial.model.with.api.key.based.cost.control","name":"freemium trial model with api-key-based cost control","description":"Offers free trial access to Monica Code capabilities with limitations (trial duration and feature restrictions unknown), transitioning to a freemium model where developers provide their own API keys for OpenAI or Anthropic. This approach shifts API costs to the developer, allowing Monica to offer the extension free while monetizing through premium features or higher-tier support. The extension does not proxy requests through Monica's servers (implied by API key requirement), reducing Monica's infrastructure costs and enabling transparent cost tracking for developers.","intents":["Try Monica Code without upfront payment or credit card","Use Monica Code with existing OpenAI or Anthropic API subscriptions","Control AI costs by managing API key quotas and rate limits","Avoid vendor lock-in by using standard API keys instead of proprietary backends"],"best_for":["Solo developers and small teams with existing API subscriptions","Cost-conscious teams wanting to avoid per-seat licensing (e.g., Copilot Pro)","Organizations with OpenAI or Anthropic enterprise agreements","Developers wanting to evaluate AI code assistants before committing to paid tools"],"limitations":["Trial duration and feature restrictions are undocumented — unclear what limitations apply after trial expires","No built-in cost controls — developers must manage API quotas manually to avoid unexpected bills","API key security is developer's responsibility — no built-in key rotation or expiration","No usage analytics or cost tracking within Monica Code — developers must check OpenAI/Anthropic dashboards","Pricing is opaque — no clear tier structure or feature matrix for paid tiers (if they exist)"],"requires":["VS Code with Monica Code extension","OpenAI API key (for GPT-4o or ChatGPT API) or Anthropic API key (for Claude 3.5 Sonnet)","Active API subscription with available credits or quota","VS Code secrets API support (for secure key storage)"],"input_types":["API key (OpenAI or Anthropic)","trial activation (if applicable)","usage data (implicitly tracked by API providers)"],"output_types":["access to Monica Code capabilities","API usage logs (from OpenAI/Anthropic, not Monica)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_8","uri":"capability://text.generation.language.inline.code.explanation.and.documentation.generation","name":"inline code explanation and documentation generation","description":"Allows developers to select code snippets and request AI-generated explanations or documentation (docstrings, comments, README sections). The extension sends the selected code to the AI model with a prompt requesting explanation in natural language or a specific documentation format (JSDoc, Python docstrings, etc.). This capability helps developers understand unfamiliar code, generate missing documentation, and maintain code clarity without manual writing.","intents":["Understand what a complex function or class does without reading the code line-by-line","Generate docstrings or comments for undocumented code","Create README sections explaining architectural decisions or module purposes","Translate code intent into natural language for non-technical stakeholders"],"best_for":["Teams maintaining legacy code with missing documentation","Developers onboarding to unfamiliar codebases","Non-native English speakers needing help articulating code intent","Teams enforcing documentation standards (e.g., JSDoc for all functions)"],"limitations":["Explanation quality depends on code clarity — obfuscated or poorly-written code may generate confusing explanations","Generated documentation may be verbose or miss edge cases — requires manual review and editing","No semantic understanding of business logic — explanations may be technically correct but miss domain context","Documentation format must be specified in the prompt — no automatic format detection or enforcement","Large code blocks (>2000 tokens) may exceed API context limits or incur high costs"],"requires":["VS Code with Monica Code extension","Code snippet selected in the editor","API credentials for OpenAI or Anthropic","Optional: specific documentation format (JSDoc, Markdown, etc.) in the prompt"],"input_types":["source code (selected snippet)","documentation format preference (implicit or explicit in prompt)","language context (detected from file type)"],"output_types":["natural language explanation (text)","formatted documentation (docstrings, comments, Markdown)","multi-paragraph descriptions"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-monicaim-monica-code__cap_9","uri":"capability://text.generation.language.context.aware.code.review.and.quality.suggestions","name":"context-aware code review and quality suggestions","description":"Analyzes selected code or entire files and provides AI-driven code review feedback, including suggestions for performance improvements, security vulnerabilities, code style violations, and architectural issues. The extension sends code to the AI model with a review prompt, optionally including project context (language, framework, team standards), and returns structured feedback with explanations and suggested fixes. This capability complements automated linters by providing semantic and architectural insights that static analysis tools cannot detect.","intents":["Get code review feedback without waiting for human reviewers","Identify security vulnerabilities or performance anti-patterns in code","Enforce team coding standards and architectural patterns","Learn best practices by receiving explanations for suggested improvements"],"best_for":["Solo developers wanting peer review feedback","Teams with high code review backlogs","Organizations enforcing security or performance standards","Developers learning best practices in a new language or framework"],"limitations":["AI review may miss context-specific requirements (e.g., intentional performance tradeoffs for readability)","Suggestions may conflict with team standards if not configured with project context","No integration with code review platforms (GitHub, GitLab) — feedback is local to VS Code","Review quality depends on AI model knowledge — may miss domain-specific issues","No prioritization of issues — all suggestions are presented equally, making it hard to focus on critical problems"],"requires":["VS Code with Monica Code extension","Code snippet or file to review","API credentials for OpenAI (GPT-4o recommended) or Anthropic (Claude 3.5 Sonnet)","Optional: project context (language, framework, team standards) in the prompt"],"input_types":["source code (selected snippet or full file)","review criteria (implicit in AI model knowledge or explicit in prompt)","project context (language, framework, team standards)"],"output_types":["code review feedback (text with suggestions)","severity levels (implied or explicit)","suggested fixes (code snippets or descriptions)"],"categories":["text-generation-language","code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["VS Code (minimum version unknown, likely 1.60+)","API key for OpenAI (ChatGPT/GPT-4o) or Anthropic (Claude 3.5 Sonnet)","Active internet connection for API calls","File must be in a supported language (20+ languages documented)","VS Code with Monica Code extension installed","API credentials for OpenAI or Anthropic","Selected code snippet (minimum 1 character, practical limit ~4000 tokens)","VS Code with Monica Code extension","Code snippet or requirements description","Testing framework installed (Jest, pytest, JUnit, etc.)"],"failure_modes":["Completion latency depends on API response time — no local fallback for offline coding","Single-file context only; does not analyze cross-file imports or dependency graphs","No token budget enforcement — may consume API quota rapidly on large files","Completion quality varies by model; ChatGPT API baseline may be lower than GPT-4o","Selection-based scope means no automatic multi-file refactoring — each file must be edited separately","No undo/version history for generated code — relies on VS Code's undo stack","AI may generate syntactically valid but semantically incorrect code — requires manual review","Large selections (>4000 tokens) may exceed API context limits or incur high costs","Generated tests may have false positives or miss edge cases — require manual review and adjustment","Test quality depends on code clarity — poorly-written code may generate ineffective tests","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.51,"quality":0.34,"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":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:37.518Z","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=monica-code","compare_url":"https://unfragile.ai/compare?artifact=monica-code"}},"signature":"+jk881W3j/Rn8cNNvnGLxYe062vl4nk1JAA9a+cgvssnnakjNS7+wNPgJtuUplWWYMAaldQbPWbSfB0ominRDg==","signedAt":"2026-06-20T01:03:05.373Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/monica-code","artifact":"https://unfragile.ai/monica-code","verify":"https://unfragile.ai/api/v1/verify?slug=monica-code","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"}}