{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-aryansrao-deekseek-extension","slug":"deepseek-extension","name":"DeepSeek extension","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=aryansrao.deekseek-extension","page_url":"https://unfragile.ai/deepseek-extension","categories":["code-editors"],"tags":["ai","chat","code-assistant","code-completion","deepseek","local-ai","ollama"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-aryansrao-deekseek-extension__cap_0","uri":"capability://code.generation.editing.local.context.aware.code.generation.via.deepseek.r1","name":"local-context-aware code generation via deepseek-r1","description":"Generates code snippets and complete functions by sending the current file context to a locally-running DeepSeek-R1 model via Ollama's HTTP API (default endpoint http://localhost:11434). The extension captures the active editor buffer and passes it as context to the model, which performs inference on the user's machine without cloud transmission. Responses are streamed back into the editor or displayed in the chat sidebar.","intents":["I want to generate a function that fits the patterns and style of my current file without sending my code to the cloud","I need a code snippet for a common task and want suggestions based on my existing codebase context","I want to auto-complete a partial function or class definition using local AI inference"],"best_for":["solo developers and small teams with privacy-sensitive codebases","developers working on proprietary or regulated code who cannot use cloud-based AI assistants","teams with strong local compute resources (GPU/CPU) who want to avoid API costs"],"limitations":["Inference speed depends entirely on local hardware — no GPU acceleration documented, so generation may take 5-30+ seconds per request on CPU-only machines","No multi-file or workspace-wide context awareness documented — only current file is sent to the model","Model must be pre-downloaded via Ollama CLI (deepseek-r1 is ~7GB), requiring significant disk space and initial setup time","No streaming token limits or timeout handling documented — long generations may block VS Code UI if not properly async"],"requires":["VS Code 1.96 or higher","Ollama installed and running (https://ollama.com)","DeepSeek-R1 model pre-downloaded via 'ollama run deepseek-r1'","Sufficient local disk space (~7GB for model weights)","Local machine with CPU or GPU capable of running inference"],"input_types":["current editor file content (text/code)","user natural language prompt via chat sidebar or command palette"],"output_types":["generated code (text/code)","inline suggestions in editor or chat sidebar response"],"categories":["code-generation-editing","local-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aryansrao-deekseek-extension__cap_1","uri":"capability://text.generation.language.interactive.code.chat.with.file.context","name":"interactive code chat with file context","description":"Provides a sidebar chat interface (accessed via Command Palette 'start' command) where developers can ask questions about their code in natural language. The extension maintains a conversation history within the chat panel and passes the current file context along with each user message to the local DeepSeek-R1 model. Responses are displayed in the chat UI, allowing iterative Q&A without re-selecting code or switching windows.","intents":["I want to ask questions about what my code does and get explanations without leaving the editor","I need to understand a complex function or algorithm by asking the AI to explain it step-by-step","I want to have a back-and-forth conversation about code improvements or refactoring options"],"best_for":["developers learning unfamiliar codebases or languages","teams doing code reviews who want AI-assisted explanation of complex logic","solo developers who want an always-available coding mentor without context switching"],"limitations":["Chat history is not persisted between VS Code sessions — closing the extension or restarting VS Code clears conversation context","Only current file context is available — cannot reference multiple files or project-wide patterns in a single query","No ability to edit code directly from chat responses — suggestions must be manually copied or applied","Model response quality depends on local hardware; complex reasoning tasks may timeout or produce incomplete answers on CPU-only machines"],"requires":["VS Code 1.96 or higher","Ollama running with DeepSeek-R1 model loaded","Active editor with code file open"],"input_types":["natural language text (user question)","current file context (code)"],"output_types":["natural language explanation (text)","code snippets or suggestions (text/code)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aryansrao-deekseek-extension__cap_2","uri":"capability://code.generation.editing.automated.code.documentation.generation","name":"automated code documentation generation","description":"Analyzes the current file or selected code snippet and generates documentation comments (JSDoc, docstrings, etc.) by passing the code to DeepSeek-R1 running locally. The extension infers the appropriate documentation format based on the detected language and inserts generated comments above functions, classes, or methods. Documentation includes parameter descriptions, return types, and usage examples where applicable.","intents":["I want to auto-generate JSDoc comments for all functions in my file without manually writing them","I need to document a complex function quickly and want AI to infer parameter and return descriptions","I want to ensure my codebase has consistent documentation style across multiple files"],"best_for":["teams enforcing documentation standards but lacking time for manual documentation","open-source maintainers who want to improve code readability for contributors","developers refactoring legacy code and adding documentation retroactively"],"limitations":["Generated documentation may be inaccurate or incomplete if function logic is complex or non-obvious — requires manual review before committing","No language-specific documentation format detection documented — may generate incorrect comment syntax for non-standard languages","Cannot insert documentation into already-documented code without overwriting existing comments — no merge or diff capability","Inference latency means generating docs for large files (1000+ lines) may take several minutes on CPU-only hardware"],"requires":["VS Code 1.96 or higher","Ollama with DeepSeek-R1 model running","Code file in a supported language (JavaScript, Python, Java, C++, etc.)"],"input_types":["source code (text/code)","optional user prompt specifying documentation style or detail level"],"output_types":["documentation comments (text/code)","inserted into editor at function/class definitions"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aryansrao-deekseek-extension__cap_3","uri":"capability://code.generation.editing.local.debugging.assistance.with.error.context","name":"local debugging assistance with error context","description":"Accepts error messages, stack traces, or buggy code snippets and uses the local DeepSeek-R1 model to identify root causes and suggest fixes. The extension can be invoked via chat to paste an error message or select problematic code, then returns debugging suggestions including potential causes, code patches, and prevention strategies. All analysis happens locally without sending error data to external services.","intents":["I have a runtime error and want the AI to explain what went wrong and suggest a fix","I want to understand why my code is producing unexpected output and get debugging strategies","I need to fix a bug quickly and want AI-assisted suggestions before diving into a debugger"],"best_for":["developers debugging unfamiliar code or frameworks","teams without dedicated QA resources who want AI-assisted bug triage","learners who want to understand common programming mistakes and fixes"],"limitations":["Debugging accuracy depends on the completeness of error context provided — truncated stack traces or missing variable state may lead to incorrect suggestions","Cannot execute code or inspect runtime state — only static analysis of provided code and error messages","No integration with VS Code's native debugger — cannot set breakpoints or step through code based on AI suggestions","May suggest fixes that don't address the root cause if the error is in a dependency or external library not visible in the provided context"],"requires":["VS Code 1.96 or higher","Ollama with DeepSeek-R1 running","Error message or code snippet to analyze"],"input_types":["error message or stack trace (text)","buggy code snippet (text/code)","optional context about expected behavior (natural language)"],"output_types":["debugging suggestions (text)","code patches or fixes (text/code)","explanation of root cause (text)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aryansrao-deekseek-extension__cap_4","uri":"capability://code.generation.editing.coding.best.practices.and.pattern.recommendations","name":"coding best practices and pattern recommendations","description":"Analyzes the current file or selected code and suggests improvements based on language-specific best practices, design patterns, and performance optimizations. The extension sends code to the local DeepSeek-R1 model, which identifies anti-patterns, suggests refactoring opportunities, and recommends idiomatic language constructs. Suggestions are presented in the chat interface with explanations and optional code examples.","intents":["I want to improve my code quality and learn best practices for the language I'm using","I need to refactor legacy code and want AI suggestions for modernizing it","I want to understand why a particular coding pattern is considered an anti-pattern and how to fix it"],"best_for":["junior developers learning language idioms and best practices","teams establishing code quality standards and wanting automated guidance","developers refactoring legacy codebases and seeking modernization suggestions"],"limitations":["Recommendations are generic and may not account for project-specific constraints, performance requirements, or architectural decisions","No integration with linters or static analysis tools — suggestions are independent of existing code quality checks","Cannot automatically apply suggestions — all refactoring must be done manually by the developer","May suggest patterns that conflict with team conventions or existing codebase style if not explicitly provided as context"],"requires":["VS Code 1.96 or higher","Ollama with DeepSeek-R1 running","Code file or snippet to analyze"],"input_types":["source code (text/code)","optional context about project constraints or style guidelines (natural language)"],"output_types":["best practice recommendations (text)","refactoring suggestions with code examples (text/code)","explanations of why patterns are preferred (text)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aryansrao-deekseek-extension__cap_5","uri":"capability://tool.use.integration.command.palette.triggered.code.assistance","name":"command-palette-triggered code assistance","description":"Exposes AI capabilities through VS Code's Command Palette (Cmd/Ctrl + Shift + P) with a 'start' command that launches the chat interface. This integration allows developers to invoke the extension without mouse interaction, maintaining keyboard-driven workflow. The command palette entry is the primary discovery and activation mechanism for the extension's features.","intents":["I want to quickly access the AI assistant without using the mouse or navigating menus","I want to discover available AI features through VS Code's standard command interface","I want to integrate AI assistance into my existing keyboard-driven VS Code workflow"],"best_for":["power users and developers who rely on keyboard shortcuts for efficiency","teams with strict accessibility requirements","developers working in terminal-heavy or headless VS Code environments"],"limitations":["Only 'start' command is documented — no granular commands for specific capabilities (e.g., 'generate docs', 'debug error')","No custom keybindings documented — users must type 'start' each time or manually configure keybindings","Command palette discoverability is weak for new users unfamiliar with VS Code — no in-editor prompts or tooltips documented","No command history or recent commands feature documented — repeated invocations require full command typing"],"requires":["VS Code 1.96 or higher","Extension installed and enabled"],"input_types":["command palette text input (user types 'start')"],"output_types":["chat sidebar panel opens"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aryansrao-deekseek-extension__cap_6","uri":"capability://tool.use.integration.ollama.based.model.abstraction.and.local.execution","name":"ollama-based model abstraction and local execution","description":"Abstracts the complexity of running large language models locally by delegating inference to Ollama, a lightweight framework for running LLMs on consumer hardware. The extension communicates with Ollama's HTTP API (default http://localhost:11434) to send prompts and receive completions. This abstraction allows the extension to support any model available in the Ollama library without code changes, though currently only DeepSeek-R1 is documented as supported.","intents":["I want to run a powerful AI model locally without setting up complex ML infrastructure","I want to ensure my code and data never leave my machine while using AI assistance","I want to avoid cloud API costs and rate limits by running inference on my own hardware"],"best_for":["developers with privacy-critical codebases (healthcare, finance, government)","teams in regions with strict data residency requirements","developers with powerful local hardware (GPU) who want to avoid cloud API costs"],"limitations":["Ollama must be installed and running as a separate process — adds operational complexity and requires user to manage model lifecycle","Inference speed is hardware-dependent — CPU-only machines may see 5-30+ second latencies per request, making interactive use frustrating","No GPU acceleration documented — extension may not leverage CUDA, Metal, or ROCm even if available on the user's machine","Model switching requires manual Ollama CLI commands ('ollama run model-name') — no UI for model management within VS Code","No fallback to cloud API if local inference fails — extension will simply error rather than gracefully degrade"],"requires":["Ollama installed (https://ollama.com)","Ollama running and accessible at http://localhost:11434 (or custom endpoint if configured)","DeepSeek-R1 model pre-downloaded via 'ollama run deepseek-r1' (~7GB disk space)","Local machine with sufficient CPU/GPU to run inference"],"input_types":["prompts and code context (text)"],"output_types":["model completions and responses (text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aryansrao-deekseek-extension__cap_7","uri":"capability://text.generation.language.sidebar.chat.panel.with.streaming.responses","name":"sidebar chat panel with streaming responses","description":"Renders a persistent chat interface in the VS Code sidebar that displays conversation history and streams model responses in real-time. The panel maintains state during a VS Code session and updates incrementally as the DeepSeek-R1 model generates tokens, providing visual feedback that inference is in progress. Users can scroll through previous messages and continue conversations without losing context.","intents":["I want a dedicated UI for interacting with the AI without switching windows or losing my place in the code","I want to see the AI thinking in real-time as it generates responses, not wait for a complete response","I want to maintain a conversation history within my current VS Code session for reference"],"best_for":["developers who prefer chat-based interaction over inline suggestions","teams using pair programming or code review workflows where chat context is valuable","developers working on complex problems that require iterative Q&A"],"limitations":["Chat history is ephemeral — closing VS Code or restarting the extension clears all conversation history (no persistence to disk)","Streaming responses may appear slow on CPU-only hardware, creating perception of unresponsiveness","No ability to export or save chat conversations for documentation or knowledge sharing","Sidebar real estate is limited — long conversations may require excessive scrolling","No markdown rendering documented — responses may display raw markdown syntax instead of formatted text"],"requires":["VS Code 1.96 or higher","Ollama with DeepSeek-R1 running","Sidebar visible in VS Code layout"],"input_types":["natural language text (user messages)","optional code context from current editor"],"output_types":["streamed text responses (displayed in chat panel)","code snippets (text/code)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"moderate","permissions":["VS Code 1.96 or higher","Ollama installed and running (https://ollama.com)","DeepSeek-R1 model pre-downloaded via 'ollama run deepseek-r1'","Sufficient local disk space (~7GB for model weights)","Local machine with CPU or GPU capable of running inference","Ollama running with DeepSeek-R1 model loaded","Active editor with code file open","Ollama with DeepSeek-R1 model running","Code file in a supported language (JavaScript, Python, Java, C++, etc.)","Ollama with DeepSeek-R1 running"],"failure_modes":["Inference speed depends entirely on local hardware — no GPU acceleration documented, so generation may take 5-30+ seconds per request on CPU-only machines","No multi-file or workspace-wide context awareness documented — only current file is sent to the model","Model must be pre-downloaded via Ollama CLI (deepseek-r1 is ~7GB), requiring significant disk space and initial setup time","No streaming token limits or timeout handling documented — long generations may block VS Code UI if not properly async","Chat history is not persisted between VS Code sessions — closing the extension or restarting VS Code clears conversation context","Only current file context is available — cannot reference multiple files or project-wide patterns in a single query","No ability to edit code directly from chat responses — suggestions must be manually copied or applied","Model response quality depends on local hardware; complex reasoning tasks may timeout or produce incomplete answers on CPU-only machines","Generated documentation may be inaccurate or incomplete if function logic is complex or non-obvious — requires manual review before committing","No language-specific documentation format detection documented — may generate incorrect comment syntax for non-standard languages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44,"quality":0.26,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","last_scraped_at":"2026-05-03T15:20:42.146Z","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=deepseek-extension","compare_url":"https://unfragile.ai/compare?artifact=deepseek-extension"}},"signature":"4AxaS6+0Ie+8dIxR5e5YDLHCrslGloU/+SbjEaXQh5KqmM4wuZc8Y2g+y8j6fPg1ChYuK63R5QloFTioViaOBA==","signedAt":"2026-06-21T10:41:04.835Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-extension","artifact":"https://unfragile.ai/deepseek-extension","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-extension","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"}}