{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion","slug":"iflow","name":"iFlow","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=iflow-cli.iflow-cli-vscode-ide-companion","page_url":"https://unfragile.ai/iflow","categories":["code-editors"],"tags":["ai","autocomplete","bash","c","c#","c++","cpp","csharp","css","go","golang","haskell","html","intellicode","intellisense","java","javascript","julia","jupyter","keybindings","kite","kotlin","lua","method completion","node","node.js","nodejs","objectivec","objective-c","ocaml","perl","php","python","react","ruby","rust","snippets","swift","typescript"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion__cap_0","uri":"capability://code.generation.editing.repository.context.aware.code.completion","name":"repository-context-aware code completion","description":"Provides AI-powered code suggestions that incorporate understanding of the entire repository structure and codebase semantics. The extension transmits the currently open file and user-selected text to the iFlow CLI component, which analyzes repository context to generate contextually relevant completions across 20+ programming languages including JavaScript, Python, Java, TypeScript, Go, Rust, and others. Completions are delivered inline within the VS Code editor.","intents":["I want code completions that understand my project's architecture and coding patterns, not just generic suggestions","I need faster code writing without leaving the editor to search for similar patterns in my codebase","I want completions that respect my project's conventions and dependencies"],"best_for":["developers working on multi-file projects with consistent coding patterns","teams using monorepos or large codebases where context matters","polyglot developers working across multiple languages in one project"],"limitations":["Repository indexing mechanism and scope unknown — unclear if entire codebase is indexed or analyzed on-demand","No documented file size limits or binary file handling specifications","Performance impact during initial repository analysis not quantified","Compatibility with other code completion extensions (GitHub Copilot, Codeium) not documented","No information on how it handles monorepos or workspace configurations"],"requires":["Visual Studio Code (minimum version unknown)","iFlow CLI component installed and configured","Repository initialized with `/init` command","Network connectivity for cloud-based model inference (assumed)"],"input_types":["source code (current file context)","selected text in editor","repository file structure"],"output_types":["inline code suggestions","completion snippets"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion__cap_1","uri":"capability://search.retrieval.repository.wide.code.question.answering","name":"repository-wide code question-answering","description":"Enables developers to ask natural language questions about their codebase and receive answers grounded in repository-wide code analysis. The extension passes queries through the iFlow CLI to an AI model that searches and comprehends the entire repository to answer questions about code purpose, feature locations, architectural patterns, and implementation details. Responses are delivered within the VS Code interface.","intents":["I need to understand what a specific feature does without manually searching through files","I want to find where a particular function or component is implemented across my codebase","I need to understand the architectural purpose of a module or subsystem","I want to discover how a particular pattern is implemented in my existing code"],"best_for":["developers onboarding to unfamiliar codebases","teams maintaining large legacy systems","developers working across multiple repositories with similar architectures","code reviewers needing quick context on implementation details"],"limitations":["Search accuracy and comprehensiveness not documented — unclear how it handles ambiguous queries or partial matches","No information on response latency or timeout behavior for large repositories","Unclear whether it searches only code or also documentation, comments, and commit history","No documented limitations on query complexity or answer length","Accuracy metrics and hallucination rates not provided"],"requires":["Visual Studio Code","iFlow CLI component","Repository initialized with `/init` command","Network connectivity for AI model inference"],"input_types":["natural language questions","repository code context"],"output_types":["natural language answers","code references and locations"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion__cap_2","uri":"capability://code.generation.editing.automated.code.generation.from.specifications","name":"automated code generation from specifications","description":"Generates new code files and project structures from natural language specifications or requirements. The extension accepts specification input and orchestrates the iFlow CLI to automatically create, read, write, and execute files within the project, enabling 0-to-1 and 1-to-n project development workflows. The system handles file creation, modification, and execution without requiring manual file management.","intents":["I want to scaffold a new feature or module from a description without manually creating boilerplate","I need to generate multiple related files (components, tests, configs) from a single specification","I want to automate repetitive code generation tasks across my project"],"best_for":["developers building new features in established projects with consistent patterns","teams with standardized project structures and code generation templates","rapid prototyping and MVP development scenarios"],"limitations":["No documentation on generation success rates, rollback mechanisms, or error handling","Unclear whether generated code requires manual review or can be directly committed","No information on how it handles dependencies, imports, or integration with existing code","File execution capabilities not documented — unclear what 'automatic execution' means or what safety guardrails exist","No version control integration documented (git staging, commit messages, etc.)","Unclear how it handles merge conflicts or overwrites of existing files"],"requires":["Visual Studio Code","iFlow CLI component with file system write permissions","Repository initialized with `/init` command","Network connectivity for AI model inference"],"input_types":["natural language specifications","feature descriptions","code patterns from existing codebase"],"output_types":["generated source code files","project structure modifications","configuration files"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion__cap_3","uri":"capability://code.generation.editing.multi.language.code.completion.across.20.languages","name":"multi-language code completion across 20+ languages","description":"Provides AI code completion support for a broad range of programming languages including JavaScript, Python, Java, TypeScript, Go, Rust, C, C++, C#, PHP, Ruby, Swift, Kotlin, Haskell, OCaml, Perl, Julia, Lua, Objective-C, and others. The extension uses language-agnostic AI models to generate contextually appropriate suggestions for each language's syntax, idioms, and conventions without requiring language-specific plugins.","intents":["I work across multiple programming languages and want consistent AI assistance across all of them","I need code completion for less common languages that other tools don't support well","I want to switch languages within a project without losing code completion capabilities"],"best_for":["polyglot developers and teams using multiple languages","organizations with legacy codebases in less common languages","developers learning new languages who benefit from AI-assisted syntax"],"limitations":["Language-specific idiom accuracy not documented — unclear if suggestions respect language conventions equally","No information on which language versions are supported (e.g., Python 2 vs 3, Java 8 vs 17)","Performance characteristics may vary significantly across languages due to training data distribution","No documentation on handling polyglot files (e.g., JavaScript with embedded SQL or HTML)"],"requires":["Visual Studio Code with language support for target language","iFlow CLI component","File extension or language mode detection by VS Code"],"input_types":["source code in any supported language","language-specific syntax context"],"output_types":["language-appropriate code completions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion__cap_4","uri":"capability://tool.use.integration.editor.state.context.relay.to.cli","name":"editor state context relay to cli","description":"Automatically captures and transmits the current editor state (open file, selected text, cursor position) from VS Code to the iFlow CLI component for use in AI analysis and generation. This integration point enables the CLI to maintain awareness of what the developer is currently working on without requiring manual context specification. The mechanism for context transmission (IPC, stdio, API calls) is undocumented.","intents":["I want AI features to automatically understand what I'm currently editing without manual context setup","I need the CLI to have access to my current selection for targeted code generation or analysis","I want seamless integration between the VS Code UI and backend AI processing"],"best_for":["developers who want minimal friction between editor and AI features","teams using iFlow CLI as a standalone tool and wanting VS Code integration"],"limitations":["Context transmission mechanism not documented — unclear if it's real-time or on-demand","No information on latency or synchronization guarantees between editor and CLI","Unclear how large files or selections are handled — potential performance impact","No documentation on whether context is cached or re-transmitted on each operation","Security implications of context transmission not addressed (local vs remote processing)"],"requires":["Visual Studio Code extension installed","iFlow CLI component running and accessible","IPC or network connectivity between extension and CLI"],"input_types":["current file content","selected text","cursor position","editor state"],"output_types":["context data transmitted to CLI"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion__cap_5","uri":"capability://memory.knowledge.repository.initialization.and.indexing","name":"repository initialization and indexing","description":"Provides a `/init` command that prepares a repository for iFlow analysis by building an internal index or semantic representation of the codebase. This initialization step enables subsequent code completion, Q&A, and generation features to operate with full repository context. The indexing mechanism, scope, and performance characteristics are undocumented.","intents":["I need to set up iFlow for a new repository before using its features","I want to rebuild the repository index after significant codebase changes","I need to ensure iFlow has current understanding of my project structure"],"best_for":["developers setting up iFlow for the first time on a project","teams with large repositories that benefit from explicit indexing","projects where repository structure changes frequently"],"limitations":["Indexing time and resource requirements not documented","Unclear whether indexing is incremental or full rebuild on each `/init` call","No information on how it handles large repositories (>100k files, >1GB code)","Unclear whether indexing includes git history, dependencies, or only source files","No documentation on index storage location or size","Unknown whether index is cached across VS Code sessions or rebuilt each time"],"requires":["Visual Studio Code with iFlow extension installed","iFlow CLI component","Repository with source code files","Sufficient disk space for index storage (size unknown)"],"input_types":["repository file structure","source code files"],"output_types":["repository index or semantic representation"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iflow-cli-iflow-cli-vscode-ide-companion__cap_6","uri":"capability://planning.reasoning.feature.suggestion.and.discovery","name":"feature suggestion and discovery","description":"Analyzes the repository structure and existing code patterns to suggest new features, improvements, or missing functionality that aligns with the project's architecture and conventions. The system identifies gaps in implementation, recommends architectural patterns based on existing code, and suggests features that would complement the current codebase.","intents":["I want suggestions for features that would naturally fit my project's architecture","I need to identify gaps or missing functionality in my codebase","I want to discover architectural patterns used in my project and apply them elsewhere"],"best_for":["product teams planning feature roadmaps","developers refactoring or extending existing systems","teams seeking to improve code consistency and architectural coherence"],"limitations":["Suggestion quality and relevance metrics not documented","Unclear whether suggestions are based on code analysis, industry best practices, or both","No information on how it handles domain-specific requirements or business constraints","Unclear how it prioritizes suggestions or handles conflicting recommendations","No documentation on false positive rates or suggestion accuracy"],"requires":["Visual Studio Code with iFlow extension","Repository initialized with `/init` command","Sufficient repository history and code patterns for analysis"],"input_types":["repository structure","existing code patterns","architectural patterns"],"output_types":["feature suggestions","architectural recommendations","improvement proposals"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (minimum version unknown)","iFlow CLI component installed and configured","Repository initialized with `/init` command","Network connectivity for cloud-based model inference (assumed)","Visual Studio Code","iFlow CLI component","Network connectivity for AI model inference","iFlow CLI component with file system write permissions","Visual Studio Code with language support for target language","File extension or language mode detection by VS Code"],"failure_modes":["Repository indexing mechanism and scope unknown — unclear if entire codebase is indexed or analyzed on-demand","No documented file size limits or binary file handling specifications","Performance impact during initial repository analysis not quantified","Compatibility with other code completion extensions (GitHub Copilot, Codeium) not documented","No information on how it handles monorepos or workspace configurations","Search accuracy and comprehensiveness not documented — unclear how it handles ambiguous queries or partial matches","No information on response latency or timeout behavior for large repositories","Unclear whether it searches only code or also documentation, comments, and commit history","No documented limitations on query complexity or answer length","Accuracy metrics and hallucination rates not provided","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.48,"quality":0.24,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:42.146Z","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=iflow","compare_url":"https://unfragile.ai/compare?artifact=iflow"}},"signature":"1PTBBjxs2d0O5+/LGUusiRWWwol0EDM7+siNbEW09ypFLxtETUjQa3NQCkCLx2FltgprmrBQpmnX9hPGZ+nnCA==","signedAt":"2026-06-15T14:12:02.837Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/iflow","artifact":"https://unfragile.ai/iflow","verify":"https://unfragile.ai/api/v1/verify?slug=iflow","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"}}