{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ms-vscode-vscode-copilot-vision","slug":"vision-for-copilot-preview","name":"Vision for Copilot Preview","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ms-vscode.vscode-copilot-vision","page_url":"https://unfragile.ai/vision-for-copilot-preview","categories":["code-editors"],"tags":["accessibility","ai","anthropic","chat-participant","claude","co-pilot","gemini","html","images","javascriptreact","markdown","openai","pilot","typescriptreact","vision"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_0","uri":"capability://image.visual.image.attachment.to.chat.context","name":"image-attachment-to-chat-context","description":"Enables users to attach images directly to chat messages in VS Code's chat panel via clipboard paste, drag-and-drop, or workspace file selection. The extension processes the image data and passes it as multimodal context to the configured vision-capable LLM provider (OpenAI, Anthropic, Gemini, or Azure OpenAI), allowing the AI to analyze visual content and respond with insights, explanations, or code suggestions based on the image content.","intents":["I want to paste a screenshot into Copilot chat and ask it to explain what I'm seeing","I need to drag an image from my workspace into chat to get AI analysis of its contents","I want to show Copilot a UI mockup or diagram and get feedback on it"],"best_for":["developers debugging visual issues or UI layouts","teams collaborating on design feedback within VS Code","developers who want to troubleshoot screenshots without leaving the editor"],"limitations":["Image input limited to standard formats (JPEG, PNG, GIF, WebP); no video or animated content support","Cannot process images from external URLs directly — must be local files or clipboard content","Image size and resolution constraints depend on configured LLM provider's vision API limits (typically 20MB max per OpenAI, varies by provider)","No batch image processing — one image per chat message attachment"],"requires":["VS Code latest version (specific minimum version number not documented)","Active API key for at least one configured vision provider (OpenAI, Anthropic, Gemini, or Azure OpenAI)","Valid account with sufficient API credits on the selected provider"],"input_types":["image (clipboard paste)","image (drag-and-drop into chat panel)","image (workspace file selection)","text (chat message accompanying image)"],"output_types":["text (chat response analyzing image)","code (if image contains code to be explained or refactored)","structured suggestions (e.g., accessibility improvements)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_1","uri":"capability://image.visual.alt.text.generation.for.images","name":"alt-text-generation-for-images","description":"Provides quick-fix code actions in markdown, HTML, JSX, and TSX files to automatically generate or refine alt text for images. When triggered, the extension sends the image file and surrounding document context to the configured vision LLM, which analyzes the image content and returns descriptive alt text that can be inserted directly into the code. This improves accessibility compliance without manual effort.","intents":["I want to generate alt text for all images in my markdown documentation automatically","I need to add missing alt attributes to img tags in my HTML/JSX components","I want to ensure my documentation meets WCAG accessibility standards for images"],"best_for":["developers building accessible web applications","technical writers maintaining documentation with images","teams with accessibility compliance requirements (WCAG 2.1 AA/AAA)"],"limitations":["Quick fixes only available in markdown, HTML, JSX, and TSX files — not other formats like reStructuredText or AsciiDoc","Alt text generation quality depends on the vision model's understanding of image context; may require manual review for specialized or technical images","No batch processing across entire project — must trigger code action per image or file","Cannot generate alt text for images referenced via external URLs; only works with local workspace files"],"requires":["VS Code latest version","Active API key for configured vision provider","Image file must be accessible within workspace or referenced with valid local path","Document must be in supported format (markdown, HTML, JSX, TSX)"],"input_types":["image (local file in workspace)","code (HTML img tag, markdown image syntax, JSX img element)","document context (surrounding code for semantic understanding)"],"output_types":["text (generated alt text string)","code (updated HTML/JSX/markdown with alt attribute inserted)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_2","uri":"capability://image.visual.screenshot.based.troubleshooting","name":"screenshot-based-troubleshooting","description":"Provides a 'Copilot Vision: Troubleshoot' command that captures the current VS Code window state as a screenshot and automatically sends it to the chat panel with the configured vision LLM. Users can then ask the AI to diagnose issues, explain error messages, or suggest fixes based on what's visible in the editor. This enables rapid troubleshooting without manual screenshot tools or context-switching.","intents":["I want to screenshot my VS Code window and ask Copilot to explain the error I'm seeing","I need help debugging a visual issue in my code editor — I want to show Copilot exactly what I see","I want to capture my current workspace state and get AI suggestions for fixing the problem"],"best_for":["developers debugging complex errors or unfamiliar error messages","developers troubleshooting build failures or linting issues","teams getting remote debugging help via shared screenshots"],"limitations":["Screenshot captures only the VS Code window — cannot capture external applications or system state","Screenshot resolution and quality depend on display DPI and VS Code window size; may be unclear for small text","No automatic error detection — user must manually invoke the troubleshoot command; not triggered on error events","Screenshot is sent to the configured LLM provider's servers; sensitive code or credentials visible in the editor will be transmitted"],"requires":["VS Code latest version","Active API key for configured vision provider","Sufficient API credits with the provider"],"input_types":["screenshot (VS Code window capture)","text (user's follow-up question in chat)"],"output_types":["text (AI diagnosis and suggestions)","code (suggested fixes or refactoring)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_3","uri":"capability://tool.use.integration.multi.provider.vision.model.configuration","name":"multi-provider-vision-model-configuration","description":"Allows users to configure and switch between multiple vision-capable LLM providers (OpenAI, Anthropic, Gemini, Azure OpenAI) and their respective models through VS Code settings and commands. The extension manages API keys per provider, validates configuration, and routes vision requests to the selected provider's API. Users can set different providers for different use cases or switch providers based on cost, latency, or model capabilities.","intents":["I want to use OpenAI's GPT-4V for high-quality image analysis but switch to Anthropic Claude for cost savings on routine tasks","I need to configure Azure OpenAI with my organization's endpoint and credentials","I want to compare different vision models' outputs on the same image by switching providers"],"best_for":["organizations with multi-cloud or multi-vendor LLM strategies","developers optimizing for cost by using different providers for different workloads","enterprises using Azure OpenAI with custom deployments"],"limitations":["Each provider requires separate API key management — no unified credential system","Model availability varies by provider; not all providers support all vision models (e.g., Gemini's vision capabilities differ from OpenAI's)","API key storage mechanism is undocumented — unclear if keys are encrypted at rest or synced via VS Code settings sync","No automatic provider failover — if the selected provider's API is down, the extension fails without fallback","Configuration is per-user; no team-level or workspace-level provider defaults"],"requires":["VS Code latest version","Valid API key for at least one provider (OpenAI, Anthropic, Gemini, or Azure OpenAI)","For Azure OpenAI: Azure subscription, deployed model endpoint, and API key","Active account with sufficient credits on selected provider"],"input_types":["text (provider name via command or settings)","text (model name via command or settings)","text (API key via secure input dialog)","text (Azure endpoint URL for Azure OpenAI)"],"output_types":["configuration state (stored in VS Code settings)","validation feedback (success/failure of API key test)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_4","uri":"capability://safety.moderation.api.key.secure.management","name":"api-key-secure-management","description":"Provides commands to securely store, update, and remove API keys for each configured vision provider. The extension uses VS Code's secure credential storage mechanism (via the VS Code Secret Storage API) to manage API keys without exposing them in plain text in settings files. Users can set or update keys via the 'Copilot Vision: Set Current Model's API Key' command and remove them via 'Copilot Vision: Remove Current Model's API Key' command.","intents":["I want to securely store my OpenAI API key without putting it in a plain-text settings file","I need to rotate my API key and update it in VS Code without manually editing configuration","I want to remove my API key from VS Code when I'm done using the extension"],"best_for":["developers working on shared machines or in team environments","security-conscious developers who want to avoid storing credentials in version control","teams with API key rotation policies"],"limitations":["API key storage mechanism is undocumented — unclear if keys are encrypted at rest or synced via VS Code settings sync to other machines","No key rotation automation — users must manually update keys when they expire or are rotated","No audit logging of key access or changes — no visibility into when keys were set/removed","If VS Code's credential storage is compromised, all stored API keys are at risk","No support for environment variable injection or external secret management systems (e.g., HashiCorp Vault, AWS Secrets Manager)"],"requires":["VS Code latest version","Access to VS Code's secure credential storage (available on Windows, macOS, Linux with credential managers)"],"input_types":["text (API key via secure input dialog)","text (provider name to identify which key to update/remove)"],"output_types":["confirmation message (key stored/removed successfully)","error message (invalid key format or storage failure)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_5","uri":"capability://text.generation.language.chat.participant.integration","name":"chat-participant-integration","description":"Registers the vision extension as a chat participant in VS Code's chat panel, allowing users to invoke vision capabilities through natural chat interactions. The extension hooks into the chat participant API to intercept messages, detect image attachments, and route them to the configured vision LLM provider. This enables a conversational interface where users can ask questions about images, request alt text generation, or seek troubleshooting help without leaving the chat UI.","intents":["I want to ask Copilot questions about an image I've attached to chat","I want to have a multi-turn conversation where I reference images and get follow-up suggestions","I want to use chat commands to trigger vision-specific actions like alt text generation"],"best_for":["developers who prefer conversational AI interaction over command-line tools","teams using VS Code's chat panel as a central AI interaction hub","developers who want to maintain context across multiple vision queries in a single chat session"],"limitations":["Chat participant integration is limited to VS Code's chat panel — cannot be used in other editors or IDEs","No custom chat commands or slash commands documented — limited to standard chat participant API capabilities","Chat history is not persisted across VS Code sessions by default — conversation context is lost when the editor closes","No support for multi-user chat or collaborative vision analysis within a single chat session"],"requires":["VS Code latest version","Chat panel UI available (standard in recent VS Code versions)"],"input_types":["text (chat message)","image (attached to chat message)","context (selected code or files in editor)"],"output_types":["text (chat response)","code (suggested refactoring or fixes)","structured data (alt text, accessibility suggestions)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_6","uri":"capability://image.visual.workspace.image.file.selection","name":"workspace-image-file-selection","description":"Allows users to select and attach image files directly from their workspace to chat messages or vision commands. The extension provides a file picker UI that filters for image formats (JPEG, PNG, GIF, WebP) and enables users to browse the workspace directory structure to find and attach images without manual file path entry. Selected images are read from disk and passed to the vision LLM provider.","intents":["I want to select an image from my project's assets folder and attach it to chat","I need to browse my workspace to find a screenshot or diagram to analyze","I want to attach multiple images from different folders without typing file paths"],"best_for":["developers working with image-heavy projects (design systems, documentation, UI mockups)","teams collaborating on visual assets within a shared workspace","developers who prefer UI-based file selection over manual path entry"],"limitations":["File picker is limited to the current workspace — cannot browse files outside the workspace root","Only image formats are shown in the picker — no support for other media types (video, audio, PDF)","Large image files (>20MB) may fail to upload depending on the configured provider's limits","No preview of selected images before attachment — users must rely on file names and folder structure","Single image selection per action — no multi-select or batch attachment"],"requires":["VS Code latest version","Workspace with image files present"],"input_types":["file system interaction (file picker UI)","image file path (selected from workspace)"],"output_types":["image data (read from disk and passed to vision LLM)","image metadata (file name, size, path)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_7","uri":"capability://image.visual.context.aware.document.analysis","name":"context-aware-document-analysis","description":"When generating alt text or analyzing images, the extension passes surrounding document context (code structure, file type, semantic meaning) to the vision LLM alongside the image data. This allows the AI to generate alt text that is semantically appropriate for the specific context (e.g., alt text for a diagram in technical documentation differs from alt text for a UI mockup in a design system). The extension extracts relevant code snippets and document metadata to enrich the vision request.","intents":["I want alt text that reflects the semantic meaning of the image in my technical documentation","I need the AI to understand that this image is a component diagram, not just a generic picture","I want alt text that matches the tone and context of my markdown file"],"best_for":["technical writers creating documentation with diagrams and screenshots","developers building design systems with visual components","teams maintaining accessibility-compliant documentation"],"limitations":["Context extraction is limited to the current file — no cross-file context or project-wide semantic understanding","Large documents may exceed the vision LLM's context window, requiring truncation of surrounding code","Context extraction quality depends on the file format and language — works best for markdown, HTML, JSX; limited for other formats","No caching of context — each vision request re-extracts and re-sends document context, increasing API costs and latency"],"requires":["VS Code latest version","Document in supported format (markdown, HTML, JSX, TSX)"],"input_types":["image (local file or clipboard)","document context (surrounding code, file metadata, semantic structure)"],"output_types":["text (context-aware alt text)","structured suggestions (accessibility improvements based on context)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_8","uri":"capability://tool.use.integration.provider.agnostic.vision.api.abstraction","name":"provider-agnostic-vision-api-abstraction","description":"Abstracts the differences between multiple vision API providers (OpenAI, Anthropic, Gemini, Azure OpenAI) behind a unified interface. The extension handles provider-specific API request formatting, response parsing, and error handling, allowing users to switch providers without changing their workflow. This abstraction layer translates generic vision requests (e.g., 'analyze this image') into provider-specific API calls with appropriate parameters, model names, and authentication.","intents":["I want to switch from OpenAI to Anthropic without changing how I use the extension","I need the extension to handle the differences between provider APIs transparently","I want to use Azure OpenAI with my custom deployment without learning a different interface"],"best_for":["organizations using multiple LLM providers","developers optimizing for cost by comparing provider pricing","enterprises with Azure OpenAI deployments requiring custom endpoint configuration"],"limitations":["Abstraction layer adds latency (~50-100ms per request) due to request translation and response parsing","Provider-specific features or parameters are not exposed — users cannot leverage unique capabilities of individual providers","Error messages are generic and may not reflect provider-specific error codes or rate limits","Model availability varies by provider — switching providers may require selecting a different model if the current model is not available","No automatic provider selection based on image type or use case — users must manually choose the provider"],"requires":["VS Code latest version","API key for at least one supported provider"],"input_types":["vision request (generic: analyze image, generate alt text, troubleshoot)","image data (format-agnostic)","provider configuration (selected provider and model)"],"output_types":["vision response (normalized across providers)","error messages (translated from provider-specific errors)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-vision__cap_9","uri":"capability://automation.workflow.deprecation.migration.path.to.github.copilot.chat","name":"deprecation-migration-path-to-github-copilot-chat","description":"The extension is documented as being deprecated in favor of built-in image flow in GitHub Copilot Chat. This capability represents the extension's current status and the planned migration path for users. The extension continues to function but is positioned as a temporary solution until GitHub Copilot Chat's native vision features reach feature parity. Users are implicitly encouraged to migrate to GitHub Copilot Chat for long-term vision support.","intents":["I want to understand the future of vision capabilities in VS Code","I need to know if I should invest in learning this extension or wait for GitHub Copilot Chat","I want to plan my migration from this extension to built-in vision features"],"best_for":["organizations evaluating long-term vision tool strategy","developers deciding whether to adopt this extension or wait for GitHub Copilot Chat","teams planning migration timelines"],"limitations":["Deprecation timeline is unclear — no specific end-of-life date or feature parity target documented","Migration path is not documented — unclear how to transfer configurations, API keys, or chat history to GitHub Copilot Chat","GitHub Copilot Chat's vision feature availability and capabilities are not detailed in this extension's documentation","No automatic migration tool or import/export functionality provided","Users may face disruption if they invest heavily in this extension before GitHub Copilot Chat reaches feature parity"],"requires":["Awareness of deprecation status (documented in extension description)"],"input_types":[],"output_types":["deprecation notice (in extension description and marketplace listing)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["VS Code latest version (specific minimum version number not documented)","Active API key for at least one configured vision provider (OpenAI, Anthropic, Gemini, or Azure OpenAI)","Valid account with sufficient API credits on the selected provider","VS Code latest version","Active API key for configured vision provider","Image file must be accessible within workspace or referenced with valid local path","Document must be in supported format (markdown, HTML, JSX, TSX)","Sufficient API credits with the provider","Valid API key for at least one provider (OpenAI, Anthropic, Gemini, or Azure OpenAI)","For Azure OpenAI: Azure subscription, deployed model endpoint, and API key"],"failure_modes":["Image input limited to standard formats (JPEG, PNG, GIF, WebP); no video or animated content support","Cannot process images from external URLs directly — must be local files or clipboard content","Image size and resolution constraints depend on configured LLM provider's vision API limits (typically 20MB max per OpenAI, varies by provider)","No batch image processing — one image per chat message attachment","Quick fixes only available in markdown, HTML, JSX, and TSX files — not other formats like reStructuredText or AsciiDoc","Alt text generation quality depends on the vision model's understanding of image context; may require manual review for specialized or technical images","No batch processing across entire project — must trigger code action per image or file","Cannot generate alt text for images referenced via external URLs; only works with local workspace files","Screenshot captures only the VS Code window — cannot capture external applications or system state","Screenshot resolution and quality depend on display DPI and VS Code window size; may be unclear for small text","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.59,"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":"2026-05-03T15:20:40.997Z","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=vision-for-copilot-preview","compare_url":"https://unfragile.ai/compare?artifact=vision-for-copilot-preview"}},"signature":"bktJQOREWwqD+Q4SVldyMhraqfSfplavqw81TflWKoEFwPRA6HdaswdNn/iGWXjj1m/S7Oo6jiZynHKMv01EDg==","signedAt":"2026-06-18T11:48:45.228Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vision-for-copilot-preview","artifact":"https://unfragile.ai/vision-for-copilot-preview","verify":"https://unfragile.ai/api/v1/verify?slug=vision-for-copilot-preview","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"}}