{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-rooveterinaryinc-roo-code-nightly","slug":"roo-code-nightly","name":"Roo Code Nightly","type":"agent","url":"https://marketplace.visualstudio.com/items?itemName=RooVeterinaryInc.roo-code-nightly","page_url":"https://unfragile.ai/roo-code-nightly","categories":["code-editors"],"tags":["agent","ai","autonomous","chatgpt","claude","cline","coding","dev","keybindings","llama","mcp","openrouter","roo code","roocode","sonnet"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_0","uri":"capability://code.generation.editing.multi.mode.ai.code.generation.with.contextual.specialization","name":"multi-mode ai code generation with contextual specialization","description":"Generates code from natural language prompts using mode-specific AI agents (Code, Architect, Ask, Debug, Custom) that tailor LLM behavior to different development tasks. Each mode pre-configures the system prompt and context window to optimize for specific workflows—Code mode for everyday edits, Architect mode for system design, Debug mode for issue isolation. The extension maintains conversation checkpoints, allowing users to navigate through prior generation states and iterate on outputs without losing context.","intents":["Generate boilerplate code from a natural language description in Code mode","Design system architecture and create migration specs in Architect mode","Get fast answers about codebase patterns in Ask mode","Trace and fix bugs by generating debug logs and test cases in Debug mode","Create specialized modes for team-specific coding standards or frameworks"],"best_for":["solo developers building features across multiple languages","teams standardizing on shared coding patterns via custom modes","architects planning system redesigns and migrations"],"limitations":["Mode switching requires manual selection—no automatic mode detection based on file type or task context","Checkpoint navigation limited to conversation history depth (unknown maximum)","Custom mode configuration syntax and validation not documented","Context window depends entirely on underlying LLM provider, not managed by extension"],"requires":["VS Code (version unknown, likely 1.80+)","API key for at least one supported LLM provider (OpenAI, Google Vertex AI, or compatible)","Active internet connection for LLM API calls"],"input_types":["natural language prompts","code snippets","file paths and project context"],"output_types":["generated code","refactored code","documentation","debug scripts"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_1","uri":"capability://code.generation.editing.codebase.aware.code.completion.and.refactoring.with.full.project.indexing","name":"codebase-aware code completion and refactoring with full project indexing","description":"Indexes the entire codebase to provide context-aware code completion and refactoring that understands project structure, naming conventions, and existing patterns. The extension builds an internal representation of the project (implementation details unknown) and uses this index to generate completions and suggest refactors that align with the codebase's architecture. Refactoring operations can span multiple files and preserve semantic meaning across the project.","intents":["Complete code with awareness of project-specific naming conventions and patterns","Refactor code across multiple files while maintaining consistency","Rename symbols and update all references automatically","Suggest refactors based on detected anti-patterns in the codebase"],"best_for":["developers working in large codebases with complex interdependencies","teams maintaining consistent code style across multiple modules","projects with domain-specific patterns or custom frameworks"],"limitations":["Codebase indexing performance on very large projects (>100k files) unknown","Index update frequency and staleness handling not documented","No explicit control over what gets indexed or exclusion patterns","Refactoring limited to files within the VS Code workspace"],"requires":["VS Code workspace with accessible file system","Sufficient disk space for codebase index (size unknown)","Project must be opened in VS Code"],"input_types":["code files in workspace","refactoring instructions in natural language"],"output_types":["code completions","refactored code","multi-file edit suggestions"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_10","uri":"capability://text.generation.language.documentation.generation.and.update.with.codebase.awareness","name":"documentation generation and update with codebase awareness","description":"Generates and updates project documentation (README, API docs, inline comments) based on codebase analysis and user instructions. The extension analyzes code structure, function signatures, and existing documentation to generate consistent, accurate documentation that reflects the actual codebase. Documentation can be generated for entire modules or specific functions, and updates can be applied across multiple files.","intents":["Generate a comprehensive README for a new project","Create API documentation from function signatures and docstrings","Update inline comments to reflect recent code changes","Generate architecture documentation from codebase structure"],"best_for":["teams maintaining documentation alongside code","projects with rapidly changing codebases where documentation falls out of sync","developers wanting to generate documentation without manual effort"],"limitations":["Documentation generation limited to code analysis—cannot infer business logic or user-facing behavior","Generated documentation may require manual review and editing","No integration with documentation tools (e.g., Sphinx, Javadoc)","Documentation updates may overwrite existing manual edits"],"requires":["Codebase with analyzable structure (functions, classes, modules)","Existing documentation or examples for style consistency"],"input_types":["code files","documentation instructions in natural language"],"output_types":["generated documentation files","updated comments and docstrings"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_11","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.specific.optimization","name":"multi-language code generation with language-specific optimization","description":"Supports code generation across multiple programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.) with language-specific optimizations for syntax, idioms, and best practices. The extension detects the target language from file extension or user specification and configures the AI agent with language-specific prompts and context. Generated code follows language conventions and integrates seamlessly with existing codebases.","intents":["Generate Python code that follows PEP 8 conventions","Create TypeScript code with proper type annotations","Generate idiomatic Rust code with memory safety considerations","Write code that matches the language and style of the existing codebase"],"best_for":["polyglot teams working across multiple languages","developers learning new languages and needing idiomatic code examples","projects with language-specific conventions or frameworks"],"limitations":["Language detection relies on file extension—may fail for ambiguous cases","Language-specific optimizations depend on LLM training data—newer languages may have limited support","No explicit validation that generated code follows language conventions","Code generation quality varies by language—popular languages (Python, JavaScript) likely better supported than niche languages"],"requires":["Target language specified via file extension or user instruction","LLM with training data for the target language"],"input_types":["natural language prompts","code snippets in target language"],"output_types":["generated code in target language"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_12","uri":"capability://code.generation.editing.real.time.code.editing.with.immediate.visual.feedback.in.editor","name":"real-time code editing with immediate visual feedback in editor","description":"Applies AI-generated code changes directly to the editor with real-time visual feedback, showing diffs and allowing users to accept, reject, or modify changes before committing. The extension integrates with VS Code's editor API to insert, replace, or delete code at specific locations, with changes reflected immediately in the editor. Users can review changes line-by-line and undo individual edits if needed.","intents":["See AI-generated code changes in the editor before committing","Accept or reject individual code changes from the AI agent","Modify AI-generated code and iterate on the result","Undo individual edits without losing the entire conversation context"],"best_for":["developers wanting to review and approve AI-generated changes before committing","teams with strict code review processes requiring human approval","users iterating on code generation with immediate visual feedback"],"limitations":["Diff visualization and change acceptance UI not documented","No explicit conflict detection if user edits overlap with AI-generated changes","Undo behavior depends on VS Code's undo stack—may be lost if user performs other operations","Real-time feedback latency depends on LLM response time and network conditions"],"requires":["VS Code with active editor","AI-generated code changes from the extension"],"input_types":["AI-generated code"],"output_types":["editor changes (insertions, replacements, deletions)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_13","uri":"capability://memory.knowledge.conversation.context.management.with.token.aware.summarization","name":"conversation context management with token-aware summarization","description":"Manages conversation context to stay within LLM token limits by automatically summarizing or truncating older conversation turns when approaching the context window limit. The extension tracks token usage across the conversation and codebase context, and implements strategies (e.g., summarization, selective context inclusion) to preserve recent context while staying within limits. Users can manually manage context via checkpoint navigation.","intents":["Maintain a long conversation without hitting token limits","Preserve recent context while summarizing older turns","Understand how much context is available for the next request","Manually trim context by navigating to a prior checkpoint"],"best_for":["developers having long conversations with the AI agent","teams working on large codebases with substantial context requirements","users wanting to understand token usage and context limits"],"limitations":["Token counting mechanism and summarization strategy not documented","Automatic summarization may lose important context from earlier turns","No explicit visibility into token usage or remaining context window","Context management depends on LLM provider's token limits—varies by model"],"requires":["Multi-turn conversation with the AI agent","LLM provider with documented token limits"],"input_types":["conversation history","codebase context"],"output_types":["summarized context","token usage information"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.orchestration.with.provider.agnostic.interface","name":"multi-provider llm orchestration with provider-agnostic interface","description":"Abstracts away provider-specific API differences by implementing a unified interface that routes requests to OpenAI, Google Vertex AI, or other compatible LLM providers. Users configure their preferred provider and model in settings, and the extension handles authentication, request formatting, and response parsing transparently. Supports switching providers without changing prompts or mode configurations, enabling cost optimization and model experimentation.","intents":["Switch between OpenAI GPT-5.5 and Google Vertex AI Claude Opus 4.7 based on cost or performance","Use local or self-hosted LLM providers via compatible APIs","Experiment with different models without reconfiguring modes or prompts","Manage multiple API keys for different providers"],"best_for":["teams optimizing LLM costs by comparing provider pricing","organizations with LLM provider contracts or compliance requirements","developers wanting to avoid vendor lock-in to a single LLM provider"],"limitations":["API key management mechanism not documented—likely stored in VS Code settings (plaintext or encrypted unknown)","Provider switching is manual; no automatic failover if primary provider is unavailable","Model-specific capabilities (e.g., vision, function calling) not abstracted—users must know provider limitations","Rate limiting and quota management delegated to provider; no built-in throttling or retry logic documented"],"requires":["API key for at least one supported LLM provider","Network connectivity to provider's API endpoints","VS Code settings configured with provider credentials"],"input_types":["provider configuration (API key, endpoint URL, model name)"],"output_types":["LLM responses routed from selected provider"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_3","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.tool.extension","name":"model context protocol (mcp) server integration for tool extension","description":"Integrates with MCP servers to extend the extension's capabilities beyond code generation and refactoring. MCP servers expose tools (e.g., web search, database queries, file operations) that the AI agent can invoke during task execution. The extension implements MCP client functionality, manages server lifecycle, and routes tool calls from the LLM to appropriate MCP servers, then feeds results back into the conversation context.","intents":["Enable the AI agent to search the web for documentation or examples during code generation","Query databases or APIs to fetch data for code generation or analysis","Execute custom tools or scripts as part of the AI workflow","Extend the agent's capabilities without modifying the extension code"],"best_for":["teams building custom tools that AI agents need to invoke (e.g., internal APIs, databases)","developers wanting to integrate external services (web search, analytics) into AI workflows","organizations standardizing on MCP for tool integration across AI applications"],"limitations":["MCP server configuration and lifecycle management not documented","Error handling and timeout behavior for MCP calls unknown","No built-in MCP server implementations—requires external servers","Tool call routing and result injection into context not fully documented","Security model for MCP server communication not specified (e.g., authentication, encryption)"],"requires":["MCP server(s) running and accessible to the extension","MCP server configuration in VS Code settings or extension config","Network connectivity to MCP servers"],"input_types":["MCP server configuration","tool definitions from MCP servers"],"output_types":["tool call results injected into conversation context"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_4","uri":"capability://text.generation.language.multi.turn.conversational.chat.with.checkpoint.based.state.navigation","name":"multi-turn conversational chat with checkpoint-based state navigation","description":"Maintains a multi-turn conversation history within a sidebar chat interface, where each turn represents a user prompt and AI response. The extension creates checkpoints at each turn, allowing users to navigate back to prior conversation states, branch from a checkpoint, and explore alternative code generation paths. Checkpoints preserve the full conversation context, codebase state, and generated code, enabling non-linear exploration of solutions.","intents":["Have a back-and-forth conversation with the AI agent to refine code generation","Navigate back to a prior conversation state and try a different approach","Compare multiple code generation paths by branching from a checkpoint","Maintain conversation context across multiple edits and file operations"],"best_for":["developers iterating on code generation through multiple refinement rounds","teams exploring multiple design alternatives before committing to one","users wanting to preserve and revisit prior conversation branches"],"limitations":["Maximum checkpoint history depth unknown—may be limited by memory or storage","Checkpoint branching UI/UX not documented","Checkpoint persistence across VS Code sessions unknown","No explicit checkpoint management (e.g., naming, deletion, export)","Context window resets if conversation exceeds LLM token limit"],"requires":["VS Code with Roo Code extension installed","Active LLM provider connection"],"input_types":["natural language prompts","code snippets for context"],"output_types":["conversational responses","generated or refactored code"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_5","uri":"capability://code.generation.editing.in.editor.code.debugging.with.ai.assisted.log.generation.and.root.cause.analysis","name":"in-editor code debugging with ai-assisted log generation and root cause analysis","description":"The Debug mode specializes the AI agent for troubleshooting by generating debug logs, test cases, and isolation strategies based on error descriptions or code snippets. The extension analyzes the provided code and error context, suggests strategic log placement, generates test cases to reproduce the issue, and proposes root cause hypotheses. Results are inserted into the editor for immediate execution and verification.","intents":["Generate debug logs at strategic points in code to trace execution flow","Create test cases that reproduce a reported bug","Analyze error messages and suggest root causes","Propose fixes based on identified root causes"],"best_for":["developers debugging complex issues in unfamiliar codebases","teams standardizing debugging approaches via custom Debug mode configurations","rapid prototyping where manual debugging is too slow"],"limitations":["Debug mode relies on code analysis alone—cannot execute code or inspect runtime state","Log placement suggestions may not be optimal for all debugging scenarios","No integration with VS Code debugger (breakpoints, watch expressions)","Root cause analysis limited to static code analysis and error message patterns"],"requires":["Error message or code snippet describing the issue","Relevant code context (file or codebase)"],"input_types":["error messages","code snippets","stack traces"],"output_types":["debug log statements","test cases","root cause hypotheses","proposed fixes"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_6","uri":"capability://search.retrieval.codebase.question.answering.with.ask.mode.for.fast.documentation.and.pattern.queries","name":"codebase question-answering with ask mode for fast documentation and pattern queries","description":"The Ask mode optimizes the AI agent for answering questions about the codebase with minimal latency. Users ask natural language questions about code patterns, architecture, or specific functions, and the extension retrieves relevant code context from the codebase index, synthesizes an answer, and provides code examples. Ask mode prioritizes speed and conciseness over detailed explanations.","intents":["Quickly find how a specific pattern is implemented in the codebase","Get an explanation of a function or module's purpose and usage","Understand the architecture and data flow of a system","Find examples of how to use a library or framework in the codebase"],"best_for":["developers onboarding to a new codebase and needing quick answers","teams using Ask mode as a lightweight documentation alternative","rapid prototyping where detailed explanations are unnecessary"],"limitations":["Ask mode optimizes for speed, potentially sacrificing explanation depth","Answers limited to codebase context—cannot provide external documentation or examples","No explicit ranking or relevance scoring for retrieved code snippets","Context window may be insufficient for large codebases with many matching patterns"],"requires":["Codebase indexed by the extension","Natural language question about the codebase"],"input_types":["natural language questions"],"output_types":["concise answers","code examples","file references"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_7","uri":"capability://planning.reasoning.architecture.and.system.design.planning.with.architect.mode","name":"architecture and system design planning with architect mode","description":"The Architect mode specializes the AI agent for high-level system design and planning tasks. Users describe architectural goals, constraints, or migration requirements, and the extension generates design specs, architecture diagrams (as text or code), migration plans, and technology recommendations. Architect mode emphasizes long-term maintainability, scalability, and team communication over immediate code generation.","intents":["Design a new system architecture based on requirements and constraints","Plan a migration from one technology stack to another","Create architecture decision records (ADRs) and design specs","Recommend technologies and patterns for a new project or feature"],"best_for":["architects and tech leads planning system redesigns","teams documenting architectural decisions and trade-offs","organizations standardizing on architectural patterns via custom Architect mode configs"],"limitations":["Architect mode generates text-based specs and recommendations—no visual diagram generation","Recommendations limited to patterns and technologies in the codebase or training data","No integration with architecture tools (e.g., C4 model, ArchiMate)","Specs may not account for organizational constraints or legacy system dependencies"],"requires":["Clear description of architectural goals and constraints","Relevant codebase context for technology recommendations"],"input_types":["natural language descriptions of architectural goals","existing codebase for context"],"output_types":["architecture specs","migration plans","technology recommendations","design decision records"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_8","uri":"capability://planning.reasoning.custom.mode.creation.for.team.specific.workflows.and.coding.standards","name":"custom mode creation for team-specific workflows and coding standards","description":"Allows teams to define custom AI agent modes tailored to their specific workflows, coding standards, or domain expertise. Custom modes are configured with specialized system prompts, context handling rules, and output formatting preferences. Once defined, custom modes appear alongside built-in modes (Code, Architect, Ask, Debug) and can be shared across the team via configuration files or documentation.","intents":["Create a specialized mode for a specific framework or technology stack (e.g., React, Django)","Define a mode that enforces team coding standards and conventions","Build a domain-specific mode for a particular type of task (e.g., API design, database schema)","Share standardized workflows across a team without requiring individual configuration"],"best_for":["teams with domain-specific coding standards or frameworks","organizations wanting to standardize AI-assisted workflows across developers","projects with unique architectural patterns or conventions"],"limitations":["Custom mode configuration syntax and validation not documented","No UI for creating custom modes—requires manual configuration file editing","Custom modes cannot override core extension behavior—limited to prompt customization","No versioning or rollback mechanism for custom mode configurations","Sharing custom modes across team requires manual distribution or version control"],"requires":["Understanding of custom mode configuration format (undocumented)","Access to VS Code settings or extension configuration files"],"input_types":["custom mode configuration (format unknown)"],"output_types":["specialized AI agent behavior"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-rooveterinaryinc-roo-code-nightly__cap_9","uri":"capability://code.generation.editing.file.operations.and.multi.file.editing.with.workspace.integration","name":"file operations and multi-file editing with workspace integration","description":"Integrates with VS Code's file system and workspace APIs to enable the AI agent to create, modify, and delete files across the project. The extension can generate code for multiple files, apply refactorings across the codebase, and manage file organization based on user instructions. File operations are tracked and can be undone via VS Code's undo mechanism or checkpoint navigation.","intents":["Generate multiple related files (e.g., component, test, styles) in a single operation","Refactor code across multiple files while maintaining consistency","Reorganize project structure based on architectural recommendations","Create boilerplate project structures (e.g., new module, API endpoint)"],"best_for":["developers generating multi-file features or modules","teams refactoring large codebases with many interdependent files","rapid prototyping where manual file creation is slow"],"limitations":["File operations limited to workspace—cannot access files outside the project","No explicit conflict detection or merge strategy for overlapping edits","Undo behavior depends on VS Code's undo stack—may be lost if user performs other operations","No transaction semantics—partial failures may leave the codebase in an inconsistent state"],"requires":["VS Code workspace with write access to files","File paths and content provided by the AI agent"],"input_types":["file creation/modification instructions in natural language"],"output_types":["created or modified files in the workspace"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["VS Code (version unknown, likely 1.80+)","API key for at least one supported LLM provider (OpenAI, Google Vertex AI, or compatible)","Active internet connection for LLM API calls","VS Code workspace with accessible file system","Sufficient disk space for codebase index (size unknown)","Project must be opened in VS Code","Codebase with analyzable structure (functions, classes, modules)","Existing documentation or examples for style consistency","Target language specified via file extension or user instruction","LLM with training data for the target language"],"failure_modes":["Mode switching requires manual selection—no automatic mode detection based on file type or task context","Checkpoint navigation limited to conversation history depth (unknown maximum)","Custom mode configuration syntax and validation not documented","Context window depends entirely on underlying LLM provider, not managed by extension","Codebase indexing performance on very large projects (>100k files) unknown","Index update frequency and staleness handling not documented","No explicit control over what gets indexed or exclusion patterns","Refactoring limited to files within the VS Code workspace","Documentation generation limited to code analysis—cannot infer business logic or user-facing behavior","Generated documentation may require manual review and editing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.54,"quality":0.35,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"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":"2026-05-03T15:20:35.026Z","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=roo-code-nightly","compare_url":"https://unfragile.ai/compare?artifact=roo-code-nightly"}},"signature":"5fvZdWoACOW3CmIAnyxH3Jzkhqxnbo9A5DmNq7K30oiOLkKXTNSDgtB2H9nWAqj2Ww/L4GPeXTF/C1T6NOFNDg==","signedAt":"2026-06-20T14:36:09.305Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/roo-code-nightly","artifact":"https://unfragile.ai/roo-code-nightly","verify":"https://unfragile.ai/api/v1/verify?slug=roo-code-nightly","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"}}