{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-cweijan-chat-copilot","slug":"chat-copilot","name":"Chat Copilot","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=cweijan.chat-copilot","page_url":"https://unfragile.ai/chat-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-cweijan-chat-copilot__cap_0","uri":"capability://text.generation.language.streaming.chat.interface.with.multi.provider.llm.support","name":"streaming-chat-interface-with-multi-provider-llm-support","description":"Provides a real-time streaming chat sidebar within VS Code that connects to OpenAI-compatible APIs (OpenAI, Anthropic, Google, Ollama, Azure OpenAI, DeepSeek) via configurable API endpoints and authentication tokens. Implements server-sent events (SSE) streaming to display token-by-token responses, with mid-stream interruption capability and automatic handling of truncated responses. The extension abstracts provider differences through a unified configuration layer supporting custom model names and base URL overrides.","intents":["I want to chat with an AI assistant without leaving VS Code","I need to switch between different LLM providers (OpenAI, Claude, local Ollama) without changing my workflow","I want to stop a long-running AI response mid-generation to save tokens or time","I need to use a custom or self-hosted LLM endpoint with my own API infrastructure"],"best_for":["developers using multiple LLM providers and wanting unified interface","teams with on-premise or self-hosted LLM infrastructure (Ollama, vLLM)","privacy-conscious developers preferring local model execution","solo developers prototyping with different model capabilities"],"limitations":["Requires active internet connection for cloud providers (OpenAI, Anthropic, Google); only Ollama supports offline operation","No built-in rate limiting or token quota management — relies on provider-level controls","Streaming latency depends on network and provider response time; no local caching of responses","Custom model support limited to OpenAI-compatible API format; proprietary APIs require wrapper","No conversation persistence across VS Code sessions without manual export"],"requires":["Visual Studio Code (minimum version unknown, likely 1.60+)","API key for at least one provider (OpenAI, Anthropic, Google, or local Ollama instance)","Network connectivity for cloud providers; localhost:11434 for Ollama","Valid OpenAI-compatible API endpoint URL (defaults to https://api.openai.com/v1)"],"input_types":["text (chat prompts)","code (via @file syntax for file references)","images (via @file syntax for image files)"],"output_types":["text (streaming response)","code (generated or refactored code blocks)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_1","uri":"capability://code.generation.editing.context.aware.code.generation.with.file.and.image.references","name":"context-aware-code-generation-with-file-and-image-references","description":"Enables users to reference multiple files and images within a single chat conversation using @file syntax, allowing the AI to generate or modify code with awareness of existing codebase context. The extension passes selected file contents and image data as part of the chat prompt to the LLM, enabling multi-file refactoring, cross-file bug fixes, and documentation generation. Image support allows users to include screenshots, diagrams, or design mockups as context for code generation.","intents":["I want to refactor code across multiple files while keeping the AI aware of dependencies","I need to fix a bug that spans multiple files by showing the AI the relevant code","I want to generate code based on a screenshot or design mockup","I need to add documentation or tests for multiple related files in one conversation"],"best_for":["developers working on multi-file refactoring or cross-module bug fixes","teams generating code from design mockups or architectural diagrams","developers needing to maintain context across related files without manual copy-paste"],"limitations":["No automatic project-wide indexing or dependency graph analysis — requires manual @file references","File size limits depend on LLM context window; large files may exceed token limits","Image understanding depends on LLM capability (not all models support vision equally)","No automatic .gitignore or sensitive file filtering — user responsible for not sharing secrets","Context is conversation-scoped; switching files requires re-referencing them in new conversation"],"requires":["Files must exist in VS Code workspace and be accessible via file picker","LLM provider must support image inputs (GPT-4V, Claude 3+, Gemini Pro Vision)","Sufficient context window in selected model to accommodate file contents + prompt"],"input_types":["text (chat prompt with @file references)","code files (referenced via @filename syntax)","images (PNG, JPG, GIF, WebP via @filename syntax)"],"output_types":["code (generated or refactored code blocks)","text (explanations, documentation)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_10","uri":"capability://tool.use.integration.multi.provider.api.key.management.with.secure.storage","name":"multi-provider-api-key-management-with-secure-storage","description":"Manages API keys for multiple LLM providers (OpenAI, Anthropic, Google, Azure OpenAI, DeepSeek, etc.) with secure storage in VS Code's credential store. Users configure one API key per provider in extension settings, and the extension routes requests to the appropriate provider based on selected model. Credentials are encrypted and stored locally, never transmitted to third parties.","intents":["I want to securely store API keys for multiple LLM providers","I need to switch between providers without re-entering credentials","I want to ensure API keys are not exposed in settings files or version control","I need to manage API keys for my team without sharing secrets"],"best_for":["developers using multiple LLM providers","teams with security requirements for credential management","organizations managing API keys across multiple team members"],"limitations":["Credentials stored locally in VS Code credential store; not synced across devices","No centralized credential management for teams — each user must configure their own keys","No audit trail or rotation mechanism for API keys","Credential store security depends on OS-level security (Windows Credential Manager, macOS Keychain, etc.)","No built-in key expiration or renewal reminders","Switching providers requires manual configuration; no automatic provider detection"],"requires":["API keys from selected providers (OpenAI, Anthropic, Google, etc.)","VS Code credential store available (OS-dependent)"],"input_types":["API key (text)"],"output_types":["authenticated API requests (internal)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_11","uri":"capability://safety.moderation.telemetry.free.operation.with.privacy.guarantee","name":"telemetry-free-operation-with-privacy-guarantee","description":"Explicitly disables all telemetry and usage data collection, ensuring user interactions, prompts, and code are never transmitted to extension maintainers or third parties beyond the selected LLM provider. This is a design choice differentiating Chat Copilot from many commercial AI tools that collect usage analytics. Users have full transparency that only LLM provider APIs receive conversation data.","intents":["I want to use an AI tool without worrying about usage tracking","I need privacy guarantees for sensitive code or proprietary projects","I want to ensure my interactions are not used for model training or analytics","I need compliance with data privacy regulations (GDPR, HIPAA, etc.)"],"best_for":["teams with strict privacy or compliance requirements","developers working on proprietary or sensitive code","organizations avoiding usage analytics collection","teams preferring open-source tools with transparent data practices"],"limitations":["No usage analytics available to extension maintainers for improvement insights","No crash reporting or error telemetry — bugs may go unreported","Users responsible for understanding LLM provider's data policies (OpenAI, Anthropic, etc. may collect data)","No built-in privacy audit or compliance verification tools","Users must trust extension code — no independent verification of telemetry claims"],"requires":["Trust in extension maintainer's privacy claims","Understanding of selected LLM provider's data policies"],"input_types":[],"output_types":[],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_2","uri":"capability://memory.knowledge.prompt.management.and.templating.system","name":"prompt-management-and-templating-system","description":"Provides a Prompt Manager feature allowing users to create, save, and reuse prompt templates with #hashtag-based lookup syntax. Templates can include placeholders and are searchable within the chat interface, enabling teams to standardize AI interactions for common tasks (code review, testing, documentation). The system stores prompts locally in VS Code settings, making them available across all projects and shareable via settings sync.","intents":["I want to save and reuse common prompts for code review, testing, or documentation","I need to standardize how my team asks the AI for specific tasks","I want to quickly access a library of prompts without typing them repeatedly","I need to share prompt templates across my team via VS Code settings sync"],"best_for":["teams with standardized code review or testing workflows","organizations wanting to enforce consistent AI interaction patterns","developers frequently repeating similar prompts for common tasks"],"limitations":["Prompts stored locally in VS Code settings; no centralized team prompt repository","No version control for prompt templates; changes overwrite previous versions","No analytics on prompt usage or effectiveness","Placeholder substitution mechanism unknown — may require manual variable replacement","Sharing requires VS Code Settings Sync enabled; no direct export/import mechanism documented"],"requires":["VS Code with Settings Sync enabled (for team sharing)","Manual creation of prompt templates via /manage-prompt command"],"input_types":["text (prompt template with optional placeholders)"],"output_types":["text (saved prompt template for reuse)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_3","uri":"capability://code.generation.editing.one.click.code.generation.and.file.creation","name":"one-click-code-generation-and-file-creation","description":"Allows users to generate new files or modify existing code directly from AI responses with single-click or keyboard-shortcut actions. The extension detects code blocks in AI responses and provides inline buttons to create files, apply patches, or insert code at cursor position. This eliminates manual copy-paste workflows and integrates code generation directly into the chat-to-editor pipeline.","intents":["I want to create a new file from AI-generated code without manual copy-paste","I need to apply AI-suggested code fixes to my current file with one click","I want to insert generated code snippets at my cursor position automatically","I need to quickly iterate on code generation without switching between chat and editor"],"best_for":["developers wanting rapid code generation and iteration workflows","teams using AI for boilerplate generation and scaffolding","solo developers prioritizing speed over manual code review"],"limitations":["No preview of changes before applying — requires manual review after insertion","Keyboard shortcuts not documented; discovery requires exploring UI","No automatic conflict detection if applying patches to modified files","Code block detection depends on markdown formatting in AI response; malformed blocks may not be recognized","No rollback mechanism — applied changes require manual undo"],"requires":["AI response containing code blocks in markdown format (triple backticks)","Write permissions to target file or directory"],"input_types":["code (from AI response in markdown format)"],"output_types":["code (written to file or inserted at cursor)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_4","uri":"capability://automation.workflow.conversation.export.and.history.management","name":"conversation-export-and-history-management","description":"Enables users to export chat conversations to Markdown format for documentation, knowledge base creation, or audit trails. Conversations can be edited and resent within the chat interface, allowing users to refine prompts and regenerate responses. The extension maintains conversation history within the current session but does not persist conversations across VS Code restarts without manual export.","intents":["I want to save a conversation for future reference or documentation","I need to export chat history for compliance or knowledge base purposes","I want to edit a previous prompt and regenerate the response","I need to share a conversation with teammates for review or learning"],"best_for":["teams maintaining AI-assisted development documentation","organizations with compliance requirements for AI interaction logs","developers creating knowledge bases from AI conversations","teams using AI conversations as learning materials"],"limitations":["No automatic conversation persistence — requires manual export to preserve history","Export format is Markdown only; no structured formats (JSON, CSV) for analysis","No built-in search or filtering across exported conversations","Conversation metadata (model used, timestamp, tokens consumed) not included in export","No automatic deduplication or cleanup of conversation history","Edited/resent prompts create new responses but don't update original conversation in export"],"requires":["Conversation history in current VS Code session","Write permissions to export destination"],"input_types":["conversation (chat history from current session)"],"output_types":["markdown file (exported conversation)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_5","uri":"capability://tool.use.integration.model.context.protocol.integration.for.custom.tools","name":"model-context-protocol-integration-for-custom-tools","description":"Supports Model Context Protocol (MCP) integration (v4.7.0+) enabling users to extend the AI's capabilities with custom tools and integrations. MCP allows the AI to call external functions, access databases, or interact with third-party services through a standardized protocol. The extension acts as an MCP client, translating tool calls from the LLM into actual function executions and returning results back to the conversation.","intents":["I want to give the AI access to custom tools or APIs specific to my project","I need the AI to query databases or external services as part of code generation","I want to extend the AI's capabilities with domain-specific integrations","I need the AI to execute functions and use results in subsequent code generation"],"best_for":["teams with custom internal tools or APIs needing AI integration","developers building AI agents with access to external systems","organizations with proprietary databases or services requiring AI access","teams wanting to extend AI capabilities beyond standard chat"],"limitations":["MCP integration details unknown — implementation specifics not documented","Tool availability depends on MCP server implementation; no built-in tool library","No automatic tool discovery — requires manual MCP server configuration","Tool call latency depends on external service response times","Error handling and timeout behavior for tool calls unknown","No built-in tool result caching or memoization"],"requires":["MCP server implementation for custom tools","Configuration of MCP server endpoint in extension settings","LLM provider supporting tool/function calling (GPT-4, Claude, etc.)"],"input_types":["tool definitions (via MCP protocol)","chat prompts requesting tool execution"],"output_types":["tool results (returned from MCP server)","code (generated using tool results)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_6","uri":"capability://planning.reasoning.reasoning.model.support.with.extended.thinking","name":"reasoning-model-support-with-extended-thinking","description":"Supports advanced reasoning models (OpenAI o1, o3-mini, DeepSeek R1) that perform extended chain-of-thought reasoning before generating responses. These models are optimized for complex problem-solving, mathematical reasoning, and code debugging by spending more compute on reasoning steps. The extension transparently routes prompts to reasoning models and handles their longer response times and different token accounting.","intents":["I need the AI to solve complex algorithmic or mathematical problems in code","I want deeper reasoning for debugging complex issues across multiple files","I need the AI to verify code correctness through step-by-step reasoning","I want to use advanced reasoning for architectural decisions or design reviews"],"best_for":["developers solving complex algorithmic problems","teams debugging intricate system issues requiring deep reasoning","developers wanting AI-assisted code verification and correctness checking","teams using AI for architectural decision-making"],"limitations":["Reasoning models significantly more expensive than standard models (higher token costs)","Response times much longer due to extended thinking (minutes vs seconds)","Token accounting differs from standard models; reasoning tokens may cost more","Not all reasoning models support all features (e.g., o1 may not support vision)","Reasoning output may be verbose; not suitable for quick iterations","Model availability and pricing subject to provider changes"],"requires":["API access to reasoning models (OpenAI o1/o3-mini, DeepSeek R1, or equivalent)","Sufficient API quota and budget for higher token costs","Patience for longer response times (reasoning models take minutes)"],"input_types":["text (complex problem statements)","code (for debugging or verification)"],"output_types":["text (reasoning steps and explanation)","code (verified or corrected code)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_7","uri":"capability://planning.reasoning.hybrid.reasoning.mode.with.deepclaude","name":"hybrid-reasoning-mode-with-deepclaude","description":"Provides DeepClaude hybrid mode (v4.6.7+) combining DeepSeek R1's reasoning capabilities with Claude's code generation strengths. This mode routes complex reasoning to DeepSeek R1, then uses Claude to generate final code based on reasoning output, optimizing for both problem-solving depth and code quality. The extension manages the multi-model pipeline transparently within a single conversation.","intents":["I want deep reasoning for complex problems but high-quality code generation","I need to combine the strengths of multiple models in a single workflow","I want reasoning transparency without sacrificing code quality","I need cost-effective reasoning by using specialized models for each stage"],"best_for":["teams wanting reasoning + code generation without manual model switching","developers solving complex problems requiring both thinking and implementation","organizations optimizing for reasoning quality and code quality simultaneously"],"limitations":["Requires API access to both DeepSeek R1 and Claude (two API keys, two providers)","Cost higher than single-model approach due to multi-model pipeline","Response time is sum of both model latencies (reasoning + generation)","Pipeline behavior and error handling unknown — undocumented feature","No control over reasoning-to-generation handoff or prompt engineering between stages","Availability depends on both DeepSeek and Anthropic API stability"],"requires":["API keys for both DeepSeek (or Ollama with DeepSeek R1) and Anthropic Claude","Sufficient API quota and budget for multi-model pipeline","DeepClaude mode enabled in extension settings"],"input_types":["text (complex problem statements)","code (for reasoning and generation)"],"output_types":["text (reasoning from DeepSeek R1)","code (generated by Claude based on reasoning)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_8","uri":"capability://text.generation.language.github.copilot.provider.integration","name":"github-copilot-provider-integration","description":"Integrates GitHub Copilot as a selectable LLM provider (v4.6.9+) within the Chat Copilot interface, allowing users to route conversations through GitHub Copilot's models instead of OpenAI or other providers. This enables teams already invested in GitHub Copilot to use Chat Copilot as a unified chat interface while maintaining their existing Copilot subscription and authentication.","intents":["I want to use GitHub Copilot models through Chat Copilot's interface","I need to consolidate multiple AI tools into a single VS Code chat interface","I want to leverage my existing GitHub Copilot subscription in Chat Copilot","I need to switch between GitHub Copilot and other providers in the same tool"],"best_for":["teams with existing GitHub Copilot subscriptions","developers wanting unified chat interface across multiple providers","organizations standardizing on GitHub Copilot but needing chat capabilities"],"limitations":["Requires active GitHub Copilot subscription","GitHub Copilot authentication mechanism unknown — may require GitHub login","Feature parity with other providers unknown — may have different capabilities","Rate limiting and quota management inherited from GitHub Copilot","No documentation on GitHub Copilot-specific configuration or limitations"],"requires":["Active GitHub Copilot subscription","GitHub authentication in VS Code","Chat Copilot v4.6.9 or later"],"input_types":["text (chat prompts)","code (via @file syntax)"],"output_types":["text (streaming response from GitHub Copilot)","code (generated code)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-cweijan-chat-copilot__cap_9","uri":"capability://text.generation.language.local.ollama.model.execution.with.custom.models","name":"local-ollama-model-execution-with-custom-models","description":"Supports local model execution via Ollama integration, allowing users to run open-source models (Llama, Qwen, DeepSeek R1, etc.) on their own hardware without cloud API costs. Users configure a custom model name and point to a local Ollama instance (default localhost:11434), enabling fully offline operation and complete data privacy. The extension treats Ollama as an OpenAI-compatible API endpoint, abstracting the local execution details.","intents":["I want to run AI models locally without sending code to cloud providers","I need complete data privacy and control over model execution","I want to avoid API costs by using open-source models on my hardware","I need to use custom or fine-tuned models not available through cloud providers"],"best_for":["teams with strict data privacy or security requirements","developers wanting to avoid cloud API costs","organizations with on-premise infrastructure","developers experimenting with open-source models","teams using custom or fine-tuned models"],"limitations":["Requires local Ollama installation and model download (10GB-100GB+ disk space)","Model quality and speed depend on local hardware (GPU recommended for reasonable performance)","No automatic model management — users responsible for downloading and updating models","Ollama API compatibility may lag behind OpenAI API changes","No built-in model performance monitoring or optimization","Requires localhost:11434 accessibility; not suitable for remote/cloud Ollama instances without tunneling"],"requires":["Ollama installed and running locally (https://ollama.ai)","Model downloaded via ollama pull (e.g., ollama pull qwen2.5)","Sufficient disk space for models (10GB-100GB+)","GPU recommended for reasonable inference speed (CPU-only very slow)","Custom Model field set to model name (e.g., qwen2.5, deepseek-r1)"],"input_types":["text (chat prompts)","code (via @file syntax)"],"output_types":["text (streaming response from local model)","code (generated code)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (minimum version unknown, likely 1.60+)","API key for at least one provider (OpenAI, Anthropic, Google, or local Ollama instance)","Network connectivity for cloud providers; localhost:11434 for Ollama","Valid OpenAI-compatible API endpoint URL (defaults to https://api.openai.com/v1)","Files must exist in VS Code workspace and be accessible via file picker","LLM provider must support image inputs (GPT-4V, Claude 3+, Gemini Pro Vision)","Sufficient context window in selected model to accommodate file contents + prompt","API keys from selected providers (OpenAI, Anthropic, Google, etc.)","VS Code credential store available (OS-dependent)","Trust in extension maintainer's privacy claims"],"failure_modes":["Requires active internet connection for cloud providers (OpenAI, Anthropic, Google); only Ollama supports offline operation","No built-in rate limiting or token quota management — relies on provider-level controls","Streaming latency depends on network and provider response time; no local caching of responses","Custom model support limited to OpenAI-compatible API format; proprietary APIs require wrapper","No conversation persistence across VS Code sessions without manual export","No automatic project-wide indexing or dependency graph analysis — requires manual @file references","File size limits depend on LLM context window; large files may exceed token limits","Image understanding depends on LLM capability (not all models support vision equally)","No automatic .gitignore or sensitive file filtering — user responsible for not sharing secrets","Context is conversation-scoped; switching files requires re-referencing them in new conversation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.51,"quality":0.34,"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":"2026-05-03T15:20:33.198Z","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=chat-copilot","compare_url":"https://unfragile.ai/compare?artifact=chat-copilot"}},"signature":"WWhxvoFGRwXcUu7ZEES2hhW5rDFvunduwXiUKDDBxOKkz+M+CBvv4C6GktqPgdxjyie1Ovxqf3gP+1A3Grd+Cw==","signedAt":"2026-06-21T01:22:08.242Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chat-copilot","artifact":"https://unfragile.ai/chat-copilot","verify":"https://unfragile.ai/api/v1/verify?slug=chat-copilot","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}