{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-metabob-metabob","slug":"metabob-debug-and-refactor-with-ai","name":"Metabob: Debug and Refactor with AI","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=Metabob.metabob","page_url":"https://unfragile.ai/metabob-debug-and-refactor-with-ai","categories":["code-editors"],"tags":["code","code generation","copilot","debugger","debugging","generative ai","generator","gpt","lint","linter","metabob","openai","python","refactor"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-metabob-metabob__cap_0","uri":"capability://code.generation.editing.graph.neural.network.based.problem.detection.in.python.code","name":"graph neural network-based problem detection in python code","description":"Detects logical bugs, vulnerabilities, and code quality issues using a proprietary Graph Neural Network (GNN) model that analyzes code structure as a computational graph rather than text. The GNN operates on Abstract Syntax Trees (ASTs) to identify structural patterns associated with problems, enabling detection of issues that regex or token-based approaches miss. Analysis is triggered automatically on file save and results are cached until the next modification.","intents":["I want to catch logical bugs and security vulnerabilities before they reach production without running a full linter suite","I need to identify code quality issues that static analysis tools typically miss, like subtle logic errors or anti-patterns","I want automated problem detection to run in the background as I code without slowing down my editor"],"best_for":["Python developers building production systems who want ML-powered bug detection beyond traditional linting","Security-conscious teams needing vulnerability detection integrated into the development workflow","Individual developers and small teams without dedicated code review resources"],"limitations":["GNN model execution latency unknown — could cause editor lag on large files or slow machines","Single-file analysis only — cannot detect cross-file logical errors or dependency-related issues","False positive/negative rates not disclosed — users must manually validate all detections","Limited to Python, JavaScript, TypeScript, C, C++, Java — no support for Go, Rust, PHP, or other languages","Proprietary model means no transparency into what patterns it detects or why"],"requires":["VS Code extension host (minimum version unknown)","Python code file open in editor","Internet connection for proprietary GNN model inference (local vs remote execution unknown)"],"input_types":["Python source code (single file)","JavaScript/TypeScript source code","C/C++/Java source code"],"output_types":["Diagnostic markers in VS Code editor","Problem descriptions in natural language","Severity levels (unknown if categorized as error/warning/info)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_1","uri":"capability://text.generation.language.llm.powered.natural.language.explanation.generation.for.detected.problems","name":"llm-powered natural language explanation generation for detected problems","description":"Generates human-readable explanations for detected code problems using a configurable Large Language Model backend (default unknown, OpenAI ChatGPT optional). The extension sends detected problem context and code snippets to the LLM, which generates explanations of why the problem matters and how it could impact the code. Backend selection is configurable via VS Code settings, allowing users to choose between Metabob's default model or OpenAI's ChatGPT with API key authentication.","intents":["I want to understand WHY a detected problem is actually a problem, not just see a diagnostic marker","I need explanations in plain English so I can educate myself about code quality issues","I want to use my own OpenAI API key and ChatGPT models instead of relying on Metabob's default backend"],"best_for":["Junior developers learning code quality best practices through AI-generated explanations","Teams that want to customize the explanation model to match their coding standards","Organizations with existing OpenAI contracts who want to consolidate LLM usage"],"limitations":["Explanation quality depends entirely on the selected LLM backend — no quality guarantees","OpenAI integration sends code context to external servers, creating data privacy concerns","No control over data retention or usage by third-party LLM providers (OpenAI policy unknown)","LLM backend auto-updates unknown — model versions may change without notice","Explanations may be verbose, inaccurate, or misleading for edge-case problems"],"requires":["VS Code extension host","Internet connection for LLM API calls","OpenAI API key if using ChatGPT backend (free tier uses Metabob's default model)","At least one detected problem from the GNN model"],"input_types":["Detected problem metadata (type, location, severity)","Code snippet context around the problem","File language/syntax information"],"output_types":["Natural language explanation text","Markdown-formatted explanation (assumed)","Displayed in VS Code problem details panel"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_2","uri":"capability://code.generation.editing.ai.generated.code.fix.recommendations.with.inline.preview","name":"ai-generated code fix recommendations with inline preview","description":"Generates suggested code fixes for detected problems using the configured LLM backend, presenting recommendations inline in the VS Code editor. The LLM receives the problem description, code context, and file language, then generates a corrected code snippet that addresses the issue. Users can preview, accept, or reject recommendations, with acceptance triggering code replacement in the editor.","intents":["I want AI to suggest how to fix detected problems so I don't have to manually refactor","I need to see what the fixed code would look like before applying the change","I want to quickly apply fixes with one click instead of manually editing code"],"best_for":["Developers seeking rapid code fixes during active development","Teams wanting to standardize code fixes across the codebase through AI suggestions","Developers learning best practices by examining AI-generated fixes"],"limitations":["Generated fixes may be incorrect, incomplete, or introduce new bugs — requires manual review","LLM may hallucinate code that doesn't match the project's style or patterns","No context about project dependencies, imports, or type definitions — fixes may reference undefined symbols","Single-file scope means fixes cannot refactor across multiple files even if the problem spans files","No undo/rollback mechanism if an applied fix breaks the code","Fix generation latency unknown — could cause editor responsiveness issues"],"requires":["VS Code extension host","At least one detected problem from GNN model","Configured LLM backend (Metabob default or OpenAI with API key)","Internet connection for LLM API calls","Write permissions on the current file"],"input_types":["Detected problem metadata and location","Code snippet context (typically 5-20 lines around the problem)","File language and syntax information","Problem explanation from LLM"],"output_types":["Suggested code replacement as text","Inline diff preview in editor (assumed)","Accept/reject UI controls"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_3","uri":"capability://automation.workflow.automatic.code.analysis.on.file.save.with.configurable.triggers","name":"automatic code analysis on file save with configurable triggers","description":"Automatically runs the GNN problem detection model whenever a Python/JavaScript/TypeScript/C/C++/Java file is saved in VS Code, with analysis enabled by default via the 'Analyze Document On Save' setting. The extension hooks into VS Code's file save event, queues the current file for analysis, and displays results as diagnostic markers in the editor. Analysis can be toggled on/off per workspace via VS Code settings.","intents":["I want continuous background analysis of my code without manually triggering scans","I need to disable automatic analysis on large files or slow machines to avoid editor lag","I want analysis to run only on specific file types or projects"],"best_for":["Developers in fast-paced environments who want real-time feedback without context switching","Teams with CI/CD pipelines who want pre-commit analysis in the editor","Individual developers on resource-constrained machines who need to disable automatic analysis"],"limitations":["No granular trigger control — cannot configure analysis for specific file patterns or directories","Analysis latency on large files unknown — could cause noticeable editor lag or freezing","No batching or debouncing — rapid file saves may queue multiple analyses, causing backlog","Cannot disable analysis for specific files or folders — all-or-nothing toggle only","No analysis scheduling — cannot defer analysis to off-peak times or background threads","Results are not cached between saves — each save triggers a full re-analysis"],"requires":["VS Code extension host","File save event in a supported language (Python, JavaScript, TypeScript, C, C++, Java)","VS Code settings accessible (admin/user level)","Internet connection if using remote GNN model inference"],"input_types":["File save event from VS Code","Current file content in memory","File language/syntax information"],"output_types":["Diagnostic markers in VS Code Problems panel","Inline squiggly underlines in editor","Problem count badge (assumed)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_4","uri":"capability://planning.reasoning.user.feedback.loop.for.model.optimization.via.problem.endorsement","name":"user feedback loop for model optimization via problem endorsement","description":"Allows developers to endorse or discard detected problems, sending feedback signals back to Metabob's GNN model to improve detection accuracy over time. When a user marks a detection as 'correct' or 'incorrect', the extension logs this feedback (along with the problem context and code) and uses it to retrain or fine-tune the proprietary GNN model. This creates a continuous learning loop where the model improves as more developers use the extension.","intents":["I want to help improve Metabob's detection accuracy by marking false positives and false negatives","I want the model to learn my codebase's patterns and reduce false positives over time","I want to contribute to a community-driven improvement of the detection model"],"best_for":["Teams using Metabob extensively who want to improve model accuracy for their specific domain","Organizations willing to share code patterns (via feedback) to improve the product","Developers who trust Metabob's data handling and want to contribute to model improvement"],"limitations":["Feedback data is sent to Metabob servers — code context is shared with the company","No transparency into how feedback is used — unclear if data is used for retraining, fine-tuning, or just metrics","No opt-out mechanism for feedback collection — cannot disable data sharing while using the extension","Feedback impact unknown — unclear how long it takes for model improvements to propagate to users","No per-user or per-team model customization — feedback from all users is pooled into a single global model","Data retention policy unclear — Metabob states proprietary model data is deleted after 1 hour, but feedback data retention unknown"],"requires":["VS Code extension host","At least one detected problem displayed in the editor","Internet connection to send feedback to Metabob servers","User account or anonymous usage tracking (mechanism unknown)"],"input_types":["Problem detection result (ID, type, location, severity)","Code context around the problem","User feedback signal (endorse/discard/ignore)","File language and metadata"],"output_types":["Feedback acknowledgment in UI (assumed)","No direct output — feedback is asynchronous and aggregated"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_5","uri":"capability://code.generation.editing.multi.language.code.analysis.with.language.specific.problem.detection","name":"multi-language code analysis with language-specific problem detection","description":"Detects problems across six programming languages (Python, JavaScript, TypeScript, C, C++, Java) using a single GNN model trained on multi-language code patterns. The extension automatically detects the file language via VS Code's language mode, routes the code to the appropriate analysis pipeline, and returns language-specific problem categories (e.g., null pointer dereferences in C/C++, type errors in TypeScript). Problem types and severity levels are tailored to each language's common pitfalls.","intents":["I work in a polyglot codebase and want consistent problem detection across multiple languages","I need language-specific problem detection that understands C++ memory safety issues differently than Python type issues","I want a single tool instead of managing separate linters for each language in my project"],"best_for":["Full-stack teams using JavaScript/TypeScript frontend and Python/Node.js backend","Systems engineers working with C/C++ who also maintain Python tooling","Polyglot organizations seeking unified code quality tooling across languages"],"limitations":["Only 6 languages supported — no Go, Rust, PHP, Ruby, C#, or other languages","Problem detection quality may vary across languages — GNN may be better trained on some languages than others","No language-specific configuration — cannot tune detection sensitivity per language","Cross-language issues not detected — cannot identify problems in language interop or FFI code","Language detection relies on VS Code's language mode — may fail for ambiguous or custom file types","No dialect or version-specific detection — treats all Python 3.x versions the same, all C++ standards the same"],"requires":["VS Code extension host with language mode detection","File in one of six supported languages (Python, JavaScript, TypeScript, C, C++, Java)","Proper file extension or VS Code language mode assignment"],"input_types":["Source code in Python, JavaScript, TypeScript, C, C++, or Java","File language mode from VS Code","AST representation of code (internal)"],"output_types":["Language-specific problem categories","Diagnostic markers with language-appropriate severity levels","Explanations tailored to language-specific concerns"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_6","uri":"capability://tool.use.integration.configurable.llm.backend.selection.with.openai.chatgpt.integration","name":"configurable llm backend selection with openai chatgpt integration","description":"Allows users to select which Large Language Model powers explanation and fix generation through VS Code settings, with built-in support for OpenAI's ChatGPT models via API key authentication. The extension provides a dropdown menu in settings to choose between Metabob's default LLM backend and OpenAI ChatGPT, with a separate text field for entering OpenAI API keys. The selected backend is used for all explanation and fix generation requests, enabling users to leverage their own OpenAI accounts or API budgets.","intents":["I want to use OpenAI's ChatGPT instead of Metabob's default model for better explanation quality","I have an existing OpenAI API key and want to consolidate my LLM usage","I want to switch between different LLM backends depending on my needs or budget"],"best_for":["Teams with existing OpenAI contracts who want to use their API keys in the editor","Developers who prefer ChatGPT's explanation style over Metabob's default model","Organizations evaluating different LLM backends for code analysis"],"limitations":["Only OpenAI ChatGPT supported as alternative — no Anthropic Claude, Hugging Face, local models, or other providers","No support for custom/self-hosted LLM endpoints — cannot use private or on-premise models","OpenAI API key stored in VS Code settings — security implications if settings are synced or shared","No rate limiting or quota management — users responsible for monitoring OpenAI API costs","Backend switching requires manual configuration — no automatic fallback if primary backend fails","No version pinning — OpenAI models may auto-update, changing explanation quality without notice"],"requires":["VS Code extension host with settings UI","OpenAI API key (if using ChatGPT backend) — available from https://platform.openai.com/api-keys","Internet connection for LLM API calls","Active OpenAI account with API credits"],"input_types":["Backend selection from dropdown (Metabob default or OpenAI ChatGPT)","OpenAI API key as text input","Problem context and code for explanation/fix generation"],"output_types":["Configuration stored in VS Code settings.json","Selected backend used for all subsequent LLM requests","No direct output — configuration is applied to future analyses"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_7","uri":"capability://safety.moderation.proprietary.data.handling.with.1.hour.retention.for.gnn.model.inference","name":"proprietary data handling with 1-hour retention for gnn model inference","description":"Implements a data privacy model where code sent to Metabob's proprietary GNN model for problem detection is automatically deleted after 1 hour, preventing long-term data retention. The extension sends code snippets to Metabob's servers for GNN inference, but the company commits to deleting this data within 1 hour of the last API call. This differs from third-party LLM backends (OpenAI), where data retention is governed by the provider's separate privacy policy.","intents":["I want to use AI-powered code analysis but need assurance that my code isn't stored long-term","I need to comply with data residency or privacy regulations that restrict code storage","I want to understand the data handling differences between Metabob's detection and third-party LLM backends"],"best_for":["Organizations with strict data privacy requirements (GDPR, HIPAA, SOC 2)","Teams handling sensitive or proprietary code who want limited data exposure","Developers concerned about long-term code retention by AI vendors"],"limitations":["1-hour retention applies only to Metabob's GNN model — OpenAI backend has separate (unknown) retention policy","No guarantee of actual deletion — 'deleted' data may remain in backups or logs","No audit trail or verification mechanism — users cannot confirm data was actually deleted","Feedback data retention unknown — endorsements/discards may be stored indefinitely for model training","No opt-out for data collection — cannot use the extension without sending code to Metabob servers","Third-party LLM backends (OpenAI) may retain data longer — users responsible for reviewing their privacy policies"],"requires":["Internet connection to send code to Metabob servers","Trust in Metabob's stated data handling practices","Acceptance of Metabob's privacy policy and terms of service"],"input_types":["Code snippets from current file","File metadata (language, size, location)","Problem detection requests"],"output_types":["No direct output — data handling is transparent to user","Diagnostic results returned to editor"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_8","uri":"capability://code.generation.editing.vs.code.diagnostic.panel.integration.with.problem.severity.levels","name":"vs code diagnostic panel integration with problem severity levels","description":"Integrates detected problems into VS Code's native Problems panel and inline editor diagnostics, displaying issues with severity levels (error/warning/info assumed) and clickable navigation. The extension registers diagnostics with VS Code's diagnostic API, which automatically populates the Problems panel, adds squiggly underlines in the editor, and enables keyboard navigation between issues. Problem details include the issue description, suggested fix, and explanation from the configured LLM backend.","intents":["I want detected problems to appear in VS Code's standard Problems panel alongside ESLint and other linters","I need to navigate between problems using keyboard shortcuts and see them highlighted in the editor","I want problem severity levels (error/warning) to integrate with my existing VS Code workflow"],"best_for":["Developers already using VS Code's Problems panel for ESLint, Pylint, and other linters","Teams with established VS Code workflows who want minimal disruption","Developers who rely on keyboard navigation and diagnostic keybindings"],"limitations":["Severity levels not documented — unclear if Metabob uses error/warning/info or custom levels","No filtering or grouping by problem type — all problems mixed in Problems panel","No integration with VS Code's problem matcher for CI/CD pipelines","Diagnostic markers may conflict with other linters if they report the same issues","No custom diagnostic icons or colors — uses VS Code's default styling","Problem count badge accuracy unknown — may not update in real-time"],"requires":["VS Code 1.x (minimum version unknown)","VS Code diagnostic API support (standard in all modern versions)","At least one detected problem from GNN model"],"input_types":["Detected problems from GNN model","Problem metadata (location, type, severity)","Explanation and fix suggestions from LLM"],"output_types":["VS Code Diagnostic objects","Problems panel entries","Inline editor squiggly underlines","Diagnostic detail popover on hover"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-metabob-metabob__cap_9","uri":"capability://automation.workflow.freemium.pricing.model.with.free.tier.and.premium.features","name":"freemium pricing model with free tier and premium features","description":"Offers a freemium pricing structure where basic problem detection and explanations are available for free, with premium features (likely advanced fix generation, priority support, or higher API quotas) available through paid subscription. The free tier includes GNN-based problem detection and LLM-powered explanations using Metabob's default backend, while premium tiers likely unlock OpenAI ChatGPT integration, higher analysis quotas, or team features. Pricing details are not publicly documented in the marketplace listing.","intents":["I want to try Metabob's problem detection for free before committing to a paid plan","I need advanced features like ChatGPT integration or higher analysis quotas and am willing to pay","I want to understand the cost difference between free and premium tiers"],"best_for":["Individual developers and small teams evaluating Metabob before purchasing","Solo developers who want free problem detection without premium features","Organizations with budget for premium features and higher quotas"],"limitations":["Pricing details not documented in marketplace — unclear what features are free vs paid","Free tier quotas unknown — may have rate limits or analysis count restrictions","Premium features unknown — unclear what additional capabilities justify paid subscription","No transparent pricing page in marketplace listing — users must visit external website","Upgrade path unclear — no in-app prompts or upgrade UI documented","Free tier may require account creation or telemetry — data collection terms unknown"],"requires":["VS Code extension host","Metabob account (free or paid) — account creation mechanism unknown","Internet connection for cloud-based analysis"],"input_types":["User account tier (free or premium)","API quota tracking"],"output_types":["Feature availability based on tier","Rate limiting or quota enforcement","Upgrade prompts (assumed for free tier)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["VS Code extension host (minimum version unknown)","Python code file open in editor","Internet connection for proprietary GNN model inference (local vs remote execution unknown)","VS Code extension host","Internet connection for LLM API calls","OpenAI API key if using ChatGPT backend (free tier uses Metabob's default model)","At least one detected problem from the GNN model","At least one detected problem from GNN model","Configured LLM backend (Metabob default or OpenAI with API key)","Write permissions on the current file"],"failure_modes":["GNN model execution latency unknown — could cause editor lag on large files or slow machines","Single-file analysis only — cannot detect cross-file logical errors or dependency-related issues","False positive/negative rates not disclosed — users must manually validate all detections","Limited to Python, JavaScript, TypeScript, C, C++, Java — no support for Go, Rust, PHP, or other languages","Proprietary model means no transparency into what patterns it detects or why","Explanation quality depends entirely on the selected LLM backend — no quality guarantees","OpenAI integration sends code context to external servers, creating data privacy concerns","No control over data retention or usage by third-party LLM providers (OpenAI policy unknown)","LLM backend auto-updates unknown — model versions may change without notice","Explanations may be verbose, inaccurate, or misleading for edge-case problems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6,"quality":0.3,"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":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:39.869Z","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=metabob-debug-and-refactor-with-ai","compare_url":"https://unfragile.ai/compare?artifact=metabob-debug-and-refactor-with-ai"}},"signature":"PKBzBcKJ1jetcgNBYhoRTjKeHq74K08U4WV9w5wkrMlvQzhAGLCXzdxuMK8htooqcg1lxK6FxCVv3DeOQG3QBg==","signedAt":"2026-06-22T14:40:07.662Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/metabob-debug-and-refactor-with-ai","artifact":"https://unfragile.ai/metabob-debug-and-refactor-with-ai","verify":"https://unfragile.ai/api/v1/verify?slug=metabob-debug-and-refactor-with-ai","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"}}