{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-paulorodrigues-your-copilot","slug":"your-copilot","name":"Your Copilot","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=PauloRodrigues.your-copilot","page_url":"https://unfragile.ai/your-copilot","categories":["code-editors"],"tags":["ai","artificial intelligence","code","coding","copilot","keybindings","machine learning","other","programming languages"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-paulorodrigues-your-copilot__cap_0","uri":"capability://tool.use.integration.openai.api.compatible.llm.server.integration.with.configurable.endpoints","name":"openai api-compatible llm server integration with configurable endpoints","description":"Enables connection to any self-hosted or third-party LLM server that implements the OpenAI API standard (e.g., LM Studio, Ollama, vLLM). The extension abstracts away server-specific implementation details by normalizing requests to the OpenAI API contract, allowing users to swap LLM backends without code changes. Configuration requires only a server URL (with http/https protocol) and optional API token, stored in VS Code settings.","intents":["I want to use my own locally-hosted LLM without sending code to cloud services","I need to switch between different LLM servers (Ollama, vLLM, LM Studio) without reconfiguring the extension","I want to use a private or custom LLM server behind my organization's firewall"],"best_for":["developers prioritizing data privacy and on-premises deployment","teams with existing self-hosted LLM infrastructure","builders experimenting with multiple open-source LLM backends"],"limitations":["Requires external LLM server to be running and network-accessible; extension cannot function offline","No built-in server health checks or automatic failover — connection failures silently degrade to no suggestions","Server URL must include protocol prefix (http:// or https://); malformed URLs not validated until first request","API token storage mechanism in VS Code settings is unencrypted at rest unless VS Code's credential store is explicitly configured"],"requires":["VS Code (minimum version unknown, not documented)","External LLM server running OpenAI API-compatible endpoint (LM Studio, Ollama, vLLM, or equivalent)","Network connectivity from VS Code host to LLM server","Optional: OpenAI API key if using official OpenAI API instead of self-hosted server"],"input_types":["server URL string","optional API token string","optional streaming toggle boolean"],"output_types":["HTTP requests to OpenAI-compatible endpoint","streaming or non-streaming text responses from LLM"],"categories":["tool-use-integration","api-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_1","uri":"capability://code.generation.editing.real.time.streaming.code.suggestions.with.optional.buffering","name":"real-time streaming code suggestions with optional buffering","description":"Streams LLM responses token-by-token directly into the editor as they are generated, providing immediate visual feedback without waiting for full response completion. The streaming feature is configurable and can be disabled if the LLM server doesn't support streaming or if performance overhead is unacceptable. Streaming is implemented via HTTP chunked transfer encoding to the OpenAI-compatible endpoint.","intents":["I want to see code suggestions appear incrementally as the LLM generates them, not wait for the full response","I need to disable streaming because my LLM server doesn't support it or it's causing performance issues","I want faster perceived response time by showing partial results immediately"],"best_for":["developers using fast local LLMs (Ollama, LM Studio) where streaming latency is minimal","users on high-latency connections who benefit from progressive rendering","teams debugging LLM server issues and needing to toggle streaming for troubleshooting"],"limitations":["Streaming can be disabled but no granular control over chunk size or buffering strategy","Performance impact is server-dependent; slow LLMs may show incomplete suggestions before user continues typing","No mechanism to cancel in-flight streaming requests if user changes mind mid-generation","Streaming overhead adds latency per token; exact impact depends on LLM server and network conditions"],"requires":["LLM server with streaming support (most OpenAI-compatible servers support this)","Optional: streaming toggle in VS Code settings to disable if needed"],"input_types":["code context from editor","user prompt or trigger"],"output_types":["streamed text tokens rendered incrementally in editor","complete code suggestion after stream ends"],"categories":["code-generation-editing","real-time-interaction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_2","uri":"capability://code.generation.editing.smart.file.context.awareness.with.implicit.file.mentioning","name":"smart file context awareness with implicit file mentioning","description":"Automatically includes the current active file's content and context in LLM requests without explicit user action. The extension infers which files are relevant to the current coding task and includes them in the prompt context sent to the LLM server. Implementation details of the 'smart' file selection algorithm are not documented, but the feature is described as enabling context-aware suggestions that reference the current file's code structure and semantics.","intents":["I want the LLM to understand my current file's context without manually copying code into the prompt","I need suggestions that reference variables, functions, and patterns already defined in my current file","I want the LLM to suggest code that matches the style and structure of my existing codebase"],"best_for":["developers working in single-file or tightly-coupled codebases where current file context is sufficient","users who want automatic context inclusion without manual prompt engineering","teams using consistent code style where LLM can infer patterns from current file"],"limitations":["Context scope is limited to current file; no cross-file or project-wide context awareness (planned RAG feature would address this)","Smart file selection algorithm is undocumented; users cannot control which files are included or excluded","No visibility into how much context is being sent; potential for exceeding LLM token limits on large files","File context is sent on every request, increasing network overhead and latency proportional to file size"],"requires":["Active file open in VS Code editor","File content must be accessible to the extension (no restrictions documented)"],"input_types":["current file content","cursor position or selection","user prompt or trigger"],"output_types":["augmented prompt sent to LLM server","context-aware code suggestions"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_3","uri":"capability://code.generation.editing.code.generation.from.natural.language.prompts.with.llm.dependent.quality","name":"code generation from natural language prompts with llm-dependent quality","description":"Accepts natural language descriptions or code comments and generates code suggestions by sending prompts to the configured LLM server. The extension acts as a thin client that marshals user intent into OpenAI API-compatible requests and renders the LLM's response back into the editor. Code quality and relevance are entirely dependent on the underlying LLM model's capabilities; the extension provides no post-processing, validation, or refinement of generated code.","intents":["I want to describe what code I need in English and have the LLM generate it for me","I want to convert code comments into working implementations","I want to quickly scaffold boilerplate or repetitive code patterns"],"best_for":["developers using capable LLMs (e.g., Mistral, Llama 2 70B) where code quality is acceptable","teams with custom fine-tuned models optimized for their codebase patterns","users prototyping or scaffolding code where perfect quality is not critical"],"limitations":["Code quality is entirely dependent on the chosen LLM model and server setup; no quality guarantees or validation","No syntax checking, type validation, or semantic analysis of generated code before insertion","Generated code may not follow project conventions, style guides, or best practices without explicit prompting","No built-in mechanism to refine or iterate on generated code; users must manually edit and re-prompt","LLM hallucinations (e.g., non-existent APIs, incorrect syntax) are not detected or warned about"],"requires":["LLM server configured and running","LLM model with reasonable code generation capability (quality varies widely)"],"input_types":["natural language prompt or code comment","optional: current file context"],"output_types":["generated code snippet","complete function or class definition"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_4","uri":"capability://automation.workflow.vs.code.extension.lifecycle.management.with.command.palette.integration","name":"vs code extension lifecycle management with command palette integration","description":"Integrates with VS Code's extension system to provide activation, configuration, and command execution through the command palette and settings UI. The extension registers commands (exact command names not documented) that users can invoke via Ctrl+Shift+P or bind to custom keybindings. Configuration is managed through VS Code's settings.json or UI, storing LLM server URL, API token, and streaming preference.","intents":["I want to configure the extension through VS Code's standard settings UI without editing JSON","I want to invoke code generation via keyboard shortcut or command palette","I want the extension to activate automatically when I open a workspace"],"best_for":["VS Code users familiar with extension configuration and command palette workflows","developers who want to customize keybindings for code generation triggers","teams standardizing on VS Code as the primary IDE"],"limitations":["Exact command names and keybindings are not documented; users must discover them through VS Code's command palette","No built-in UI for managing multiple LLM server configurations; users must manually edit settings.json for server switching","Settings are stored in VS Code's unencrypted settings.json by default; API tokens are not automatically encrypted","No extension-specific status bar indicators or UI elements documented; integration points with VS Code UI are unclear"],"requires":["VS Code (minimum version not specified)","Extension installed from VS Code Marketplace"],"input_types":["command palette invocation","settings.json configuration","custom keybinding"],"output_types":["extension activation","command execution","settings persistence"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_5","uri":"capability://code.generation.editing.planned.offline.tab.completion.with.language.specific.models","name":"planned: offline tab completion with language-specific models","description":"Upcoming feature (not yet implemented) that will provide fast, language-specific code completion without network requests by running lightweight models locally or caching completions. This feature is planned to enable low-latency, context-aware suggestions for common completion patterns (variable names, method calls, imports) without the overhead of sending requests to the LLM server. Implementation approach is not documented.","intents":["I want fast code completion that doesn't require network latency or LLM server availability","I want language-specific completion that understands syntax and semantics of my programming language","I want to use Your Copilot in offline environments or with unreliable network connectivity"],"best_for":["developers in offline or low-connectivity environments","users prioritizing completion latency over generation quality","teams with strict network policies that prevent external API calls"],"limitations":["Feature is planned but not yet implemented; no timeline or release date provided","Language-specific model selection and configuration approach is unknown","No documentation on which programming languages will be supported","Unclear whether offline completion will be mandatory or optional; may conflict with LLM server-based generation"],"requires":["Future version of Your Copilot with offline completion feature","Potentially: local language-specific model or cached completion data"],"input_types":["partial code or identifier prefix","current file context"],"output_types":["completion suggestions","ranked list of possible completions"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_6","uri":"capability://memory.knowledge.planned.retrieval.augmented.generation.rag.with.project.documentation.and.codebase.history","name":"planned: retrieval-augmented generation (rag) with project documentation and codebase history","description":"Upcoming feature (not yet implemented) that will augment LLM prompts with relevant project documentation and codebase history to improve suggestion accuracy and relevance. This feature would enable the LLM to reference project-specific patterns, APIs, and conventions without manual context inclusion. Implementation approach (vector embeddings, semantic search, indexing strategy) is not documented.","intents":["I want the LLM to understand my project's architecture and conventions without manually explaining them","I want suggestions that reference my project's documentation and existing code patterns","I want the LLM to generate code that's consistent with my project's style and best practices"],"best_for":["teams with large, complex codebases where context is critical","projects with extensive documentation that should inform code generation","developers working on long-lived projects with established patterns and conventions"],"limitations":["Feature is planned but not yet implemented; no timeline or release date provided","RAG implementation approach is unknown; unclear whether it will use vector embeddings, BM25, or other retrieval methods","No documentation on how project documentation will be indexed or updated","Unclear whether RAG will be optional or mandatory; may add latency to every request","No specification of supported documentation formats (Markdown, Docstrings, Javadoc, etc.)"],"requires":["Future version of Your Copilot with RAG feature","Project documentation in supported format","Potentially: vector database or semantic search infrastructure"],"input_types":["project documentation","codebase files and history","user prompt or code context"],"output_types":["augmented prompt with retrieved context","context-aware code suggestions"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_7","uri":"capability://planning.reasoning.planned.agentic.behavior.with.autonomous.refactoring.bug.detection.and.documentation.generation","name":"planned: agentic behavior with autonomous refactoring, bug detection, and documentation generation","description":"Upcoming feature (not yet implemented) that will enable the LLM to autonomously perform multi-step tasks such as refactoring code, detecting bugs, and generating documentation without explicit user prompts for each step. This feature would implement agentic workflows where the LLM can plan, execute, and validate changes across multiple files. Implementation approach (planning algorithms, state management, validation logic) is not documented.","intents":["I want the LLM to autonomously refactor my code to improve quality or performance","I want the LLM to detect bugs and suggest fixes without me manually reviewing each issue","I want the LLM to generate documentation for my code automatically"],"best_for":["teams with large codebases that benefit from automated refactoring","developers who want to offload repetitive code quality tasks to the LLM","projects with poor documentation that need automated generation"],"limitations":["Feature is planned but not yet implemented; no timeline or release date provided","Agentic behavior introduces risk of unintended code changes; no rollback or approval workflow documented","No specification of which refactoring patterns or bug types will be supported","Unclear whether agentic behavior will be scoped to single files or entire projects","No documentation on how the LLM will validate changes or handle failures during multi-step tasks"],"requires":["Future version of Your Copilot with agentic feature","LLM model capable of multi-step reasoning and planning","Potentially: code analysis tools for bug detection and validation"],"input_types":["code files","user request for refactoring, bug detection, or documentation"],"output_types":["refactored code","bug reports with suggested fixes","generated documentation"],"categories":["planning-reasoning","code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_8","uri":"capability://safety.moderation.no.data.storage.or.cloud.transmission.local.first.architecture","name":"no data storage or cloud transmission — local-first architecture","description":"The extension explicitly does not store or transmit user code to cloud services. All code context is sent only to the user-configured LLM server (which may be local or on-premises), and no data is retained by the extension after the request completes. This is a privacy-first design that contrasts with cloud-dependent copilots that store code snippets for analytics or model improvement.","intents":["I need to ensure my code never leaves my organization's network","I want to use an AI coding assistant without sharing code with third-party cloud services","I need compliance with data residency or privacy regulations that prohibit cloud code transmission"],"best_for":["enterprises with strict data governance and privacy requirements","teams working on proprietary or sensitive code that cannot be shared externally","organizations in regulated industries (finance, healthcare, government) with data residency requirements"],"limitations":["Data privacy is only guaranteed if the LLM server itself is local or on-premises; using a third-party OpenAI-compatible server may still transmit code externally","No audit trail or logging of what code was sent to the LLM server; users must implement their own monitoring","Extension does not verify that the configured LLM server is actually local or on-premises; users are responsible for server security","No built-in encryption for code transmission to the LLM server; users must configure TLS/HTTPS themselves"],"requires":["Local or on-premises LLM server (e.g., Ollama, LM Studio running locally)","Network isolation or firewall rules to prevent external data transmission"],"input_types":["code context from editor"],"output_types":["code sent to configured LLM server only"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-paulorodrigues-your-copilot__cap_9","uri":"capability://tool.use.integration.freemium.pricing.model.with.no.usage.limits.or.paid.tiers","name":"freemium pricing model with no usage limits or paid tiers","description":"The extension is available for free on the VS Code Marketplace with no usage limits, paid tiers, or subscription requirements. Users only pay for the LLM server infrastructure they choose to run (e.g., cloud compute for Ollama, local hardware for LM Studio). This pricing model eliminates per-request costs or seat-based licensing, making the extension cost-effective for teams with existing LLM infrastructure.","intents":["I want to use an AI coding assistant without paying per-request or per-seat fees","I want to avoid subscription lock-in and maintain control over my LLM infrastructure costs","I want to experiment with different LLM models without worrying about usage-based pricing"],"best_for":["teams with existing self-hosted LLM infrastructure (Ollama, LM Studio, vLLM)","developers who want to minimize ongoing costs for AI coding assistance","organizations that prefer open-source or self-hosted solutions over cloud services"],"limitations":["No commercial support or SLA; users are responsible for LLM server maintenance and uptime","LLM server costs are borne entirely by the user; no shared infrastructure or economies of scale","No usage analytics or billing dashboard; users must monitor their own LLM server costs","Free tier may not be sustainable long-term if the project requires significant maintenance or support"],"requires":["VS Code (free)","LLM server infrastructure (cost varies: free for open-source models, paid for cloud compute)"],"input_types":["none (pricing is not usage-based)"],"output_types":["free extension download and installation"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["VS Code (minimum version unknown, not documented)","External LLM server running OpenAI API-compatible endpoint (LM Studio, Ollama, vLLM, or equivalent)","Network connectivity from VS Code host to LLM server","Optional: OpenAI API key if using official OpenAI API instead of self-hosted server","LLM server with streaming support (most OpenAI-compatible servers support this)","Optional: streaming toggle in VS Code settings to disable if needed","Active file open in VS Code editor","File content must be accessible to the extension (no restrictions documented)","LLM server configured and running","LLM model with reasonable code generation capability (quality varies widely)"],"failure_modes":["Requires external LLM server to be running and network-accessible; extension cannot function offline","No built-in server health checks or automatic failover — connection failures silently degrade to no suggestions","Server URL must include protocol prefix (http:// or https://); malformed URLs not validated until first request","API token storage mechanism in VS Code settings is unencrypted at rest unless VS Code's credential store is explicitly configured","Streaming can be disabled but no granular control over chunk size or buffering strategy","Performance impact is server-dependent; slow LLMs may show incomplete suggestions before user continues typing","No mechanism to cancel in-flight streaming requests if user changes mind mid-generation","Streaming overhead adds latency per token; exact impact depends on LLM server and network conditions","Context scope is limited to current file; no cross-file or project-wide context awareness (planned RAG feature would address this)","Smart file selection algorithm is undocumented; users cannot control which files are included or excluded","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.26,"quality":0.3,"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":"2026-05-03T15:20:31.090Z","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=your-copilot","compare_url":"https://unfragile.ai/compare?artifact=your-copilot"}},"signature":"I+nl78IhiN6pQP41lW4RLK/BA9FlN3RKVd+mWBrLPMUBQPNaufzrjavmDKvdJftI8L3Kg8nq950i96pgH28JAQ==","signedAt":"2026-06-22T01:07:41.032Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/your-copilot","artifact":"https://unfragile.ai/your-copilot","verify":"https://unfragile.ai/api/v1/verify?slug=your-copilot","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"}}