{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-huggingface-huggingface-vscode","slug":"llm-vscode","name":"llm-vscode","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=HuggingFace.huggingface-vscode","page_url":"https://unfragile.ai/llm-vscode","categories":["code-editors"],"tags":["ai","assistant","code","development","huggingface","keybindings","llm"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-huggingface-huggingface-vscode__cap_0","uri":"capability://code.generation.editing.context.aware.inline.code.completion.with.ghost.text.ui","name":"context-aware inline code completion with ghost-text ui","description":"Generates code suggestions in real-time as developers type by sending the current file's prefix and suffix context (relative to cursor position) to a configurable LLM backend (Hugging Face Inference API, Ollama, OpenAI, or TGI). The extension automatically tokenizes input using the tokenizers library to fit within the model's context window, constructs a prompt with special tokens (start_token, end_token, middle_token), and renders completions as ghost-text overlays matching VS Code's native completion UI pattern. Supports multiple model backends without leaving the editor.","intents":["Generate code suggestions while typing without context switching","Switch between different LLM backends (cloud vs local) without reconfiguring","Complete code with awareness of surrounding context in the current file","Use open-source models like StarCoder instead of proprietary alternatives"],"best_for":["Solo developers and small teams using open-source LLMs","Developers preferring local inference (Ollama) over cloud APIs","Teams evaluating Hugging Face models for code generation","Developers wanting cost-controlled completion via self-hosted TGI"],"limitations":["No multi-file context awareness — only current file prefix/suffix is sent to the model","Context window automatically truncated to fit model limits, potentially losing surrounding code","Network latency from HTTP requests to external backends (Inference API, Ollama) adds completion delay","Free tier Hugging Face Inference API has rate limits; PRO plan recommended for production use","No streaming response support documented — full completion must be generated before display","Tokenization overhead via tokenizers library adds computational cost per completion request"],"requires":["VS Code (minimum version unknown)","Hugging Face API token (auto-detected from huggingface-cli cache or manual entry via 'Llm: Login' command)","For Ollama backend: local Ollama instance running and accessible","For TGI backend: local Text Generation Inference service running","For OpenAI backend: OpenAI-compatible API endpoint and API key"],"input_types":["source code (current file prefix and suffix relative to cursor)","cursor position (exact character offset)","model configuration (modelId, backend type, request body parameters)"],"output_types":["generated code text (ghost-text completion)","completion metadata (confidence, token count, backend used)"],"categories":["code-generation-editing","editor-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-huggingface-huggingface-vscode__cap_1","uri":"capability://safety.moderation.code.attribution.checking.via.bloom.filter.matching.against.the.stack.dataset","name":"code attribution checking via bloom filter matching against the stack dataset","description":"Detects whether generated code matches sequences in The Stack training dataset by performing a rapid first-pass Bloom filter lookup against a pre-built index, then optionally linking to stack.dataportraits.org for detailed attribution verification. The extension requires a minimum 50-character code sequence and sufficient surrounding context to perform matching. Triggered via the 'Cmd+Shift+A' keyboard shortcut or command palette. Uses probabilistic matching (Bloom filter) for speed, with acknowledged false positives.","intents":["Check if generated code was directly copied from The Stack training data","Verify code attribution and understand training data provenance","Identify potential licensing or plagiarism concerns before committing code","Link to detailed attribution information for code sequences"],"best_for":["Developers concerned about training data contamination and code provenance","Teams with strict IP policies requiring attribution verification","Open-source maintainers auditing generated code for licensing compliance","Researchers studying code generation model behavior and training data leakage"],"limitations":["Bloom filter-based matching produces false positives — not exact matching","Minimum 50-character sequence requirement means short code snippets cannot be checked","Requires sufficient surrounding context for accurate matching (exact threshold unknown)","First-pass check only — full verification requires separate Stack search tool","No offline mode — requires network access to stack.dataportraits.org","Attribution check latency depends on network and Bloom filter lookup performance (metrics unknown)"],"requires":["VS Code (minimum version unknown)","Network access to stack.dataportraits.org","Code selection or cursor position in editor","Minimum 50-character code sequence for reliable matching"],"input_types":["source code text (50+ characters)","surrounding context (prefix and suffix code)"],"output_types":["attribution match result (matched/not matched)","link to stack.dataportraits.org for detailed verification","match confidence or Bloom filter hit indicator"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-huggingface-huggingface-vscode__cap_2","uri":"capability://tool.use.integration.multi.backend.model.switching.with.unified.configuration","name":"multi-backend model switching with unified configuration","description":"Allows developers to select and switch between 4 different LLM backend types (Hugging Face Inference API, Ollama, OpenAI-compatible, Text Generation Inference) via VS Code settings without modifying code or restarting the extension. Each backend has configurable parameters: base URL, model ID, and custom request body JSON. The extension constructs HTTP POST requests with backend-specific URL patterns and forwards the configured requestBody to the selected endpoint. Supports automatic token counting to fit prompts within each model's context window.","intents":["Switch between cloud and local inference backends based on privacy or cost requirements","Use different models (StarCoder, Llama, CodeLlama) without changing extension code","Configure custom endpoints for self-hosted or enterprise LLM services","Test multiple backends to compare completion quality and latency"],"best_for":["Teams evaluating multiple LLM backends for code generation","Enterprises with self-hosted LLM infrastructure (Ollama, TGI)","Developers prioritizing privacy (local Ollama) over cloud convenience","Organizations with existing OpenAI or compatible API investments"],"limitations":["Backend configuration requires manual entry in VS Code settings — no UI wizard","URL construction patterns are backend-specific and not fully documented (docs cut off)","Switching backends requires reloading the extension or restarting VS Code (exact behavior unknown)","No built-in health checks or fallback mechanisms if a backend becomes unavailable","Ollama and TGI backends require local services running — adds infrastructure complexity","Request body customization requires JSON knowledge — no validation or schema guidance provided"],"requires":["VS Code (minimum version unknown)","For Hugging Face backend: Hugging Face API token and internet connection","For Ollama backend: Ollama installed and running locally (any version compatible with HTTP API)","For OpenAI backend: OpenAI API key or compatible endpoint URL","For TGI backend: Text Generation Inference service running and accessible","Knowledge of model IDs and backend-specific configuration parameters"],"input_types":["backend type selection (huggingface, ollama, openai, tgi)","model ID (e.g., 'bigcode/starcoder')","base URL (for self-hosted backends)","custom request body JSON (optional)"],"output_types":["active backend configuration","model metadata (context window size, token limits)","backend health status (unknown if implemented)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-huggingface-huggingface-vscode__cap_3","uri":"capability://code.generation.editing.automatic.context.window.fitting.with.tokenizer.based.prompt.truncation","name":"automatic context window fitting with tokenizer-based prompt truncation","description":"Automatically measures and fits the code completion prompt within each model's context window by using the tokenizers library to count tokens in the prefix, suffix, and surrounding code. If the combined prompt exceeds the model's maximum context length, the extension truncates the prefix and/or suffix to fit. This ensures requests succeed without manual context management by the developer. Token counting happens per-request with computational overhead.","intents":["Generate completions for large files without manual context management","Avoid 'context too long' errors from LLM backends","Automatically adapt to different model context window sizes","Maintain completion quality by preserving the most relevant context"],"best_for":["Developers working on large codebases with long files","Teams using models with varying context window sizes","Developers who want automatic context management without configuration"],"limitations":["Truncation strategy is not documented — unclear if prefix or suffix is prioritized","Token counting adds computational overhead per completion request (metrics unknown)","No control over truncation behavior — developers cannot customize which context to preserve","Tokenizer library version and accuracy not specified","May lose important context if file is very large relative to model's context window","No visibility into how much context was truncated or why"],"requires":["VS Code (minimum version unknown)","tokenizers library (Python-based, integration method unknown)","Model context window size metadata (must be configured per model)"],"input_types":["source code prefix (before cursor)","source code suffix (after cursor)","surrounding context code","model context window size (from configuration)"],"output_types":["truncated prompt text","token count (visible or internal)","truncation metadata (amount removed, strategy used)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-huggingface-huggingface-vscode__cap_4","uri":"capability://automation.workflow.vs.code.command.palette.and.keyboard.shortcut.integration","name":"vs code command palette and keyboard shortcut integration","description":"Exposes core extension functionality through VS Code's command palette (Cmd/Ctrl+Shift+P) and dedicated keyboard shortcuts. Documented commands include 'Llm: Login' for authentication and 'Llm: Code Attribution Check' (Cmd+Shift+A). The extension registers these commands with VS Code's command registry, making them discoverable and remappable. Additional commands exist but are not enumerated in available documentation.","intents":["Authenticate with Hugging Face API without opening settings","Trigger code attribution checks with a single keyboard shortcut","Discover available extension commands through the command palette","Customize keyboard shortcuts to match personal workflow preferences"],"best_for":["Developers preferring keyboard-driven workflows","Teams with custom keybinding standards","Users who want quick access to extension features without menu navigation"],"limitations":["Full command list is not documented — only 'Llm: Login' and attribution check are mentioned","Keybinding customization requires manual VS Code keybindings.json editing","No command palette search filtering or categorization visible","Command discovery requires knowing command names or browsing documentation"],"requires":["VS Code (minimum version unknown)","Knowledge of command names or access to documentation"],"input_types":["command name (string)","keyboard input (for shortcut triggers)"],"output_types":["command execution result (authentication, attribution check, etc.)","UI feedback (dialogs, notifications, inline results)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-huggingface-huggingface-vscode__cap_5","uri":"capability://tool.use.integration.hugging.face.api.token.management.with.auto.detection.and.manual.entry","name":"hugging face api token management with auto-detection and manual entry","description":"Manages Hugging Face API authentication by automatically detecting tokens from the huggingface-cli cache on disk (if huggingface-cli was previously configured) or accepting manual token entry via the 'Llm: Login' command. Tokens are stored in VS Code's secure credential storage (mechanism not specified). The extension validates tokens before making API requests to the Hugging Face Inference API. Tokens can be obtained from hf.co/settings/token.","intents":["Authenticate with Hugging Face Inference API without manual token entry","Securely store and manage API credentials within VS Code","Reuse existing huggingface-cli authentication if already configured","Update or change API tokens without reconfiguring the extension"],"best_for":["Developers already using Hugging Face CLI tools","Teams managing multiple Hugging Face accounts","Users prioritizing secure credential storage over manual configuration"],"limitations":["Auto-detection only works if huggingface-cli was previously installed and configured","Token storage mechanism not documented — unclear if VS Code's built-in secret storage or file-based","No token validation or expiration checking documented","Free tier Hugging Face Inference API has rate limits — PRO plan recommended for production","No support for token rotation or refresh mechanisms","Manual token entry requires copying from hf.co/settings/token — no in-extension token generation"],"requires":["Hugging Face account (free or PRO)","API token from hf.co/settings/token","For auto-detection: huggingface-cli installed and previously configured","VS Code (minimum version unknown)"],"input_types":["API token (string, 40+ characters)","huggingface-cli cache (if auto-detecting)"],"output_types":["authentication status (authenticated/not authenticated)","token validation result","API rate limit information (if available)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-huggingface-huggingface-vscode__cap_6","uri":"capability://automation.workflow.vs.code.settings.panel.configuration.with.llm.filter","name":"vs code settings panel configuration with llm filter","description":"Exposes extension configuration through VS Code's standard settings UI (Cmd+, → filter 'Llm'). Developers can configure backend type, model ID, base URLs, request body parameters, and other options via a searchable settings panel. The full list of available configuration options is not enumerated in documentation. Settings are persisted in VS Code's configuration store and applied immediately or after extension reload.","intents":["Configure backend type and model without editing JSON files","Customize request parameters for specific LLM backends","Search for extension settings using the 'Llm' filter","Persist configuration across VS Code sessions"],"best_for":["Developers preferring UI-based configuration over JSON editing","Teams with non-technical members managing extension settings","Users wanting discoverable configuration options"],"limitations":["Full configuration option list is not documented","No schema validation or inline documentation for each setting","Complex settings (like custom request body JSON) require manual JSON entry","No UI wizard or guided setup for initial configuration","Settings changes may require extension reload (exact behavior unknown)","No validation or error messages for invalid configuration values"],"requires":["VS Code (minimum version unknown)","Access to VS Code settings panel (Cmd+, or Ctrl+,)"],"input_types":["configuration key-value pairs (backend, modelId, baseUrl, requestBody, etc.)","JSON for complex settings"],"output_types":["persisted configuration","extension behavior changes (model switching, backend changes, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-huggingface-huggingface-vscode__cap_7","uri":"capability://code.generation.editing.inline.code.completion.rendering.with.ghost.text.ui.pattern","name":"inline code completion rendering with ghost-text ui pattern","description":"Renders generated code completions as ghost-text overlays in the editor, matching VS Code's native code completion UI pattern. The extension inserts completions at the cursor position when accepted (typically via Tab or Enter key). Ghost-text appears in a dimmed color to distinguish it from actual code. The rendering is handled by VS Code's InlineCompletionItemProvider API (or similar completion API).","intents":["Display code suggestions without disrupting the editing flow","Accept or reject completions with standard VS Code keybindings","Preview completions before committing them to the file","Maintain visual consistency with VS Code's native completion UI"],"best_for":["Developers familiar with VS Code's native code completion","Teams wanting a familiar completion UX without custom UI","Users who prefer ghost-text over dropdown completion menus"],"limitations":["Ghost-text rendering is limited to single-line or short multi-line completions (exact limit unknown)","No customization of ghost-text color or styling documented","Completion acceptance keybindings are fixed (Tab/Enter) — no customization visible","No completion ranking or filtering — all generated text is shown as-is","No completion metadata display (confidence, source, etc.)"],"requires":["VS Code (minimum version unknown, likely 1.50+ for InlineCompletionItemProvider)","Active editor with code file open"],"input_types":["generated code text (from LLM backend)","cursor position (for insertion point)"],"output_types":["rendered ghost-text in editor","completion acceptance/rejection event"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["VS Code (minimum version unknown)","Hugging Face API token (auto-detected from huggingface-cli cache or manual entry via 'Llm: Login' command)","For Ollama backend: local Ollama instance running and accessible","For TGI backend: local Text Generation Inference service running","For OpenAI backend: OpenAI-compatible API endpoint and API key","Network access to stack.dataportraits.org","Code selection or cursor position in editor","Minimum 50-character code sequence for reliable matching","For Hugging Face backend: Hugging Face API token and internet connection","For Ollama backend: Ollama installed and running locally (any version compatible with HTTP API)"],"failure_modes":["No multi-file context awareness — only current file prefix/suffix is sent to the model","Context window automatically truncated to fit model limits, potentially losing surrounding code","Network latency from HTTP requests to external backends (Inference API, Ollama) adds completion delay","Free tier Hugging Face Inference API has rate limits; PRO plan recommended for production use","No streaming response support documented — full completion must be generated before display","Tokenization overhead via tokenizers library adds computational cost per completion request","Bloom filter-based matching produces false positives — not exact matching","Minimum 50-character sequence requirement means short code snippets cannot be checked","Requires sufficient surrounding context for accurate matching (exact threshold unknown)","First-pass check only — full verification requires separate Stack search tool","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.57,"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":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:33.198Z","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=llm-vscode","compare_url":"https://unfragile.ai/compare?artifact=llm-vscode"}},"signature":"pREbyJIP0e8DuhrgRhQOtRLMIpRtcAs+7WJBw4fOuxtX90uP2YUXLC3BPggs5PB/o51X9jHl19HApspHAv7nAw==","signedAt":"2026-06-21T18:18:16.475Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-vscode","artifact":"https://unfragile.ai/llm-vscode","verify":"https://unfragile.ai/api/v1/verify?slug=llm-vscode","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"}}