{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ruslan-cybersec-ollama-code-fixer","slug":"ollama-code-fixer-ai-coding-assistant","name":"Ollama Code Fixer - AI Coding Assistant","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=Ruslan-Cybersec.ollama-code-fixer","page_url":"https://unfragile.ai/ollama-code-fixer-ai-coding-assistant","categories":["code-editors","testing-quality"],"tags":["ai","artificial intelligence","code assistant","code fixer","code generator","code optimizer","codellama","llama","local ai","ollama","programming assistant","refactoring","security","testing"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_0","uri":"capability://code.generation.editing.local.model.powered.code.error.detection.and.fixing","name":"local-model-powered code error detection and fixing","description":"Analyzes selected code blocks using local Ollama models (default: CodeLlama 7B) to identify syntax errors, logic bugs, and runtime issues, then generates corrected code with explanations. The extension sends the selected code as context to the local Ollama API endpoint (default http://localhost:11434), receives the fixed version, and presents it in a preview before applying changes. This approach eliminates cloud dependency and API costs while maintaining full code privacy on the developer's machine.","intents":["I want to quickly fix compilation errors in my code without leaving the editor","I need to identify and correct logic bugs in a function I just wrote","I want to understand what's wrong with my code and get a corrected version"],"best_for":["solo developers and small teams prioritizing code privacy","developers in air-gapped or offline environments","teams with strict data governance policies prohibiting cloud code transmission"],"limitations":["Accuracy depends on local model quality (7B CodeLlama has lower accuracy than GPT-4 or Claude 3)","No access to project-wide context or build system diagnostics — only analyzes selected code in isolation","Inference latency varies with local hardware (typically 5-30 seconds for 7B model on CPU)","Cannot detect errors requiring runtime execution or external dependency analysis"],"requires":["VS Code 1.85.0 or higher","Ollama installed and running locally (http://localhost:11434 by default)","CodeLlama or compatible model downloaded via Ollama (minimum 7B parameter model)","Selected code block in VS Code editor"],"input_types":["source code (any language supported by selected model)","code fragments or complete functions"],"output_types":["corrected source code","explanation text describing the fixes applied"],"categories":["code-generation-editing","local-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_1","uri":"capability://code.generation.editing.code.performance.and.readability.optimization","name":"code performance and readability optimization","description":"Sends selected code to the local Ollama model with an optimization prompt, requesting improvements to algorithmic efficiency, memory usage, and code readability. The model analyzes the code structure and generates refactored versions with explanations of optimizations applied (e.g., reducing time complexity, removing redundant operations, improving variable naming). Results are previewed in the editor before application, with optional automatic backup of the original code.","intents":["I want to improve the performance of a slow function without rewriting it from scratch","I need to make my code more readable and maintainable for my team","I want suggestions on how to reduce memory usage in this algorithm"],"best_for":["developers optimizing performance-critical code paths","teams conducting code reviews and seeking readability improvements","junior developers learning optimization patterns from AI suggestions"],"limitations":["Optimization suggestions may not account for language-specific runtime characteristics or compiler optimizations","Cannot measure actual performance impact — suggestions are heuristic-based, not benchmarked","No access to profiling data or execution traces that would inform better optimizations","May suggest premature optimizations that violate 'optimize later' best practices"],"requires":["VS Code 1.85.0 or higher","Ollama running locally with a code-capable model (CodeLlama 7B+)","Selected code block in editor"],"input_types":["source code in any language"],"output_types":["optimized source code","explanation of optimizations applied"],"categories":["code-generation-editing","local-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_10","uri":"capability://text.generation.language.intelligent.chat.interface.for.conversational.coding.assistance","name":"intelligent chat interface for conversational coding assistance","description":"Provides a dedicated chat panel in the VS Code sidebar for conversational interaction with the local Ollama model. Developers can ask questions about code, request explanations, discuss design decisions, or get coding advice in a multi-turn conversation. Chat context includes the current file and selected code, allowing the model to provide contextually relevant responses. All conversation stays local and private.","intents":["I want to ask the AI questions about my code without running a specific operation","I need to have a conversation about design decisions for this function","I want to get coding advice or best practices for a specific problem"],"best_for":["developers seeking interactive coding guidance and mentorship","teams using AI as a collaborative design partner","developers learning new languages or frameworks through conversation"],"limitations":["Chat quality depends on model capability — 7B models may provide less nuanced advice than GPT-4","No memory of previous conversations — each chat session starts fresh","Context limited to current file and selected code — cannot reference other files or project structure","No integration with code execution — cannot validate suggestions by running code"],"requires":["VS Code 1.85.0 or higher","Ollama with code model","Chat panel opened in sidebar"],"input_types":["natural language questions and prompts"],"output_types":["natural language responses from the model"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_11","uri":"capability://automation.workflow.automatic.ollama.server.lifecycle.management","name":"automatic ollama server lifecycle management","description":"Optionally automates starting and stopping the local Ollama server based on extension usage. When enabled via configuration (`autoStartOllama`), the extension detects if Ollama is not running and automatically starts it before executing operations. This eliminates the need for developers to manually start Ollama in a separate terminal. Server lifecycle is managed transparently in the background.","intents":["I want the extension to automatically start Ollama when I use it, without manual terminal commands","I don't want to worry about whether Ollama is running before using the extension","I want a seamless experience where the extension just works"],"best_for":["solo developers and small teams prioritizing convenience","developers new to Ollama who may forget to start the server","teams wanting to reduce setup friction for new developers"],"limitations":["Auto-start assumes Ollama is installed and in system PATH — fails silently if Ollama is not found","No visibility into server startup status or errors — developers may not know if auto-start failed","Cannot auto-stop server (would require detecting when extension is no longer needed, which is ambiguous)","May start multiple Ollama instances if multiple VS Code windows are open, causing port conflicts"],"requires":["VS Code 1.85.0 or higher","Ollama installed and accessible in system PATH","`autoStartOllama` configuration enabled"],"input_types":["configuration setting"],"output_types":["Ollama server process started (if not already running)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_12","uri":"capability://text.generation.language.multilingual.ui.and.localization.support","name":"multilingual ui and localization support","description":"Provides the extension interface in multiple languages (English, Russian, Ukrainian) through configuration. Developers can set the UI language via the `ollamaCodeFixer.language` setting, and all menus, buttons, and messages are displayed in the selected language. Localization is static (not dynamic language detection) and requires configuration change to switch languages.","intents":["I want to use the extension in my native language (Russian, Ukrainian, etc.)","I need the extension UI in a language my team understands"],"best_for":["non-English speaking developers and teams","international teams with diverse language preferences","organizations in non-English speaking regions"],"limitations":["Limited language support — only English, Russian, Ukrainian documented","Language switching requires configuration change and extension reload","No automatic language detection based on OS or VS Code settings","AI-generated code and explanations remain in the language the model produces (typically English)"],"requires":["VS Code 1.85.0 or higher","`ollamaCodeFixer.language` configuration set to supported language code"],"input_types":["language configuration setting"],"output_types":["UI text in selected language"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_2","uri":"capability://text.generation.language.code.explanation.and.documentation.generation","name":"code explanation and documentation generation","description":"Processes selected code through the local Ollama model to generate natural language explanations of what the code does, how it works, and why specific patterns are used. The extension sends code context to the model and receives human-readable explanations that help developers understand complex logic, unfamiliar patterns, or legacy code. A separate 'Add Comments' operation generates inline code comments at appropriate locations.","intents":["I need to understand what this legacy code does before refactoring it","I want to document this function's logic for my team without writing it manually","I need to explain this algorithm to a junior developer on my team"],"best_for":["teams onboarding new developers to existing codebases","developers maintaining legacy code with minimal documentation","technical writers creating API documentation from code"],"limitations":["Explanations may be verbose or miss domain-specific context that only humans understand","Cannot explain business logic or requirements — only describes technical implementation","Quality depends on model's training data; may misinterpret unusual or novel patterns","Generated comments may not match team coding standards or documentation style"],"requires":["VS Code 1.85.0 or higher","Ollama with code-capable model","Selected code block"],"input_types":["source code"],"output_types":["natural language explanation text","inline code comments"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_3","uri":"capability://code.generation.editing.automated.unit.test.generation.with.edge.case.coverage","name":"automated unit test generation with edge case coverage","description":"Analyzes selected code and generates unit tests using the local Ollama model, with documented support for edge case identification and coverage. The model receives the function/method as context and produces test cases covering normal inputs, boundary conditions, error states, and edge cases. Generated tests are formatted for the detected language (Jest for JavaScript, pytest for Python, etc.) and can be inserted above, below, or in a new file based on configuration.","intents":["I want to generate unit tests for this function without writing them manually","I need to ensure my code has edge case coverage before shipping","I want to create tests for legacy code that has no test coverage"],"best_for":["developers practicing test-driven development or adding tests to untested code","teams improving code coverage metrics quickly","developers unfamiliar with testing patterns for a specific language"],"limitations":["Generated tests may not cover all meaningful edge cases — model's coverage depends on training data","Tests are syntactically correct but may not reflect actual business requirements or acceptance criteria","Cannot generate integration tests or tests requiring external dependencies/mocks","No validation that generated tests actually pass against the code being tested"],"requires":["VS Code 1.85.0 or higher","Ollama with code model","Selected function or method"],"input_types":["source code (function/method)"],"output_types":["unit test code in language-appropriate format (Jest, pytest, JUnit, etc.)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_4","uri":"capability://code.generation.editing.code.refactoring.with.structural.improvements","name":"code refactoring with structural improvements","description":"Sends selected code to the Ollama model with a refactoring prompt requesting structural and architectural improvements. The model suggests changes to code organization, design patterns, separation of concerns, and maintainability without changing functionality. Refactoring suggestions are presented in preview mode before application, allowing developers to review and accept changes selectively.","intents":["I want to improve the architecture of this code without changing what it does","I need to apply design patterns to make this code more maintainable","I want to break this large function into smaller, more testable pieces"],"best_for":["developers conducting code reviews and seeking structural improvements","teams migrating code to follow new architectural standards","developers learning design patterns through AI-suggested refactorings"],"limitations":["Refactoring suggestions may not align with team-specific architectural patterns or conventions","Cannot validate that refactored code maintains identical behavior — requires manual testing","May suggest over-engineering for simple code or miss context about why current structure exists","No integration with version control to track refactoring changes"],"requires":["VS Code 1.85.0 or higher","Ollama with code model","Selected code block"],"input_types":["source code"],"output_types":["refactored source code","explanation of structural changes"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_5","uri":"capability://safety.moderation.security.vulnerability.detection.and.remediation","name":"security vulnerability detection and remediation","description":"Analyzes selected code using the local Ollama model to identify common security vulnerabilities (SQL injection, XSS, insecure cryptography, hardcoded secrets, etc.) and suggests fixes. The model receives code context and returns identified vulnerabilities with severity levels and corrected code examples. Results are presented in preview mode before application, allowing developers to understand and approve security fixes.","intents":["I want to check this code for common security vulnerabilities before committing","I need to fix SQL injection risks in my database queries","I want to ensure I'm not hardcoding secrets or credentials in my code"],"best_for":["developers conducting security code reviews","teams implementing security-first development practices","developers learning secure coding patterns"],"limitations":["Detection accuracy depends on model training — may miss sophisticated or novel vulnerability types","Cannot detect vulnerabilities requiring runtime analysis or external dependency scanning","No integration with SAST tools or security databases (CVE, CWE) for comprehensive vulnerability mapping","May produce false positives or suggest overly defensive fixes that impact performance","Cannot validate that suggested fixes actually eliminate the vulnerability"],"requires":["VS Code 1.85.0 or higher","Ollama with code model","Selected code block"],"input_types":["source code"],"output_types":["vulnerability descriptions with severity levels","remediated source code"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_6","uri":"capability://code.generation.editing.code.generation.from.natural.language.descriptions","name":"code generation from natural language descriptions","description":"Accepts natural language descriptions or requirements from the developer (via command palette or prompt) and generates complete, functional code using the local Ollama model. The model receives the description as context and produces code in the language of the current editor file. Generated code is inserted at the cursor position or in a new file based on configuration, with optional preview before application.","intents":["I want to generate a function that does X without writing it from scratch","I need to quickly scaffold boilerplate code for a common pattern","I want to generate code based on a description I write in natural language"],"best_for":["developers rapidly prototyping features","developers unfamiliar with a language or framework seeking code examples","teams accelerating development velocity through AI-assisted scaffolding"],"limitations":["Generated code may not follow team coding standards, naming conventions, or architectural patterns","Quality depends on description clarity — vague requirements produce lower-quality code","No validation that generated code is correct, efficient, or secure","May generate code that doesn't compile or has runtime errors requiring manual fixes","Cannot generate code requiring knowledge of private APIs, internal libraries, or project-specific patterns"],"requires":["VS Code 1.85.0 or higher","Ollama with code model","Natural language description provided by developer"],"input_types":["natural language description"],"output_types":["source code in the language of the current editor file"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_7","uri":"capability://code.generation.editing.programming.language.code.translation","name":"programming language code translation","description":"Converts code from one programming language to another using the local Ollama model. The developer selects code and specifies a target language (via command or configuration), and the model generates semantically equivalent code in the target language. Translation preserves logic and functionality while adapting to language-specific idioms, libraries, and best practices. Results are previewed before application.","intents":["I need to port this Python function to JavaScript for a web project","I want to convert this Java code to Go for better performance","I need to translate this legacy C code to modern Python"],"best_for":["teams migrating codebases between languages","developers learning new languages by translating familiar code","polyglot teams working across multiple language ecosystems"],"limitations":["Translation accuracy varies by language pair — some languages are more similar than others","May not preserve performance characteristics (e.g., Python to C++ translation may not be optimized)","Cannot translate language-specific features that don't exist in target language (e.g., Python decorators to Java)","No validation that translated code is functionally equivalent or compiles correctly","May miss library-specific idioms or best practices in the target language"],"requires":["VS Code 1.85.0 or higher","Ollama with code model trained on multiple languages","Selected code block","Target language specification"],"input_types":["source code in any language"],"output_types":["source code in target language"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_8","uri":"capability://tool.use.integration.dynamic.local.model.selection.and.management","name":"dynamic local model selection and management","description":"Provides a sidebar UI panel for selecting, installing, and switching between Ollama models without leaving VS Code. Developers can view available models, download new models via the Ollama CLI integration, and switch the active model for subsequent operations. The extension stores the selected model in configuration and applies it to all operations. Supports any model compatible with the Ollama API, not restricted to specific models.","intents":["I want to try different models (CodeLlama, Mistral, etc.) to see which works best for my code","I need to install a new model without leaving VS Code","I want to use a larger model for complex tasks and a smaller model for quick fixes"],"best_for":["developers experimenting with different model architectures and sizes","teams optimizing model selection for cost vs. quality tradeoffs","developers with limited disk space wanting to switch models based on task requirements"],"limitations":["Model installation requires Ollama to be running and accessible at the configured API endpoint","No built-in model benchmarking or comparison — developers must manually evaluate quality","Switching models mid-session may produce inconsistent results if models have different capabilities","No integration with Ollama's model management beyond basic selection — advanced features require CLI access"],"requires":["VS Code 1.85.0 or higher","Ollama installed and running locally","Network access to Ollama API endpoint (default http://localhost:11434)"],"input_types":["model selection via UI"],"output_types":["configuration update (stored in VS Code settings)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ruslan-cybersec-ollama-code-fixer__cap_9","uri":"capability://automation.workflow.configurable.code.insertion.and.preview.workflow","name":"configurable code insertion and preview workflow","description":"Implements a flexible code insertion system with multiple modes (replace, above, below, new file) and optional preview-before-apply workflow. Before applying any generated or modified code, developers can review changes in a preview panel and choose to accept, reject, or edit them. Optional automatic backup of original code is created before modifications. Configuration options control whether preview is mandatory or changes are auto-applied.","intents":["I want to review AI-generated code before it modifies my file","I need to insert generated code above my current function, not replace it","I want a backup of my original code in case the AI changes break something"],"best_for":["developers cautious about AI-generated code quality","teams with code review processes requiring human approval before changes","developers learning from AI suggestions and wanting to understand changes"],"limitations":["Preview workflow adds latency — developers must wait for preview to render before accepting changes","Backup files accumulate over time if not manually cleaned up","No built-in diff viewer — preview is text-based without syntax highlighting or side-by-side comparison","No integration with version control — backups are local files, not Git commits"],"requires":["VS Code 1.85.0 or higher","Configuration of insertion mode and preview preferences"],"input_types":["generated or modified code from AI operations"],"output_types":["code inserted into editor at specified location"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"moderate","permissions":["VS Code 1.85.0 or higher","Ollama installed and running locally (http://localhost:11434 by default)","CodeLlama or compatible model downloaded via Ollama (minimum 7B parameter model)","Selected code block in VS Code editor","Ollama running locally with a code-capable model (CodeLlama 7B+)","Selected code block in editor","Ollama with code model","Chat panel opened in sidebar","Ollama installed and accessible in system PATH","`autoStartOllama` configuration enabled"],"failure_modes":["Accuracy depends on local model quality (7B CodeLlama has lower accuracy than GPT-4 or Claude 3)","No access to project-wide context or build system diagnostics — only analyzes selected code in isolation","Inference latency varies with local hardware (typically 5-30 seconds for 7B model on CPU)","Cannot detect errors requiring runtime execution or external dependency analysis","Optimization suggestions may not account for language-specific runtime characteristics or compiler optimizations","Cannot measure actual performance impact — suggestions are heuristic-based, not benchmarked","No access to profiling data or execution traces that would inform better optimizations","May suggest premature optimizations that violate 'optimize later' best practices","Chat quality depends on model capability — 7B models may provide less nuanced advice than GPT-4","No memory of previous conversations — each chat session starts fresh","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.15,"quality":0.5,"ecosystem":0.45,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:31.090Z","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=ollama-code-fixer-ai-coding-assistant","compare_url":"https://unfragile.ai/compare?artifact=ollama-code-fixer-ai-coding-assistant"}},"signature":"Kri0VcUte2Xbh2M2KFsbVICtIQREh4AImfeRS4XSHO1g+EzaI4ryFTa6R3egcXmYbIjID1DbF9iw6wnXjn0OBQ==","signedAt":"2026-06-21T06:10:42.810Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ollama-code-fixer-ai-coding-assistant","artifact":"https://unfragile.ai/ollama-code-fixer-ai-coding-assistant","verify":"https://unfragile.ai/api/v1/verify?slug=ollama-code-fixer-ai-coding-assistant","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"}}