{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-dataquanta-vscode-kaggle-run","slug":"kaggle-studio","name":"Kaggle Studio","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=DataQuanta.vscode-kaggle-run","page_url":"https://unfragile.ai/kaggle-studio","categories":["code-editors"],"tags":["data science","datasets","gpu","jupyter","kaggle","machine learning","notebook"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-dataquanta-vscode-kaggle-run__cap_0","uri":"capability://automation.workflow.local.to.remote.notebook.execution.with.compute.resource.toggling","name":"local-to-remote notebook execution with compute resource toggling","description":"Enables developers to edit Jupyter notebooks locally in VS Code while submitting them to Kaggle's cloud infrastructure for execution, with dynamic GPU/TPU/CPU selection via kaggle.yml configuration. The extension reads .ipynb files from the local filesystem, serializes them via the Kaggle API client, and pushes them to Kaggle's kernel execution service, which handles environment setup, dependency resolution, and compute allocation. Results are automatically downloaded to a .kaggle-outputs/ directory for local inspection.","intents":["I want to develop notebooks locally with my preferred editor but run them on Kaggle's free GPU/TPU without manual web uploads","I need to toggle between CPU, GPU, and TPU execution for the same notebook without re-uploading or changing code","I want to iterate quickly on data science code by pushing and running from my editor in one command"],"best_for":["data scientists and ML engineers participating in Kaggle competitions","researchers prototyping on free GPU/TPU without local hardware","teams collaborating on Kaggle projects who prefer local development workflows"],"limitations":["Requires active internet connectivity to Kaggle API; all execution is remote and blocking until completion","No local execution fallback if Kaggle API is unavailable or rate-limited","Output download behavior and file naming conflict resolution are undocumented; unclear if outputs auto-overwrite or append","Concurrent execution limits unknown; unclear if multiple simultaneous notebook runs are supported","Execution timeout and output size limits are not documented and depend on Kaggle backend constraints"],"requires":["Valid Kaggle account with active API token","VS Code (minimum version unknown, likely 1.60+)",".ipynb notebook file in local workspace","kaggle.yml configuration file with kernel_slug and accelerator settings","Network connectivity to api.kaggle.com"],"input_types":[".ipynb (Jupyter notebook format)","kaggle.yml (YAML configuration)","Python code cells within notebook"],"output_types":[".ipynb (executed notebook with outputs)","CSV, JSON, images, and other artifacts saved to .kaggle-outputs/","Execution logs and error messages in VS Code output panel"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_1","uri":"capability://tool.use.integration.kaggle.api.authentication.with.dual.credential.storage","name":"kaggle api authentication with dual credential storage","description":"Manages Kaggle API token authentication through two configurable methods: file-based credentials stored at ~/.kaggle/kaggle.json (recommended for persistent, shared environments) or in-memory credentials via VS Code's built-in credential storage (for ephemeral or single-user setups). The extension validates tokens by calling Kaggle's API status endpoint and provides Sign In/Sign Out commands to manage credentials without manual file editing. Expired tokens trigger 401 Unauthorized errors, requiring manual regeneration from kaggle.com/settings/account.","intents":["I want to authenticate with Kaggle once and have the extension automatically use my credentials for all API calls","I need to securely store my API token without hardcoding it in configuration files or environment variables","I want to switch between multiple Kaggle accounts or revoke access without reinstalling the extension"],"best_for":["individual data scientists setting up local development environments","teams using shared machines where multiple users need separate Kaggle accounts","CI/CD pipelines that need to authenticate with Kaggle without interactive prompts"],"limitations":["Token expiration is not automatically detected or refreshed; users must manually regenerate tokens at kaggle.com and update credentials","No token rotation or refresh mechanism; long-lived tokens pose security risk if compromised","File-based credentials at ~/.kaggle/kaggle.json are readable by any process on the machine; no encryption at rest","VS Code credential storage backend is OS-dependent (Keychain on macOS, Credential Manager on Windows, pass/secretservice on Linux); behavior varies across platforms","No multi-account support within a single VS Code window; switching accounts requires Sign Out and Sign In"],"requires":["Active Kaggle account (free or paid)","API token generated from kaggle.com/settings/account → API section","Either ~/.kaggle/kaggle.json file with username and api_key fields, OR VS Code credential storage access","Network connectivity to api.kaggle.com for token validation"],"input_types":["Kaggle username (string)","Kaggle API key (string, 40+ characters)","kaggle.json file (JSON format with username and api_key keys)"],"output_types":["Authentication status (boolean: authenticated or not)","API status response (JSON from Kaggle API)","Error messages (401 Unauthorized, invalid token format, etc.)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_2","uri":"capability://automation.workflow.project.initialization.and.metadata.management","name":"project initialization and metadata management","description":"Scaffolds new Kaggle projects by generating kaggle.yml and kernel-metadata.json configuration files that define project identity, compute requirements, dataset dependencies, and internet access policies. The 'Init Project' command creates these files in the workspace root with sensible defaults; the 'Link Notebook' command associates an existing Kaggle notebook with the local project by populating kernel_slug. The extension reads and validates these YAML/JSON files on startup to configure subsequent operations (execution, dataset attachment, submission).","intents":["I want to initialize a new Kaggle project locally with configuration that mirrors my Kaggle notebook settings","I need to link an existing Kaggle notebook to my local workspace so I can push changes back to it","I want to declare dataset dependencies and compute requirements in a version-controlled configuration file"],"best_for":["data scientists starting new Kaggle competition projects","teams using git to version-control Kaggle project configurations","developers migrating existing Kaggle notebooks to local-first workflows"],"limitations":["kernel-metadata.json format is undocumented; unclear what fields it contains or how it differs from kaggle.yml","No schema validation for kaggle.yml; invalid YAML or missing required fields may cause silent failures during execution","No migration tool to convert existing Kaggle notebook settings to kaggle.yml; manual transcription required","Project name conflicts with existing Kaggle notebooks are not detected; users may accidentally overwrite remote notebooks","No support for project templates or scaffolding beyond default file generation"],"requires":["VS Code workspace with write access to root directory","Valid Kaggle account (for 'Link Notebook' command)","Existing Kaggle notebook URL or kernel_slug (for 'Link Notebook' command)"],"input_types":["Project name (string)","Kaggle notebook kernel_slug (string, format: username/project-name)","Accelerator choice (enum: gpu, tpu, none)","Internet access flag (boolean)"],"output_types":["kaggle.yml (YAML file with project configuration)","kernel-metadata.json (JSON file with metadata, format unknown)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_3","uri":"capability://search.retrieval.dataset.discovery.attachment.and.download","name":"dataset discovery, attachment, and download","description":"Provides a searchable sidebar tree view of Kaggle datasets filtered by name, owner, and competition context. Users can browse dataset metadata (size, file count, description) without downloading, attach datasets to projects by adding them to the kaggle.yml datasets array, and download entire datasets to the local workspace via the 'Download Dataset' command. The extension uses Kaggle's dataset API to list available datasets and the dataset download API to fetch files, with progress indication in the VS Code status bar.","intents":["I want to discover and attach datasets to my project without leaving VS Code or visiting the Kaggle website","I need to download a dataset locally to inspect its structure before writing code that uses it","I want to declare dataset dependencies in kaggle.yml so they are automatically available when my notebook runs on Kaggle"],"best_for":["data scientists exploring Kaggle datasets for competition participation","teams managing multiple dataset dependencies across projects","developers building reproducible data pipelines with declared dataset versions"],"limitations":["Dataset search is limited to name/owner/competition filters; no advanced filtering by size, license, or data type","Download behavior for large datasets (>10GB) is undocumented; unclear if resumable downloads or streaming are supported","No dataset versioning support; attaching a dataset does not pin a specific version, so dataset updates may break downstream code","File naming conflicts during download are not documented; unclear if files are overwritten, renamed, or skipped","No preview or sampling capability; users must download entire datasets to inspect structure","Private datasets are not accessible unless the user has explicit access; no clear error messaging for permission denied"],"requires":["Valid Kaggle API token (for dataset listing and download)","Network connectivity to api.kaggle.com and kaggle-datasets.s3.amazonaws.com","Local disk space sufficient for downloaded datasets","Read/write access to workspace directory"],"input_types":["Dataset search query (string: name, owner, or competition)","Dataset identifier (string, format: username/dataset-name)","Download destination path (string, defaults to workspace root)"],"output_types":["Dataset metadata (JSON: name, size, file count, description, owner)","Dataset files (CSV, JSON, images, etc., downloaded to local directory)","kaggle.yml updated with dataset entry in datasets array"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_4","uri":"capability://automation.workflow.competition.integration.with.submission.and.tracking","name":"competition integration with submission and tracking","description":"Displays a sidebar tree view of Kaggle competitions filtered by status (entered, featured, all) and searchable by name. Users can submit predictions to competitions directly from VS Code via the 'Submit to Competition' command, which uploads a CSV file and returns a submission ID and leaderboard score. The extension tracks submission history in a 'Runs' tree view, showing execution timestamps, compute resources used, and output file locations.","intents":["I want to submit competition predictions without leaving VS Code or manually uploading to the Kaggle website","I need to track my submission history and see which runs produced the best leaderboard scores","I want to quickly iterate on competition solutions by pushing code, running it, and submitting results in one workflow"],"best_for":["competitive data scientists participating in active Kaggle competitions","teams collaborating on competition solutions with shared submission tracking","developers building automated competition pipelines with programmatic submission"],"limitations":["Submission file format is limited to CSV; no support for other formats (JSON, Parquet, etc.) even if competition accepts them","Leaderboard score is returned immediately but may not reflect final evaluation if competition uses delayed scoring","No submission message or description support; users cannot annotate submissions with experiment notes","Submission history is not persisted locally; closing VS Code loses the 'Runs' tree view state","No rollback or revert mechanism; users cannot unsubmit or delete submissions from the extension","Competition rules and submission limits are not enforced by the extension; users may exceed daily submission quotas without warning"],"requires":["Valid Kaggle API token with competition access","Entered or featured competition (visible in 'Competitions' tree view)","CSV file with predictions matching competition submission format","Network connectivity to api.kaggle.com"],"input_types":["Competition identifier (string, format: competition-name)","Prediction CSV file (path to local file)","Submission message (optional, string)"],"output_types":["Submission ID (integer)","Leaderboard score (float, if available immediately)","Submission status (pending, scored, etc.)","Submission timestamp (ISO 8601 datetime)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_5","uri":"capability://automation.workflow.execution.history.and.output.tracking","name":"execution history and output tracking","description":"Maintains a 'Runs' tree view that displays all notebook executions triggered from VS Code, including execution timestamp, compute resource used (GPU/TPU/CPU), execution status (running, completed, failed), and output file location in .kaggle-outputs/. Users can click on a run to view its outputs or logs. The extension queries Kaggle's kernel execution API to populate this view and polls for status updates until execution completes.","intents":["I want to see a history of all notebook runs I've triggered from VS Code with their execution times and resource usage","I need to quickly access outputs from a previous run without manually navigating the file system","I want to compare outputs across multiple runs to debug performance or accuracy issues"],"best_for":["data scientists iterating on notebooks and comparing results across runs","teams auditing which code versions produced which outputs for reproducibility","developers debugging notebook failures by reviewing execution logs"],"limitations":["Runs history is not persisted across VS Code sessions; closing the extension loses the tree view state","No filtering or sorting options for runs (by date, status, resource, etc.); only chronological display","Execution logs are not displayed in the tree view; users must navigate to .kaggle-outputs/ to find log files","No comparison view to side-by-side inspect outputs from different runs","Polling for execution status adds latency; users may not see completion status immediately","No integration with git or version control; runs are not linked to code commits or branches"],"requires":["Valid Kaggle API token","At least one notebook execution triggered from VS Code","Network connectivity to api.kaggle.com for status polling"],"input_types":["Execution ID (integer, returned by Kaggle API)","Polling interval (integer, milliseconds, default unknown)"],"output_types":["Run metadata (JSON: timestamp, status, compute resource, kernel_slug)","Output file paths (strings, relative to .kaggle-outputs/)","Execution logs (text, if available)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_6","uri":"capability://automation.workflow.inline.notebook.execution.trigger.with.rocket.button","name":"inline notebook execution trigger with rocket button","description":"Adds a rocket button (🚀) to the VS Code notebook editor toolbar that triggers immediate execution of the current notebook on Kaggle infrastructure. Clicking the button is equivalent to running the 'Kaggle: Run Current Notebook' command, which reads the .ipynb file, validates the kaggle.yml configuration, and submits the notebook to Kaggle's kernel execution API. The extension displays execution progress in the status bar and automatically downloads outputs to .kaggle-outputs/ when complete.","intents":["I want a one-click way to run my notebook on Kaggle without typing a command or opening the command palette","I need visual feedback that my notebook is executing and when it completes","I want to quickly iterate by editing code, clicking the rocket button, and reviewing outputs"],"best_for":["data scientists who prefer visual UI over command-line interfaces","developers iterating rapidly on notebooks and wanting minimal friction between edits and execution","teams using VS Code as the primary development environment for Kaggle projects"],"limitations":["Rocket button is only visible when a .ipynb file is open in the editor; no quick access from other views","No confirmation dialog before execution; accidental clicks may trigger unintended runs","Execution is blocking; the button is disabled until the run completes, preventing concurrent submissions","No option to customize button appearance or keybinding; rocket emoji is fixed","Progress indication is limited to status bar text; no detailed progress bar or log streaming"],"requires":["VS Code with an open .ipynb notebook file","Valid kaggle.yml configuration in the workspace root","Valid Kaggle API token","Network connectivity to api.kaggle.com"],"input_types":[".ipynb file (current notebook in editor)"],"output_types":["Execution status updates (text in status bar)","Output files (downloaded to .kaggle-outputs/)","Execution ID (for tracking in 'Runs' tree view)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_7","uri":"capability://automation.workflow.push.and.run.workflow.with.automatic.output.download","name":"push-and-run workflow with automatic output download","description":"The 'Push & Run' command combines notebook upload and execution into a single operation: it reads the local .ipynb file, pushes it to Kaggle via the API, triggers execution with the compute resources specified in kaggle.yml, monitors execution status via polling, and automatically downloads all output files (including the executed notebook with cell outputs) to the .kaggle-outputs/ directory when complete. This eliminates the need for separate push and run commands.","intents":["I want to push my local notebook to Kaggle and run it in one command without waiting for separate operations","I need the executed notebook with all cell outputs downloaded automatically so I can review results locally","I want to minimize the number of manual steps between editing code and seeing results"],"best_for":["data scientists iterating rapidly on notebooks and wanting minimal friction","teams using local-first development workflows and pushing to Kaggle only for execution","developers building automated pipelines that need to push, run, and retrieve outputs programmatically"],"limitations":["Push-and-run is a blocking operation; the command does not return until execution completes, which may take minutes for long-running notebooks","No timeout configuration; users cannot set a maximum execution time before canceling","Output download is automatic but file naming and conflict resolution are undocumented; unclear if files are overwritten or renamed","No option to push without running; users must run the notebook if they want to push changes","Execution logs are not streamed to VS Code; users must check .kaggle-outputs/ for log files after execution completes","No rollback mechanism; if execution fails, the notebook is already pushed to Kaggle and may overwrite previous versions"],"requires":["Valid Kaggle API token","Valid kaggle.yml configuration with kernel_slug and accelerator settings",".ipynb notebook file in the workspace","Network connectivity to api.kaggle.com","Sufficient disk space in .kaggle-outputs/ for downloaded outputs"],"input_types":[".ipynb file (current notebook)","kaggle.yml configuration (for compute resource and dataset settings)"],"output_types":["Executed .ipynb file with cell outputs (downloaded to .kaggle-outputs/)","Generated artifacts (CSV, images, etc., downloaded to .kaggle-outputs/)","Execution logs (if available, downloaded to .kaggle-outputs/)","Execution ID and status (displayed in status bar and 'Runs' tree view)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_8","uri":"capability://data.processing.analysis.output.file.download.and.local.storage.management","name":"output file download and local storage management","description":"The 'Download Outputs' command retrieves all output files generated by a notebook execution from Kaggle's storage and saves them to the .kaggle-outputs/ directory in the local workspace. The extension automatically creates this directory if it does not exist and organizes files by execution ID or timestamp (exact organization is undocumented). Users can also manually download outputs from the 'Runs' tree view by clicking on a specific run.","intents":["I want to download outputs from a Kaggle notebook execution without visiting the web interface","I need to organize outputs locally by execution for comparison and analysis","I want to integrate Kaggle outputs into my local development workflow (git, version control, etc.)"],"best_for":["data scientists who prefer local file inspection over web-based output viewing","teams using git to version-control outputs for reproducibility","developers building automated pipelines that need to retrieve and process Kaggle outputs"],"limitations":["Output organization scheme is undocumented; unclear if files are organized by execution ID, timestamp, or flat in .kaggle-outputs/","File naming conflicts are not documented; unclear if outputs are overwritten, renamed, or skipped if files already exist","No selective download; all outputs are downloaded even if only specific files are needed","No streaming or resumable downloads; large output files must be downloaded in full","Output size limits are not documented; unclear if there is a maximum total size or individual file size",".kaggle-outputs/ directory is not automatically added to .gitignore; users may accidentally commit large output files to git"],"requires":["Valid Kaggle API token","Completed notebook execution (with outputs available on Kaggle)","Network connectivity to api.kaggle.com and Kaggle's storage backend","Local disk space sufficient for downloaded outputs","Write access to workspace directory"],"input_types":["Execution ID (integer, from 'Runs' tree view or returned by push-and-run)","Download destination (string, defaults to .kaggle-outputs/)"],"output_types":["Output files (CSV, JSON, images, pickled models, etc.)","Executed notebook (.ipynb with cell outputs)","Execution logs (if available)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-dataquanta-vscode-kaggle-run__cap_9","uri":"capability://search.retrieval.sidebar.tree.view.navigation.for.notebooks.datasets.and.competitions","name":"sidebar tree view navigation for notebooks, datasets, and competitions","description":"Provides three searchable sidebar tree views that display Kaggle resources without leaving VS Code: 'My Notebooks' (lists user's notebooks filtered by language, type, competition), 'Datasets' (searchable dataset browser), and 'Competitions' (lists entered and featured competitions). Each tree view supports incremental search by name, owner, or competition context. Clicking on a resource in the tree view opens its details or triggers an action (e.g., clicking a notebook opens it, clicking a dataset attaches it).","intents":["I want to browse my Kaggle notebooks, datasets, and competitions without leaving VS Code","I need to quickly search for a specific notebook or dataset by name without visiting the Kaggle website","I want to discover featured competitions and datasets relevant to my interests"],"best_for":["data scientists who spend most of their time in VS Code and want to minimize context switching","teams managing multiple Kaggle projects and needing quick access to resources","developers building Kaggle-integrated workflows who want all resources discoverable in one place"],"limitations":["Search is limited to name, owner, and competition filters; no advanced filtering by size, license, data type, or creation date","Tree view is read-only; users cannot rename, delete, or modify resources from the sidebar","Pagination or lazy loading is not documented; unclear how many resources are displayed if a user has hundreds of notebooks or datasets","Tree view state is not persisted across VS Code sessions; expanded/collapsed state is lost when closing the extension","No sorting options; resources are displayed in an undocumented order (likely creation date or alphabetical)","Private resources are not clearly distinguished from public; users may accidentally share or expose private notebooks"],"requires":["Valid Kaggle API token","Network connectivity to api.kaggle.com","VS Code sidebar visible (not collapsed)"],"input_types":["Search query (string: notebook name, dataset name, competition name, owner)","Filter criteria (enum: language, type, competition status)"],"output_types":["Resource list (JSON: notebook/dataset/competition metadata)","Resource details (name, owner, size, description, etc.)","Actions triggered by clicking (open notebook, attach dataset, enter competition)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Valid Kaggle account with active API token","VS Code (minimum version unknown, likely 1.60+)",".ipynb notebook file in local workspace","kaggle.yml configuration file with kernel_slug and accelerator settings","Network connectivity to api.kaggle.com","Active Kaggle account (free or paid)","API token generated from kaggle.com/settings/account → API section","Either ~/.kaggle/kaggle.json file with username and api_key fields, OR VS Code credential storage access","Network connectivity to api.kaggle.com for token validation","VS Code workspace with write access to root directory"],"failure_modes":["Requires active internet connectivity to Kaggle API; all execution is remote and blocking until completion","No local execution fallback if Kaggle API is unavailable or rate-limited","Output download behavior and file naming conflict resolution are undocumented; unclear if outputs auto-overwrite or append","Concurrent execution limits unknown; unclear if multiple simultaneous notebook runs are supported","Execution timeout and output size limits are not documented and depend on Kaggle backend constraints","Token expiration is not automatically detected or refreshed; users must manually regenerate tokens at kaggle.com and update credentials","No token rotation or refresh mechanism; long-lived tokens pose security risk if compromised","File-based credentials at ~/.kaggle/kaggle.json are readable by any process on the machine; no encryption at rest","VS Code credential storage backend is OS-dependent (Keychain on macOS, Credential Manager on Windows, pass/secretservice on Linux); behavior varies across platforms","No multi-account support within a single VS Code window; switching accounts requires Sign Out and Sign In","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.32,"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":"2026-05-24T12:16:34.118Z","last_scraped_at":"2026-05-03T15:20:36.253Z","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=kaggle-studio","compare_url":"https://unfragile.ai/compare?artifact=kaggle-studio"}},"signature":"z/7UNEZU0d05ciT6F66hejBESdHnojmBC/K21VDJeOvZ0rFNQt/zLHpErEC6GNcOJE2OgA1kQlvgl/d5q7SECQ==","signedAt":"2026-06-19T12:38:48.238Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kaggle-studio","artifact":"https://unfragile.ai/kaggle-studio","verify":"https://unfragile.ai/api/v1/verify?slug=kaggle-studio","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"}}