{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-smallcloud-codify","slug":"refact-open-source-ai-agent-code-generator-chat-for-javascript-python-typescript-java-php-go-and-more","name":"Refact – Open-Source AI Agent, Code Generator & Chat for JavaScript, Python, TypeScript, Java, PHP, Go, and more.","type":"agent","url":"https://marketplace.visualstudio.com/items?itemName=smallcloud.codify","page_url":"https://unfragile.ai/refact-open-source-ai-agent-code-generator-chat-for-javascript-python-typescript-java-php-go-and-more","categories":["ai-agents","automation"],"tags":["ai","autocomplete","bash","c","c#","c++","chat","chatgpt","code completion","coding","copilot","cpp","csharp","css","cursor","documentation","go","golang","haskell","html","intellicode","intellisense","java","javascript","julia","jupyter","keybindings","kite","kotlin","llm","lua","method completion","node","node.js","objective-c","ocaml","perl","php","python","react","refact","refact.ai","refactor","refactoring","ruby","rust","scss","security","snippets","swift","tabnine","test","typescript","vue"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-smallcloud-codify__cap_0","uri":"capability://code.generation.editing.context.aware.inline.code.completion.with.rag.based.snippet.retrieval","name":"context-aware inline code completion with rag-based snippet retrieval","description":"Provides real-time code suggestions within the VS Code editor using a locally-deployed Qwen2.5-Coder-1.5B model combined with Retrieval-Augmented Generation (RAG) to fetch project-specific code snippets. The system analyzes the current file context, retrieves semantically similar patterns from the codebase, and generates completions that align with existing code style and architecture, reducing latency by performing local inference rather than cloud round-trips.","intents":["I want autocomplete suggestions that understand my project's coding patterns and conventions","I need fast, offline-capable code completion without sending my code to external servers","I want completions that respect my codebase's architecture and naming conventions"],"best_for":["solo developers and small teams prioritizing code privacy","teams working with proprietary or sensitive codebases","developers in low-bandwidth environments requiring local inference"],"limitations":["Qwen2.5-Coder-1.5B model has lower accuracy than larger models (GPT-4, Claude) for complex multi-step logic","RAG retrieval quality depends on codebase documentation and code clarity; poorly documented projects yield weaker suggestions","No built-in persistence of completion preferences or learning from user acceptance/rejection patterns","Context window limited to current file plus retrieved snippets; cannot reason across entire codebase simultaneously"],"requires":["VS Code 1.80 or later (estimated based on extension API requirements)","Minimum 2GB RAM for local Qwen2.5-Coder-1.5B inference","Network connection for initial model download and RAG indexing"],"input_types":["source code (current file context)","codebase files (for RAG indexing)","cursor position and surrounding code"],"output_types":["code completion suggestions (single or multiple variants)","inline preview of suggested completion"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_1","uri":"capability://text.generation.language.in.ide.chat.interface.with.command.context.attachment","name":"in-ide chat interface with @-command context attachment","description":"Provides an integrated chat sidebar within VS Code that allows developers to ask questions and request code changes without leaving the editor. The system supports @-command syntax (@file, @web, @definition, @references, @tree) to explicitly attach context sources, enabling precise control over what information the AI model receives. This architecture avoids context pollution by letting users selectively include relevant code snippets, definitions, or external information rather than sending entire projects.","intents":["I want to ask my AI assistant about my code without switching to a separate chat window","I need to reference specific files, definitions, or web resources in my questions","I want to explore my codebase structure and understand relationships between components"],"best_for":["developers who want seamless AI assistance without context switching","teams using complex codebases requiring precise context selection","developers who need to reference external documentation or web resources in code discussions"],"limitations":["Manual @-command syntax required; no automatic context inference, so users must explicitly specify what context to include","Chat history is not persisted across VS Code sessions by default (persistence mechanism unknown)","@web command requires network access and may have rate limiting on web scraping","@definition and @references require accurate symbol resolution, which may fail in dynamically-typed languages or with complex metaprogramming"],"requires":["VS Code 1.80 or later","Refact extension installed and activated","API key for selected LLM provider (Claude, GPT-4, Gemini, etc.) or use of free tier with rate limits"],"input_types":["natural language text (chat messages)","code snippets (via @file, @definition)","file paths (via @file, @tree)","web URLs (via @web)","images (via image upload button)"],"output_types":["natural language responses","code suggestions and refactorings","explanations and documentation","structured codebase information (via @tree)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_10","uri":"capability://search.retrieval.definition.lookup.and.cross.reference.attachment.with.definition.and.references.commands","name":"definition lookup and cross-reference attachment with @definition and @references commands","description":"Provides @definition and @references commands that enable developers to attach symbol definitions and all usage locations to chat messages. The @definition command retrieves the definition of a symbol (function, class, variable) at the cursor position, while @references finds all locations where that symbol is used. This allows developers to provide the AI with complete context about how a symbol is defined and used across the codebase without manually copying code snippets.","intents":["I want to show the AI how a function is defined and used throughout the codebase","I need to understand all places where a class or interface is referenced before refactoring it","I want to provide the AI with complete context about a symbol's usage patterns"],"best_for":["developers refactoring symbols with many usages","teams understanding impact of changes to widely-used functions or classes","developers working with unfamiliar code needing to understand symbol usage patterns"],"limitations":["Symbol resolution depends on language-specific tooling; may fail for dynamically-typed languages or complex metaprogramming","Definition lookup may return incorrect results for overloaded functions or methods with same name in different classes","References may miss indirect usages (reflection, dynamic dispatch, string-based lookups)","No filtering of results; all references are returned without prioritization or grouping by usage pattern","Performance may degrade for symbols with thousands of usages"],"requires":["Language server or symbol resolution capability for the project's language","Cursor positioned on a valid symbol in the editor"],"input_types":["@definition command (retrieves definition of symbol at cursor)","@references command (retrieves all usages of symbol at cursor)"],"output_types":["symbol definition (code snippet with location)","list of reference locations (file paths and line numbers)","usage context for each reference"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_11","uri":"capability://search.retrieval.web.context.attachment.and.external.documentation.integration.with.web.command","name":"web context attachment and external documentation integration with @web command","description":"Provides a @web command that allows developers to attach web pages, documentation, or external resources to chat messages by URL. The system fetches and parses the web content, extracting relevant information and including it in the AI's context. This enables developers to reference external APIs, documentation, design specifications, or standards without manually copying content, and allows the AI to generate code that conforms to external specifications.","intents":["I want to reference an API documentation page when asking the AI to implement an integration","I need to show the AI a design specification or requirements document from a web link","I want the AI to generate code that conforms to an external standard or specification"],"best_for":["developers integrating with external APIs or services","teams using web-based documentation and specifications","projects requiring conformance to external standards or specifications"],"limitations":["Web scraping may fail for JavaScript-heavy sites or pages requiring authentication","Content extraction may be inaccurate for complex HTML layouts or dynamically-loaded content","Rate limiting on web requests; frequent @web commands may be throttled","No caching of fetched content; repeated references to same URL require re-fetching","Privacy concern: web URLs are transmitted to Refact servers (unless on-premise deployment) for fetching and parsing"],"requires":["Network connectivity to external websites","Publicly accessible URLs (authentication-protected pages may not work)","Web content in text-extractable format (HTML, PDF, etc.)"],"input_types":["@web command with URL","natural language context about what information is needed from the URL"],"output_types":["extracted web content (text, code examples, specifications)","context-aware code generation based on external documentation"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_12","uri":"capability://automation.workflow.free.tier.with.unlimited.basic.features.and.optional.paid.enhancements","name":"free tier with unlimited basic features and optional paid enhancements","description":"Offers a freemium pricing model with unlimited access to basic features (inline code completion, chat interface, context attachment) at no cost, while optional paid features or higher usage tiers may require subscription. The free tier includes the local Qwen2.5-Coder-1.5B model for completions and basic chat access, with paid tiers likely offering access to more powerful cloud models (Claude, GPT-4, Gemini) and higher rate limits. This enables developers to use Refact without financial commitment while providing monetization for advanced features.","intents":["I want to try Refact.ai without paying upfront","I need unlimited code completion and basic chat without subscription costs","I want to upgrade to more powerful models only when I need them"],"best_for":["individual developers and small teams with limited budgets","developers evaluating Refact.ai before committing to paid plans","projects with modest AI assistance needs that fit within free tier"],"limitations":["Free tier limits unknown; unclear what constitutes 'unlimited' (rate limits, monthly usage caps, etc.)","Paid tier features and pricing unknown; no published pricing information","Free tier may be limited to local Qwen2.5-Coder-1.5B model; access to Claude/GPT-4 may require payment","Upgrade path and feature differentiation between tiers unknown","Free tier sustainability unknown; service may introduce restrictions or pricing changes in future"],"requires":["Free Refact.ai account (registration mechanism unknown)","VS Code extension installed"],"input_types":["user account and subscription tier selection"],"output_types":["access to free tier features","optional upgrade to paid features"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_13","uri":"capability://planning.reasoning.swe.bench.verified.autonomous.agent.leaderboard.ranking","name":"swe-bench verified autonomous agent leaderboard ranking","description":"Claims to rank #1 on the SWE-bench verified leaderboard for free open-source AI agents, a standardized benchmark measuring autonomous software engineering task completion. The leaderboard evaluates agents on their ability to autonomously resolve GitHub issues, implement features, and fix bugs in real-world repositories. This ranking serves as a third-party validation of the agent's capabilities, though the specific evaluation methodology, test set, and performance metrics are not detailed in available documentation.","intents":["I want to use an AI agent with proven autonomous task completion capabilities","I need confidence that the tool can handle real-world software engineering tasks","I want to evaluate Refact.ai against other autonomous agents using standardized benchmarks"],"best_for":["teams evaluating autonomous agents for production use","organizations wanting third-party validation of agent capabilities","developers interested in state-of-the-art autonomous software engineering"],"limitations":["SWE-bench leaderboard position claimed but not independently verified in provided documentation","Benchmark may not reflect real-world task complexity or domain-specific challenges","Leaderboard ranking may change as other agents improve; current #1 ranking may not be sustained","Evaluation methodology and test set unknown; unclear what types of tasks are measured","Performance on proprietary codebases may differ significantly from open-source benchmark repositories"],"requires":["Access to SWE-bench leaderboard for verification (external resource)","Understanding of benchmark evaluation criteria"],"input_types":["benchmark task specifications (GitHub issues, feature requests)"],"output_types":["autonomous task completion (code changes, pull requests)","leaderboard ranking and performance metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.orchestration.with.model.selection.per.task","name":"multi-provider llm orchestration with model selection per task","description":"Abstracts multiple LLM providers (Claude 3.7/4 Sonnet, GPT-4.1/4o, o3-mini, Gemini 2.5 Pro) behind a unified interface, allowing users to select different models for different tasks based on complexity and cost. The system routes requests to the appropriate provider based on user configuration, supporting both cloud-hosted models and on-premise deployments. Users can bring their own API keys (BYOK) for any supported provider, maintaining control over billing and data routing.","intents":["I want to use different AI models for different tasks (fast models for simple completions, powerful models for complex reasoning)","I need to control which LLM provider processes my code for privacy or compliance reasons","I want to manage my own API keys and billing without vendor lock-in"],"best_for":["enterprises with specific LLM provider requirements or compliance constraints","developers optimizing for cost by using cheaper models for simple tasks and expensive models for complex reasoning","teams requiring on-premise deployment for data sovereignty"],"limitations":["No automatic model selection based on task complexity; users must manually choose models, requiring domain knowledge of model capabilities","Switching between providers may introduce latency variance (Claude API vs OpenAI API response times differ)","No built-in fallback mechanism if primary provider is unavailable; requires manual reconfiguration","On-premise deployment details unknown; unclear whether all models support self-hosted inference or only API-based access"],"requires":["API keys for at least one supported LLM provider (OpenAI, Anthropic, Google, etc.)","Network connectivity to provider APIs (unless on-premise deployment is configured)","VS Code settings configuration to specify model preferences"],"input_types":["code context (from editor or @-commands)","natural language prompts (from chat or inline requests)","model selection configuration (user preference)"],"output_types":["LLM responses routed from selected provider","structured data (code, explanations, refactorings)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_3","uri":"capability://automation.workflow.autonomous.end.to.end.task.execution.with.external.tool.integration","name":"autonomous end-to-end task execution with external tool integration","description":"Enables the AI agent to autonomously execute multi-step software engineering tasks by integrating with external tools including GitHub/GitLab (version control), PostgreSQL/MySQL (databases), Docker (containerization), Python debugger (pdb), shell commands, and MCP (Model Context Protocol). The system decomposes high-level user requests into executable subtasks, invokes appropriate tools, interprets results, and iteratively refines execution until task completion. This architecture allows the agent to modify code, run tests, commit changes, and deploy without manual intervention.","intents":["I want the AI to automatically implement a feature end-to-end, including code changes, tests, and commits","I need the AI to debug failing tests by running them, analyzing output, and proposing fixes","I want to automate repetitive tasks like database migrations, Docker builds, or shell script execution"],"best_for":["teams with well-defined CI/CD pipelines and automated testing infrastructure","developers working on greenfield projects with clear requirements and low ambiguity","organizations with strong code review processes to validate AI-generated changes before merge"],"limitations":["Autonomous execution without human approval introduces risk of unintended code changes or data modifications; requires robust rollback mechanisms","Tool integration reliability depends on external service availability (GitHub API rate limits, database connectivity, Docker daemon stability)","No built-in guardrails for destructive operations (database deletes, force pushes); requires careful permission scoping","MCP integration details unknown; unclear which MCP servers are supported or how protocol negotiation works","Success rate on complex, ambiguous tasks unknown; SWE-bench leaderboard position claimed but not independently verified"],"requires":["Configured credentials for integrated tools (GitHub token, database connection string, Docker socket, etc.)","Appropriate permissions in external systems (write access to repositories, database modification rights, Docker execution capability)","Well-structured codebase with clear conventions and automated testing","Network connectivity to external services or local tool availability"],"input_types":["natural language task description (from chat)","code context (current project state)","tool configuration (credentials, endpoints)"],"output_types":["code changes (commits to version control)","test results and debugging output","deployment artifacts (Docker images, database migrations)","execution logs and step-by-step reasoning"],"categories":["automation-workflow","planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_4","uri":"capability://memory.knowledge.codebase.wide.semantic.understanding.with.rag.indexed.retrieval","name":"codebase-wide semantic understanding with rag-indexed retrieval","description":"Indexes the entire project codebase into a vector database, enabling semantic search and retrieval of relevant code snippets based on natural language queries or code context. When a user asks a question or requests a change, the system retrieves the most semantically similar code patterns, definitions, and implementations from the index, providing the LLM with precise, project-specific context. This approach scales to large codebases by avoiding full-context transmission and instead fetching only the most relevant snippets.","intents":["I want to understand how a specific feature is implemented across my codebase","I need to find all usages of a particular function or pattern to ensure consistency","I want the AI to generate code that matches my project's existing patterns and conventions"],"best_for":["teams with large, complex codebases (10k+ lines) where full-context transmission is impractical","projects with strong architectural patterns that should be replicated in new code","developers needing to understand legacy code or unfamiliar modules"],"limitations":["RAG retrieval quality depends on code documentation and naming clarity; poorly documented code yields weak semantic matches","Vector embeddings may miss domain-specific patterns or business logic that isn't explicitly named in code","Initial indexing requires time and computational resources; incremental updates may lag behind recent code changes","No built-in mechanism to weight recent changes higher than old code, potentially retrieving outdated patterns","Semantic search may retrieve false positives (similar-looking code with different intent) without human validation"],"requires":["Codebase indexed into vector database (automatic on extension activation)","Sufficient disk space for vector embeddings (typically 10-20% of source code size)","Network connectivity for embedding generation (unless local embedding model is used)"],"input_types":["natural language queries (from chat)","code context (current file or selection)","codebase files (for indexing)"],"output_types":["ranked list of semantically similar code snippets","file paths and line numbers for retrieved code","context-aware suggestions based on retrieved patterns"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_5","uri":"capability://code.generation.editing.multi.language.code.generation.and.refactoring.with.style.adaptation","name":"multi-language code generation and refactoring with style adaptation","description":"Generates and refactors code across 25+ programming languages (Python, JavaScript, TypeScript, Java, Rust, Go, PHP, C++, C#, etc.) while automatically adapting to the project's coding style, naming conventions, and architectural patterns. The system analyzes existing code to infer style preferences (indentation, naming, error handling patterns) and applies them to generated code, ensuring consistency without explicit configuration. Supports both single-file refactorings and cross-file changes that maintain referential integrity.","intents":["I want to generate code in my project's language that matches my team's style and conventions","I need to refactor a function while preserving its interface and maintaining consistency across all call sites","I want to migrate code between languages while preserving logic and adapting to target language idioms"],"best_for":["polyglot teams working across multiple programming languages","projects with strong style guides and architectural conventions","developers performing large-scale refactorings across multiple files"],"limitations":["Style adaptation relies on analyzing existing code; projects with inconsistent styles may produce inconsistent output","Cross-file refactorings may miss indirect dependencies or dynamic references (reflection, metaprogramming)","Language-specific idioms may not be perfectly adapted; generated code may be syntactically correct but not idiomatic","No built-in validation that generated code compiles or passes type checking; requires manual review and testing","Refactoring safety depends on test coverage; projects without comprehensive tests risk introducing bugs"],"requires":["Source code in supported language (25+ languages supported)","Codebase indexed for style analysis (automatic)","Language-specific tooling for validation (compiler, linter, type checker) if automated verification is desired"],"input_types":["source code (file or selection)","natural language refactoring request (from chat)","target language specification (for cross-language generation)"],"output_types":["refactored code (single or multiple files)","code generation suggestions","diff view showing changes"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_6","uri":"capability://image.visual.image.based.code.context.and.visual.documentation.analysis","name":"image-based code context and visual documentation analysis","description":"Accepts images (screenshots, diagrams, architecture drawings) as context input in the chat interface, enabling developers to reference visual documentation, UI mockups, or system diagrams when requesting code changes. The system analyzes images using vision capabilities to extract relevant information and incorporates it into code generation or explanation tasks. This enables developers to describe requirements visually rather than in text, reducing ambiguity in complex architectural or UI-related tasks.","intents":["I want to show the AI a screenshot of a UI and have it generate the corresponding code","I need to reference an architecture diagram when asking for implementation guidance","I want to upload a design mockup and have the AI generate responsive HTML/CSS"],"best_for":["frontend developers working from design mockups or screenshots","teams using visual documentation (architecture diagrams, flowcharts) as requirements","developers prototyping UIs quickly from visual references"],"limitations":["Image analysis quality depends on image clarity and resolution; low-quality or complex diagrams may be misinterpreted","Vision models may struggle with hand-drawn diagrams or non-standard notation","No OCR for extracting text from images; text in diagrams may not be reliably extracted","Image upload mechanism and size limits unknown; may have restrictions on file size or format","Generated code from visual references may require significant refinement; vision-based code generation is less reliable than text-based"],"requires":["Image file in supported format (JPEG, PNG, WebP, etc. — specific formats unknown)","LLM provider with vision capabilities (Claude 3.7+, GPT-4o, Gemini 2.5 Pro)","Image upload button in chat interface"],"input_types":["image files (screenshots, diagrams, mockups)","natural language description (accompanying the image)"],"output_types":["code generated from visual reference","explanations of visual content","structured data extracted from diagrams"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_7","uri":"capability://automation.workflow.on.premise.deployment.with.full.codebase.privacy.control","name":"on-premise deployment with full codebase privacy control","description":"Supports self-hosted deployment of Refact.ai infrastructure, allowing organizations to run the entire system (indexing, inference, chat backend) within their own infrastructure without transmitting code to external servers. This enables compliance with data sovereignty requirements, intellectual property protection, and regulatory constraints (HIPAA, GDPR, etc.). Users can configure which LLM providers to use (local models or private API endpoints) and maintain complete control over data retention and processing.","intents":["I need to ensure my proprietary code never leaves our infrastructure for compliance or IP protection reasons","I want to deploy Refact.ai in an air-gapped environment without external internet access","I need to maintain full control over data retention and model training data usage"],"best_for":["enterprises with strict data governance requirements (financial services, healthcare, government)","organizations with proprietary algorithms or trade secrets requiring maximum security","teams in regulated industries (HIPAA, GDPR, SOC 2) requiring data residency guarantees"],"limitations":["On-premise deployment requires significant infrastructure investment (servers, GPU resources, networking)","Maintenance and updates responsibility shifts to the organization; no automatic patching or updates","Local LLM inference (Qwen2.5-Coder-1.5B) has lower accuracy than cloud models; using cloud providers requires network access","Deployment configuration and scaling guidance unknown; no documentation provided on hardware requirements or multi-node setup","Support and troubleshooting may be limited compared to cloud SaaS offering"],"requires":["On-premise infrastructure (servers, storage, GPU resources for local inference)","Docker or Kubernetes for containerized deployment (assumed)","Network isolation or VPN for secure communication","System administration expertise for deployment and maintenance","Local LLM model or configured API endpoints for external providers"],"input_types":["deployment configuration (infrastructure specs, model selection)","codebase (for local indexing)"],"output_types":["self-hosted Refact.ai instance","indexed codebase within private infrastructure"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_8","uri":"capability://planning.reasoning.custom.system.prompt.configuration.for.personalized.ai.behavior","name":"custom system prompt configuration for personalized ai behavior","description":"Allows users to define custom system prompts that shape the AI's behavior, tone, and reasoning approach without modifying the underlying model. Users can specify preferences for code style, documentation requirements, error handling philosophy, or domain-specific conventions. The custom prompt is prepended to all requests, influencing how the AI interprets tasks and generates responses. This enables teams to enforce organizational standards and coding philosophies at the AI level.","intents":["I want the AI to always generate code with comprehensive error handling and logging","I need the AI to follow my team's specific architectural patterns and design principles","I want to enforce documentation standards (docstrings, comments) in all generated code"],"best_for":["teams with strong coding standards and architectural philosophies","organizations wanting to embed organizational best practices into AI-assisted development","projects requiring domain-specific conventions (e.g., financial systems, medical software)"],"limitations":["Custom prompt effectiveness depends on prompt engineering skill; poorly written prompts may be ignored or misinterpreted","No validation that custom prompts are followed; AI may deviate from custom instructions in complex scenarios","Prompt injection vulnerabilities if user input is not sanitized; malicious prompts in chat could override custom system prompt","Configuration mechanism unknown; unclear whether prompts are stored per-user, per-project, or globally","No built-in testing or evaluation of custom prompt effectiveness"],"requires":["Access to custom prompt configuration (location and mechanism unknown)","Understanding of prompt engineering principles","Ability to test and iterate on prompts"],"input_types":["custom system prompt text (natural language instructions)"],"output_types":["AI behavior modified according to custom prompt","code and responses reflecting custom preferences"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-smallcloud-codify__cap_9","uri":"capability://search.retrieval.workspace.structure.exploration.and.navigation.with.tree.command","name":"workspace structure exploration and navigation with @tree command","description":"Provides a @tree command that displays the project's directory and file structure within the chat interface, enabling developers to explore and understand codebase organization without manually navigating the file system. The system generates a hierarchical view of the project, helping developers understand module organization, identify relevant files for tasks, and provide context to the AI about project structure. 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