{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-feiskyer-chatgpt-copilot","slug":"chatgpt-copilot","name":"ChatGPT Copilot","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=feiskyer.chatgpt-copilot","page_url":"https://unfragile.ai/chatgpt-copilot","categories":["code-editors"],"tags":["agent","ai","chatgpt","Claude","copilot","find bugs","Gemini","gpt","gpt4","keybindings","Llama","llm","Ollama","openai","testing"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-feiskyer-chatgpt-copilot__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.routing.with.unified.chat.interface","name":"multi-provider llm routing with unified chat interface","description":"Routes chat requests to 15+ configurable AI providers (OpenAI, Anthropic, Google, Ollama, GitHub Copilot, DeepSeek, Azure, Groq, Perplexity, xAI, Mistral, Together, OpenRouter) through a single VS Code sidebar conversation window. Users configure API keys per provider and select which model/provider to use; the extension abstracts provider-specific API differences and handles streaming response aggregation back into the chat UI. Supports both cloud-hosted and local models (Ollama) without code changes.","intents":["Switch between different AI models without leaving VS Code or reconfiguring chat context","Use local Ollama models for privacy-sensitive code without sending to cloud APIs","Access 200+ models via OpenRouter aggregator without managing individual API keys","Compare responses from multiple providers (e.g., GPT-4 vs Claude) on the same prompt"],"best_for":["developers evaluating multiple LLM providers for cost/quality tradeoffs","teams with existing contracts to specific providers (Azure OpenAI, GitHub Copilot)","privacy-conscious teams using local Ollama deployments","enterprises needing provider flexibility without extension replacement"],"limitations":["No automatic provider failover — if primary provider API fails, user must manually switch providers","API key management is manual per provider — no centralized credential store across providers","Provider-specific features (e.g., OpenAI function calling vs Anthropic tool_use) require different prompt engineering per provider","No cost tracking or usage analytics across providers — billing visibility limited to each provider's dashboard","Streaming latency varies by provider; no client-side buffering or response time optimization"],"requires":["VS Code installed (minimum version unknown)","API key(s) from at least one configured provider (OpenAI, Anthropic, Google, etc.)","Internet connection for cloud providers; local Ollama instance for offline models","For GitHub Copilot provider: existing GitHub Copilot subscription and VS Code GitHub authentication"],"input_types":["text prompts","code snippets (via @mention syntax)","multiple files (via @mention syntax)","images (multimodal support for providers like Gemini, Claude)"],"output_types":["streaming text responses","code suggestions","structured reasoning (for o1/o3/DeepSeek R1 reasoning models)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_1","uri":"capability://code.generation.editing.context.aware.code.generation.with.file.attachment","name":"context-aware code generation with file attachment","description":"Generates new code or entire files by accepting multiple files and images as context via @mention syntax, then streaming AI-generated code directly into the editor or creating new files. The extension parses @-prefixed references, loads file contents into the chat context, and passes them to the selected LLM. Generated code can be inserted inline with one-click application or created as new files. Supports multimodal input (images + code) for visual-to-code generation workflows.","intents":["Generate boilerplate code for a new feature by referencing existing similar files as examples","Create a new file from scratch based on multiple reference files and architectural patterns","Convert a screenshot or diagram (image) into working code with surrounding file context","Generate test files by referencing the implementation file and existing test patterns"],"best_for":["solo developers building features quickly by leveraging existing codebase patterns","teams onboarding new developers who need to generate code matching project conventions","developers working with visual specifications (screenshots, wireframes) that need code generation"],"limitations":["No automatic project-wide indexing — only explicitly @-mentioned files are included in context","Context window limited by selected model's max tokens; large files or many references may be truncated","Generated code quality depends on reference file quality and LLM capability — no validation or linting applied before insertion","No automatic dependency resolution — generated code may reference packages not in project","File creation requires manual directory specification; no intelligent placement based on project structure"],"requires":["VS Code with ChatGPT Copilot extension installed","At least one configured LLM provider with valid API key","Target files must be readable from VS Code workspace (no external file URLs supported)"],"input_types":["text prompts describing desired code","reference code files (via @filename syntax)","images (screenshots, diagrams, wireframes)","multiple files in single prompt"],"output_types":["generated code (inline insertable)","new file creation with generated content","code suggestions in chat for manual copy-paste"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_10","uri":"capability://tool.use.integration.github.copilot.provider.integration.with.native.authentication","name":"github copilot provider integration with native authentication","description":"Integrates GitHub Copilot as a provider option, allowing users with existing GitHub Copilot subscriptions to use their Copilot models (GPT-4o, Claude Sonnet 4, o3-mini, Gemini 2.5 Pro) through the ChatGPT Copilot extension. Uses VS Code's native GitHub authentication (no separate API key required), automatically detecting GitHub Copilot subscription status. Routes requests to GitHub's Copilot API endpoints.","intents":["Use existing GitHub Copilot subscription within ChatGPT Copilot's chat interface","Avoid managing separate API keys by leveraging GitHub authentication","Access GitHub Copilot's model selection (GPT-4o, Claude, Gemini) through a unified chat UI"],"best_for":["developers with existing GitHub Copilot subscriptions wanting a unified chat interface","teams using GitHub as their primary platform and already paying for Copilot","organizations preferring GitHub-managed authentication over API key management"],"limitations":["Requires active GitHub Copilot subscription — free GitHub accounts cannot use this provider","Depends on VS Code GitHub authentication — if GitHub auth fails, provider is unavailable","GitHub Copilot API rate limits apply — unclear what limits are enforced","No cost visibility — billing is through GitHub Copilot subscription, not per-request","Limited model selection compared to OpenAI directly — only GitHub's curated model list"],"requires":["VS Code with ChatGPT Copilot extension","Active GitHub Copilot subscription","GitHub account authenticated in VS Code"],"input_types":["text prompts","code files (via @mention)","images (if model supports)"],"output_types":["code suggestions","text responses"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_11","uri":"capability://tool.use.integration.openai.compatible.api.support.for.custom.model.endpoints","name":"openai-compatible api support for custom model endpoints","description":"Supports any OpenAI-compatible API endpoint (self-hosted models, private deployments, alternative providers) by accepting a custom base URL and API key. The extension treats OpenAI-compatible endpoints as a provider option, allowing users to point to their own model servers or private cloud deployments. Useful for organizations running self-hosted LLMs or using alternative providers with OpenAI-compatible APIs.","intents":["Use a self-hosted LLM server (e.g., vLLM, text-generation-webui) with OpenAI-compatible API","Connect to a private Azure OpenAI deployment with a custom endpoint","Use alternative OpenAI-compatible providers (e.g., Together, Replicate) without separate integrations"],"best_for":["organizations running self-hosted LLM infrastructure","teams with private cloud deployments requiring custom endpoints","developers experimenting with alternative OpenAI-compatible providers"],"limitations":["Requires manual endpoint URL and API key configuration — no auto-discovery","OpenAI API compatibility is assumed but not validated — incompatible endpoints will fail silently","No endpoint health checking or fallback — if endpoint is down, requests fail without retry","Custom endpoints may have different rate limits or capabilities — no automatic adjustment","Debugging endpoint issues is difficult — no detailed error messages for API incompatibilities"],"requires":["VS Code with ChatGPT Copilot extension","OpenAI-compatible API endpoint (URL)","API key for the custom endpoint"],"input_types":["text prompts","code files","images (if endpoint supports)"],"output_types":["responses from custom endpoint"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_12","uri":"capability://memory.knowledge.multi.file.context.aggregation.with.mention.syntax","name":"multi-file context aggregation with @mention syntax","description":"Allows users to reference multiple files in a single chat prompt using @filename syntax, automatically loading file contents into the chat context. The extension parses @-prefixed references, resolves them to workspace files, and includes their full contents in the prompt sent to the LLM. Supports both relative and absolute file paths, and allows mixing multiple files with text and images in a single message.","intents":["Reference multiple related files (e.g., interface, implementation, tests) in a single code generation prompt","Provide architectural context by including multiple files that define the system design","Generate code that spans multiple files by showing the LLM the full context","Ask the LLM to refactor across multiple files by including all affected files"],"best_for":["developers working with multi-file features or modules","teams needing to provide full architectural context to AI","developers refactoring across multiple files"],"limitations":["No automatic file discovery — users must manually specify each file to include","Context window limits apply — including too many large files may exceed model's token limit","No file change detection — if files are modified after @mention, old content is used","File resolution is workspace-relative — absolute paths or external files not supported","No file preview in chat — users can't verify which files were included without external tools","Large files may be truncated without warning — unclear how truncation is handled","No automatic dependency resolution — @-mentioning a file doesn't automatically include its imports"],"requires":["VS Code with ChatGPT Copilot extension","Files must be in the VS Code workspace","Files must be readable (no permission restrictions)"],"input_types":["@filename references","text prompts","images"],"output_types":["aggregated file contents in prompt","code suggestions based on multi-file context"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_13","uri":"capability://safety.moderation.telemetry.free.operation.with.local.data.retention","name":"telemetry-free operation with local data retention","description":"Operates without collecting usage telemetry, analytics, or user behavior data. The extension does not send information about prompts, code, files, or interactions to the publisher or third parties (beyond the configured LLM provider). Conversation history and custom prompts are retained locally (storage location unknown but assumed to be local VS Code storage). No tracking pixels, analytics SDKs, or telemetry libraries are included.","intents":["Use AI coding assistance without privacy concerns about usage tracking","Comply with data privacy regulations (GDPR, HIPAA, etc.) that restrict telemetry","Maintain full control over where code and prompts are sent (only to configured LLM provider)"],"best_for":["organizations with strict privacy policies prohibiting telemetry","developers working on sensitive or proprietary code","teams in regulated industries (healthcare, finance, government)"],"limitations":["No usage analytics for the extension itself — developers can't see how features are used","No error reporting — crashes or bugs may go unnoticed by the publisher","No feature usage tracking — unclear which features are popular or underused","No telemetry means no automatic bug detection — issues must be manually reported"],"requires":["VS Code with ChatGPT Copilot extension"],"input_types":["any prompts, code, or files (no telemetry sent)"],"output_types":["no telemetry data"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_14","uri":"capability://text.generation.language.vs.code.sidebar.chat.ui.with.conversation.management","name":"vs code sidebar chat ui with conversation management","description":"Provides a dedicated sidebar panel in VS Code for chat conversations, displaying messages in a threaded format with streaming responses. The sidebar UI includes conversation history, message editing (to resend modified prompts), and visual indicators for message status (sending, complete, error). Integrates with VS Code's sidebar layout, allowing users to resize, collapse, or move the chat panel alongside other sidebar panels (Explorer, Source Control, etc.).","intents":["Maintain a persistent chat conversation while working on code in the editor","Reference previous messages and context without losing conversation history","Edit and resend previous prompts to refine AI responses","Keep chat visible alongside code without switching windows"],"best_for":["developers who prefer chat-based AI interaction over inline suggestions","teams using AI for iterative problem-solving and refinement","developers who want to maintain conversation context across multiple files"],"limitations":["Sidebar space is limited on smaller monitors — chat may be cramped or require scrolling","No conversation search or filtering — must scroll through history to find previous messages","No conversation organization (folders, tags) — all conversations are in a flat list","Message editing requires manual re-execution — no diff view of changes","No conversation branching — editing a message overwrites the original, losing the alternative path","Sidebar state is not persisted across VS Code restarts — unclear if conversation history survives restarts"],"requires":["VS Code with ChatGPT Copilot extension"],"input_types":["text prompts","file references (@mention)","images"],"output_types":["chat messages","streaming responses","conversation history"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_2","uri":"capability://code.generation.editing.inline.code.modification.and.one.click.application","name":"inline code modification and one-click application","description":"Applies AI-suggested code changes directly to the editor with a single click, without requiring manual copy-paste. When the LLM suggests code modifications (refactoring, bug fixes, optimizations), the extension detects code blocks in the response and provides clickable 'apply' buttons that insert the suggestion at the cursor position or replace selected text. Supports both full-file replacements and partial edits.","intents":["Fix a bug by getting AI suggestions and applying them immediately without manual editing","Refactor code by accepting AI-suggested improvements with one click","Optimize performance by applying AI-suggested code changes without manual review","Apply multiple code suggestions sequentially from a single chat response"],"best_for":["developers iterating quickly on code fixes and refactoring","teams using AI for code review and wanting fast application of suggestions","developers with high trust in their selected LLM's code quality"],"limitations":["No diff preview before application — changes are applied immediately on click","No undo integration with VS Code undo history (relies on VS Code's native undo after insertion)","Code block detection is heuristic-based (markdown code fence parsing) — malformed suggestions may not be clickable","No syntax validation before application — invalid code can be inserted and must be manually fixed","No automatic test execution or validation after applying changes","Partial edits require precise line number matching — off-by-one errors in AI suggestions cause misalignment"],"requires":["VS Code with ChatGPT Copilot extension","Active chat conversation with code suggestions from LLM","File open in editor for insertion (or text selected for replacement)"],"input_types":["AI-generated code suggestions in chat","current file content (implicit context)"],"output_types":["modified code in editor","file state change (tracked by VS Code)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_3","uri":"capability://memory.knowledge.conversation.history.export.and.persistence","name":"conversation history export and persistence","description":"Exports entire chat conversations to Markdown format for documentation, knowledge retention, or sharing. The extension maintains conversation history within the sidebar chat window, allowing users to edit and resend previous prompts, and provides batch export functionality to save conversations as .md files. Supports conversation editing (modify previous prompts and re-run) and selective export.","intents":["Document AI-assisted code generation decisions for team knowledge sharing","Export debugging sessions for future reference or team learning","Create runbooks or decision logs from AI conversations","Archive conversations for compliance or audit purposes"],"best_for":["teams documenting architectural decisions made with AI assistance","developers building knowledge bases from AI conversations","organizations with compliance requirements for AI-assisted work"],"limitations":["Export format is Markdown only — no structured formats (JSON, CSV) for programmatic processing","Conversation storage location is unknown — unclear if stored locally or in cloud, affecting data residency","No automatic conversation tagging or organization — all exports are flat files","Editing previous prompts requires manual re-execution — no diff view of changes","No conversation search or filtering within the sidebar — must export to search externally","Image attachments may not be preserved in Markdown export — unclear how multimodal content is handled"],"requires":["VS Code with ChatGPT Copilot extension","Active conversation history in sidebar","Write access to filesystem for export"],"input_types":["conversation history (text + code + images)"],"output_types":["Markdown files (.md)","editable conversation state (for re-running prompts)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_4","uri":"capability://memory.knowledge.custom.prompt.management.and.reuse","name":"custom prompt management and reuse","description":"Stores and retrieves custom prompts via a prompt manager (v4.6+) accessible via #-symbol search within the chat. Users can create, edit, and organize reusable prompt templates that can be prefixed to any chat message. Supports custom prompt prefixes that are automatically prepended to user prompts before sending to the LLM, enabling consistent system instructions or role-playing without manual repetition.","intents":["Create a 'code reviewer' prompt template and reuse it across multiple code review sessions","Define project-specific coding standards as a prompt prefix that's automatically applied to all code generation","Build a library of debugging prompts for common issue types","Maintain consistent AI behavior across team members by sharing prompt templates"],"best_for":["teams standardizing AI interactions across developers","developers with repetitive AI workflows (e.g., daily code reviews, testing)","organizations embedding domain-specific instructions into AI interactions"],"limitations":["Prompt storage location unknown — unclear if stored locally or synced across VS Code instances","No prompt versioning or rollback — edits overwrite previous versions","No sharing mechanism documented — prompts may not be exportable for team distribution","Search via # symbol is manual — no autocomplete or fuzzy matching mentioned","No prompt analytics — no visibility into which prompts are most used","Custom prefixes are simple string concatenation — no variable substitution or conditional logic"],"requires":["VS Code with ChatGPT Copilot extension","Active chat conversation"],"input_types":["custom prompt text","prompt template definitions"],"output_types":["stored prompt templates","prefixed prompts sent to LLM"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_5","uri":"capability://tool.use.integration.tool.calling.via.native.apis.and.prompt.based.parsing","name":"tool calling via native apis and prompt-based parsing","description":"Enables LLMs to call external tools and functions through two mechanisms: (1) native tool calling APIs for models that support them (OpenAI function calling, Anthropic tool_use), and (2) prompt-based tool calling (v4.9+) for models without native support, where the LLM outputs tool calls as structured text that the extension parses and executes. Integrates with Model Context Protocol (MCP, v4.7+) to allow users to 'bring your own tools' for custom integrations. Supports both built-in tools and user-defined tool schemas.","intents":["Enable an LLM to execute shell commands or query a database as part of code generation","Allow Claude or GPT-4 to call custom APIs (e.g., internal company APIs) during problem-solving","Use local Ollama models with tool calling by parsing LLM output instead of relying on native APIs","Integrate custom tools (e.g., linters, test runners) into the AI conversation flow"],"best_for":["developers building AI agents that need to interact with external systems","teams with custom internal APIs that should be accessible to AI","organizations using local models (Ollama) that need tool calling without cloud APIs"],"limitations":["Prompt-based tool calling is less reliable than native APIs — parsing errors can cause tool calls to fail silently","Tool schema definition is manual — no automatic schema generation from code or APIs","No built-in tool library — users must define all custom tools from scratch","Tool execution context is limited — unclear if tools can access VS Code internals or filesystem","Error handling for failed tool calls is basic — no retry logic or fallback mechanisms mentioned","MCP integration is new (v4.7) — stability and feature completeness unknown","No tool call logging or debugging — difficult to diagnose tool calling failures"],"requires":["VS Code with ChatGPT Copilot extension v4.7+ (for MCP support)","For native tool calling: LLM provider that supports it (OpenAI, Anthropic, etc.)","For custom tools: MCP server implementation or tool schema definition","Tool execution environment (e.g., shell access, API credentials)"],"input_types":["tool schema definitions (JSON)","LLM-generated tool calls (text or structured format)"],"output_types":["tool execution results","structured tool responses back to LLM"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_6","uri":"capability://text.generation.language.streaming.response.aggregation.and.real.time.chat.ui","name":"streaming response aggregation and real-time chat ui","description":"Streams AI responses in real-time to the VS Code sidebar chat window, displaying tokens as they arrive from the LLM provider without waiting for full response completion. The extension buffers streaming chunks from the provider API, aggregates them into the chat message, and updates the UI incrementally. Supports streaming for all configured providers (cloud and local Ollama). Provides visual feedback (e.g., loading indicators) while streaming is in progress.","intents":["See AI responses appear in real-time without waiting for full generation","Start reading and acting on AI suggestions while the response is still being generated","Monitor long-running code generation tasks without UI freezing","Provide immediate feedback that the AI is processing the request"],"best_for":["developers who want immediate feedback from AI interactions","teams with high-latency LLM providers (e.g., local Ollama) where streaming visibility is important","users working with reasoning models (o1, o3) that have long generation times"],"limitations":["Streaming latency varies by provider — no client-side buffering or response time optimization","No pause/resume functionality for streaming — must wait for completion or manually stop","Streaming may impact UI responsiveness if chunks arrive very rapidly — no throttling mentioned","No streaming progress indicators (e.g., token count, estimated time remaining)","Streaming state is not persisted — if VS Code crashes during streaming, response is lost","No streaming cancellation UI — unclear how to stop a long-running response"],"requires":["VS Code with ChatGPT Copilot extension","LLM provider that supports streaming (most modern providers do)","Stable network connection (for cloud providers)"],"input_types":["user prompts"],"output_types":["streaming text tokens","complete response after streaming ends"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_7","uri":"capability://image.visual.multimodal.input.with.image.attachment.and.visual.to.code.generation","name":"multimodal input with image attachment and visual-to-code generation","description":"Accepts images (screenshots, diagrams, wireframes) as input alongside text prompts and code files via @mention syntax. Passes images to multimodal LLM providers (Google Gemini, Claude, GPT-4 Vision) for visual understanding and code generation. Enables workflows like converting a UI screenshot to HTML/CSS, generating code from architecture diagrams, or creating tests from visual specifications. Image context is included in the same chat message as text prompts and code references.","intents":["Convert a screenshot of a UI design into working HTML/CSS/React code","Generate code from an architecture diagram or flowchart","Create test cases based on a visual specification or wireframe","Analyze and explain code based on a screenshot of error messages or logs"],"best_for":["frontend developers converting designs to code","teams using visual specifications (Figma, Sketch) as input to code generation","developers debugging visual issues by sharing screenshots with AI"],"limitations":["Only multimodal providers support images — non-multimodal models (Ollama, some open-source) will fail if images are included","Image format support is provider-dependent — unclear which formats (PNG, JPG, SVG, PDF) are supported","Image size limits are provider-dependent — large images may be rejected or resized","No image preview in chat — users can't see which image was attached without external tools","Image quality affects code generation quality — low-resolution or unclear screenshots produce poor results","No OCR for text in images — images with code or text require manual transcription"],"requires":["VS Code with ChatGPT Copilot extension","Multimodal LLM provider (Google Gemini, Claude, GPT-4 Vision, etc.)","Image file in supported format (PNG, JPG, etc.)"],"input_types":["images (screenshots, diagrams, wireframes)","text prompts","code files (via @mention)"],"output_types":["generated code","visual analysis and explanations","structured data extracted from images"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_8","uri":"capability://planning.reasoning.reasoning.model.support.with.extended.thinking","name":"reasoning model support with extended thinking","description":"Supports reasoning models (OpenAI o1, o3, DeepSeek R1) that perform extended chain-of-thought reasoning before generating responses. These models take longer to generate responses but produce higher-quality solutions for complex problems. The extension routes requests to reasoning models like any other provider, displays streaming reasoning steps (if available), and presents final responses. Reasoning models have different configuration requirements (e.g., no system prompts, no tool calling in some cases).","intents":["Solve complex algorithmic problems by leveraging extended reasoning","Debug difficult issues by having the AI reason through the problem step-by-step","Generate high-quality code for critical systems where accuracy is paramount","Understand complex code by asking reasoning models to explain it thoroughly"],"best_for":["developers tackling complex algorithmic or architectural problems","teams prioritizing code quality over speed","organizations with budgets for higher-cost reasoning model APIs"],"limitations":["Reasoning models are significantly more expensive than standard models — cost per request is 5-10x higher","Response generation is much slower — reasoning steps can take 30+ seconds","Reasoning models have restrictions (e.g., no system prompts, limited tool calling) that may conflict with extension features","Reasoning step visibility is provider-dependent — unclear if reasoning process is shown to user","No cost estimation before using reasoning models — users may incur unexpected charges","Reasoning models may not be suitable for all tasks — using them for simple queries wastes money"],"requires":["VS Code with ChatGPT Copilot extension","API key for reasoning model provider (OpenAI, DeepSeek, etc.)","Sufficient API quota and budget for expensive model calls"],"input_types":["complex problem descriptions","code snippets requiring analysis","architectural questions"],"output_types":["reasoning steps (if visible)","final solution or explanation","high-quality code generation"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-feiskyer-chatgpt-copilot__cap_9","uri":"capability://code.generation.editing.local.model.execution.via.ollama.integration","name":"local model execution via ollama integration","description":"Executes open-source models (Llama, Qwen, CodeLlama, Mistral, etc.) locally via Ollama without sending code or prompts to cloud APIs. The extension connects to a local Ollama instance (running on localhost:11434 by default), routes requests to the local model, and streams responses back. Supports all Ollama-compatible models and enables fully offline code generation and analysis. No API keys required for local models.","intents":["Generate code without sending proprietary code to cloud APIs for privacy/compliance","Use AI for code generation in air-gapped or offline environments","Reduce API costs by using free open-source models instead of paid cloud APIs","Experiment with different open-source models without API key management"],"best_for":["organizations with strict data privacy or compliance requirements (healthcare, finance, government)","teams working on proprietary code that cannot be sent to cloud APIs","developers in offline or low-bandwidth environments","cost-conscious teams wanting to avoid API charges"],"limitations":["Requires local Ollama instance to be running — adds infrastructure management overhead","Local model quality is lower than cloud models (GPT-4, Claude) — trade-off between privacy and capability","Local model inference is slower than cloud APIs — depends on local hardware (GPU, CPU)","Ollama setup and model downloading requires technical knowledge and disk space (models are 4-70GB)","No automatic model selection — users must manually choose which Ollama model to use","Limited model variety compared to cloud providers — fewer specialized models available","No streaming progress indicators for local inference — unclear how long generation will take"],"requires":["VS Code with ChatGPT Copilot extension","Ollama installed and running locally (https://ollama.ai)","At least one Ollama model downloaded (e.g., ollama pull llama2)","Sufficient local hardware (GPU recommended for reasonable inference speed)","Ollama listening on localhost:11434 (or custom configured port)"],"input_types":["text prompts","code files (via @mention)","images (if model supports multimodal)"],"output_types":["locally-generated code","locally-generated text responses"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["VS Code installed (minimum version unknown)","API key(s) from at least one configured provider (OpenAI, Anthropic, Google, etc.)","Internet connection for cloud providers; local Ollama instance for offline models","For GitHub Copilot provider: existing GitHub Copilot subscription and VS Code GitHub authentication","VS Code with ChatGPT Copilot extension installed","At least one configured LLM provider with valid API key","Target files must be readable from VS Code workspace (no external file URLs supported)","VS Code with ChatGPT Copilot extension","Active GitHub Copilot subscription","GitHub account authenticated in VS Code"],"failure_modes":["No automatic provider failover — if primary provider API fails, user must manually switch providers","API key management is manual per provider — no centralized credential store across providers","Provider-specific features (e.g., OpenAI function calling vs Anthropic tool_use) require different prompt engineering per provider","No cost tracking or usage analytics across providers — billing visibility limited to each provider's dashboard","Streaming latency varies by provider; no client-side buffering or response time optimization","No automatic project-wide indexing — only explicitly @-mentioned files are included in context","Context window limited by selected model's max tokens; 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