{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"jupyter","slug":"jupyter","name":"Jupyter","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter","page_url":"https://unfragile.ai/jupyter","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"jupyter__cap_0","uri":"capability://code.generation.editing.interactive.cell.based.code.execution.with.kernel.abstraction","name":"interactive cell-based code execution with kernel abstraction","description":"Executes code cells individually against a Jupyter kernel process running in a separate process or remote environment, communicating via the Jupyter Wire Protocol. Each cell maintains execution state in the kernel, enabling incremental development workflows where variables persist across cell runs. The extension marshals code from the notebook editor to the kernel, captures stdout/stderr, and returns execution results without requiring full script re-execution.","intents":["Run Python code snippets interactively without leaving VS Code","Develop and test code incrementally with persistent kernel state","Execute cells in arbitrary order while maintaining variable context","Switch between different language kernels (Python, R, Julia) for polyglot notebooks"],"best_for":["data scientists and researchers prototyping analyses interactively","Python developers transitioning from Jupyter notebooks to VS Code","teams using polyglot notebooks with multiple language kernels"],"limitations":["Requires external Jupyter kernel installation — extension does not bundle or manage kernels","Kernel process must be running on local machine or accessible remote server; no built-in kernel provisioning","In browser contexts (vscode.dev, github.dev), local kernel execution is unavailable — requires pre-configured remote kernel","Cell execution order is user-controlled; no automatic dependency resolution between cells","Kernel crashes or hangs require manual restart via UI; no automatic recovery"],"requires":["Python 3.6+ with Jupyter package installed (pip install jupyter)","VS Code 1.67+ (specific minimum version not documented)","Jupyter kernel matching the notebook language (ipykernel for Python, IRkernel for R, etc.)","For remote kernels: SSH access or cloud kernel endpoint URL"],"input_types":["Python code (primary)","R code","Julia code","C# code","Any language with Jupyter kernelspec"],"output_types":["stdout/stderr text","structured execution results","error tracebacks","kernel state (variables, imports)"],"categories":["code-generation-editing","notebook-execution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_1","uri":"capability://image.visual.rich.mime.type.output.rendering.with.extensible.renderers","name":"rich mime-type output rendering with extensible renderers","description":"Renders cell execution outputs by detecting MIME types (text/plain, text/html, image/png, application/json, text/latex, application/vnd.plotly.v1+json, etc.) and delegating to specialized renderers. The Jupyter Notebook Renderers extension (auto-installed) provides built-in renderers for common types; custom renderers can be registered via the Notebook Renderer API. Output is displayed inline below the cell with support for interactive elements (Plotly charts, HTML widgets).","intents":["Display matplotlib plots, Plotly charts, and other visualizations inline in notebooks","Render LaTeX equations and mathematical notation","Show HTML tables, widgets, and interactive outputs from libraries like ipywidgets","Preview JSON, DataFrames, and structured data with formatted display"],"best_for":["data scientists and analysts working with visualization-heavy notebooks","researchers publishing computational narratives with mixed media output","teams building custom output renderers for domain-specific data types"],"limitations":["Only MIME types explicitly supported by registered renderers are displayed; unsupported types fall back to text/plain representation","Interactive widgets (ipywidgets) have limited interactivity in VS Code compared to JupyterLab — some widget state updates may not propagate correctly","Large outputs (e.g., high-resolution images, massive JSON) can degrade editor performance; no built-in pagination or lazy loading","Custom renderers require VS Code extension development; no simple configuration-based approach for custom MIME types","Web-based contexts (vscode.dev) may have restricted access to certain MIME types or rendering libraries"],"requires":["Jupyter Notebook Renderers extension (auto-installed with main extension)","Output-generating code that produces MIME-typed results (e.g., matplotlib.pyplot.show() produces image/png)","For custom renderers: VS Code extension development environment and knowledge of Notebook Renderer API"],"input_types":["Jupyter MIME bundle (dict with MIME type keys)","matplotlib Figure objects","Plotly Figure objects","pandas DataFrame objects","HTML strings","LaTeX strings"],"output_types":["rendered HTML in notebook cell output area","interactive SVG/Canvas elements","embedded images (PNG, SVG, JPEG)","formatted tables and data structures"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_10","uri":"capability://tool.use.integration.remote.kernel.connection.and.cloud.compute.integration","name":"remote kernel connection and cloud compute integration","description":"Allows connecting to Jupyter kernels running on remote servers or cloud platforms via SSH, HTTP, or cloud-specific endpoints. Users can configure remote kernel connections in VS Code settings or via the kernel picker UI, specifying connection details (host, port, authentication). The extension communicates with remote kernels using the Jupyter Wire Protocol over the network, enabling execution of code on remote compute resources without local installation. Supports GitHub Codespaces kernels and custom remote kernel servers.","intents":["Execute code on remote servers or cloud compute resources (AWS, GCP, Azure)","Use powerful remote GPUs or large memory machines for heavy computations","Collaborate on shared remote kernels with team members","Run notebooks in GitHub Codespaces without local Jupyter installation"],"best_for":["data scientists running heavy computations on cloud resources","teams collaborating on shared remote kernels","researchers using HPC clusters or cloud platforms for analysis"],"limitations":["Remote kernel connection requires manual configuration of host, port, and authentication; no built-in cloud provider integration (AWS, GCP, Azure)","SSH tunneling or HTTP proxy setup may be required for secure connections; configuration is complex and error-prone","Network latency can impact interactive development; cell execution and output transfer are slower than local kernels","Remote kernel discovery is not automated; users must know the kernel's address and credentials","No built-in support for kernel provisioning or scaling; users must manage remote kernel lifecycle separately","GitHub Codespaces integration is limited; kernel configuration is tied to Codespaces environment setup"],"requires":["Remote Jupyter kernel running on accessible server (SSH, HTTP, or cloud endpoint)","Network connectivity to remote kernel (SSH access or HTTP endpoint)","For SSH: SSH client and key-based authentication configured","For cloud platforms: cloud provider credentials and kernel endpoint URL"],"input_types":["remote kernel connection details (host, port, authentication)","cell code to execute on remote kernel"],"output_types":["execution results from remote kernel","remote kernel state (variables, outputs)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_11","uri":"capability://automation.workflow.notebook.metadata.and.kernel.configuration.persistence","name":"notebook metadata and kernel configuration persistence","description":"Stores notebook-level metadata (kernel name, language, custom settings) in the .ipynb file's 'metadata' JSON object. When a notebook is opened, the extension reads the stored kernel name and automatically selects that kernel, ensuring consistent execution environment across sessions. Users can also configure kernel-specific settings (e.g., Python environment variables, kernel arguments) in the notebook metadata or VS Code settings. Metadata is preserved when notebooks are shared or version-controlled.","intents":["Ensure notebooks use the correct kernel when opened by different users or on different machines","Store kernel-specific configuration (environment variables, arguments) with the notebook","Share notebook execution environment configuration via Git","Reproduce notebook execution with consistent kernel settings"],"best_for":["teams sharing notebooks via Git repositories","researchers ensuring reproducibility of computational results","projects requiring consistent kernel configuration across environments"],"limitations":["Kernel metadata is stored in .ipynb JSON; if the specified kernel is not available on the target machine, the notebook will fail to load","No automatic kernel installation or environment provisioning; users must manually install the required kernel","Kernel configuration is limited to what the kernel supports; complex environment setup (e.g., system dependencies) cannot be stored in metadata","No validation of kernel availability before opening notebook; errors occur only when attempting to execute","Metadata is human-readable JSON but editing it directly is error-prone; no UI for metadata editing"],"requires":["Kernel specified in notebook metadata must be installed on the target system","VS Code settings or notebook metadata configuration for kernel-specific options"],"input_types":["notebook metadata (kernel name, language, custom settings)","kernel configuration options"],"output_types":["kernel selection based on metadata","kernel configuration applied to execution"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_12","uri":"capability://data.processing.analysis.notebook.export.and.format.conversion","name":"notebook-export-and-format-conversion","description":"Exports notebooks to multiple formats (HTML, PDF, Markdown, Python script) using nbconvert integration. Triggered via command palette (`Jupyter: Export as...`) or right-click context menu. Requires nbconvert package and optional dependencies (pandoc for PDF, etc.) to be installed in the kernel environment. Exports preserve cell outputs, metadata, and formatting based on the target format.","intents":["Share analysis results in HTML format for stakeholders without Jupyter","Generate PDF reports from notebooks for documentation","Convert notebooks to Python scripts for production deployment","Export notebooks to Markdown for publishing on blogs or wikis"],"best_for":["Data scientists sharing analysis results with non-technical stakeholders","Teams generating reports from notebooks","Developers converting notebooks to production code"],"limitations":["Export quality depends on nbconvert version and optional dependencies; PDF export requires pandoc installation","Complex notebooks with custom CSS or JavaScript may not export correctly","Export process can be slow for large notebooks (>100MB)","Exported formats may lose interactive features (e.g., Plotly interactivity in static HTML)"],"requires":["nbconvert package installed in kernel environment (pip install nbconvert)","For PDF export: pandoc installed on system (separate installation)","For HTML export: no additional dependencies required"],"input_types":[".ipynb notebook file","Export format selection (HTML, PDF, Markdown, Python)"],"output_types":[".html files (static HTML)",".pdf files (PDF documents)",".md files (Markdown)",".py files (Python scripts)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_2","uri":"capability://memory.knowledge.variable.inspector.with.kernel.state.introspection","name":"variable inspector with kernel state introspection","description":"Displays a panel showing all variables currently defined in the kernel's namespace, including their type, shape (for arrays/DataFrames), and value. The extension queries the kernel using introspection commands (e.g., Python's dir() and type() functions) to populate the variable list. Clicking a variable can show its full representation or open a data viewer for large structures like DataFrames. The variable list updates after each cell execution.","intents":["Inspect variable types and values without writing print statements","Debug data shape and content issues in data processing pipelines","Monitor kernel state across multiple cell executions","Explore large DataFrames and arrays with built-in data viewer"],"best_for":["data scientists debugging data transformations and exploratory analysis","Python developers learning how variables change across cell execution","teams collaborating on notebooks who need to understand shared kernel state"],"limitations":["Variable introspection adds latency after each cell execution (kernel must enumerate namespace)","Large objects (e.g., DataFrames with millions of rows) may be slow to introspect or display","Only shows variables in the kernel's global namespace; local variables inside functions are not visible","Data viewer for DataFrames has limited sorting/filtering capabilities compared to dedicated data exploration tools","No support for watching specific variables or setting conditional breakpoints based on variable values"],"requires":["Active Jupyter kernel with Python 3.6+ (or equivalent introspection support in other languages)","Kernel must support dir() and type() introspection (standard in Python, R, Julia)"],"input_types":["kernel namespace state (queried via introspection)"],"output_types":["variable list with name, type, shape","formatted variable values","data viewer UI for structured data"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_3","uri":"capability://tool.use.integration.kernel.selection.and.multi.kernel.management","name":"kernel selection and multi-kernel management","description":"Provides UI for discovering, selecting, and switching between Jupyter kernels installed on the system or accessible remotely. The kernel picker (dropdown in notebook toolbar) queries the system for available kernelspecs (JSON files defining kernel metadata and launch commands) and allows users to select one. Switching kernels restarts the kernel process and clears the previous kernel's state. The extension can also auto-detect Python environments (conda, venv, pyenv) and create kernel entries for them.","intents":["Switch between Python versions or virtual environments without restarting VS Code","Use different language kernels (Python, R, Julia) in the same notebook","Connect to remote kernels running on servers or cloud platforms","Manage multiple kernel installations and select the appropriate one for a project"],"best_for":["data scientists working with multiple Python environments or language kernels","teams using polyglot notebooks with mixed-language analysis","researchers connecting to remote compute resources for heavy workloads"],"limitations":["Kernel discovery relies on kernelspec files in standard locations (~/.local/share/jupyter/kernels); custom kernel paths require manual configuration","No built-in kernel installation or environment management — users must install kernels separately (e.g., pip install ipykernel)","Switching kernels clears all variables and state from the previous kernel; no state preservation or migration","Remote kernel connection requires manual URL/SSH configuration; no built-in cloud provider integration (AWS, GCP, Azure)","Kernel startup time is not optimized; slow kernels (e.g., Julia) may cause UI freezing during selection"],"requires":["Jupyter kernels installed on system or accessible via network (kernelspec must be discoverable)","For Python: ipykernel package in the target environment (pip install ipykernel)","For remote kernels: SSH access or HTTP endpoint to kernel server"],"input_types":["kernelspec JSON files","Python environment paths (conda, venv, pyenv)","remote kernel URLs or SSH connection strings"],"output_types":["kernel selection UI dropdown","kernel metadata (name, language, version)","active kernel indicator in notebook toolbar"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_4","uri":"capability://automation.workflow.notebook.file.format.persistence.and.version.control.integration","name":"notebook file format persistence and version control integration","description":"Stores notebooks in the standard Jupyter .ipynb format (JSON with cells, metadata, outputs, and kernel info). The extension reads and writes .ipynb files directly, preserving cell order, execution counts, and output MIME bundles. Notebooks are version-controllable via Git; the extension provides no special merge conflict resolution, so conflicts must be resolved manually or with external tools. Cell metadata (tags, slide show settings) is preserved in the .ipynb JSON structure.","intents":["Save interactive notebooks as shareable, version-controllable files","Commit notebooks to Git with full history of code and outputs","Share notebooks across team members via Git repositories","Preserve cell execution order and output state for reproducibility"],"best_for":["teams collaborating on notebooks via Git repositories","researchers publishing computational narratives with reproducible outputs","projects requiring notebook version history and audit trails"],"limitations":["Merge conflicts in .ipynb files are difficult to resolve manually (JSON structure is complex); no built-in conflict resolution UI","Large outputs (e.g., high-resolution images) bloat .ipynb file size and slow down Git operations","Execution counts and output state are stored in the file; clearing outputs requires manual editing or external tools","No built-in support for notebook diffing or visual comparison of changes across versions","Notebooks with binary outputs (images, pickled objects) are not human-readable in Git diffs"],"requires":["VS Code file system access (local or remote via SSH/Codespaces)","For version control: Git installation and repository setup"],"input_types":[".ipynb JSON files","cell code, outputs, and metadata"],"output_types":[".ipynb files on disk","Git-compatible JSON format"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_5","uri":"capability://code.generation.editing.code.completion.and.intellisense.via.language.server.protocol","name":"code completion and intellisense via language server protocol","description":"Provides code completion, parameter hints, and inline documentation for notebook cells by delegating to VS Code's built-in language servers (Pylance for Python, etc.). As users type in a cell, the language server analyzes the code context (imports, variable definitions from previous cells) and suggests completions. The extension passes the cell's code and kernel state to the language server, enabling context-aware suggestions that account for variables defined in earlier cells.","intents":["Get code completion suggestions while typing in notebook cells","View parameter hints and documentation for functions and methods","Catch syntax errors and type mismatches before execution","Explore available methods and attributes on objects"],"best_for":["Python developers using VS Code's Pylance extension for advanced IntelliSense","teams standardizing on VS Code's language server ecosystem","users familiar with VS Code's code completion from regular Python files"],"limitations":["IntelliSense accuracy depends on the language server's ability to understand kernel state; dynamic imports or runtime-generated variables may not be recognized","Completion suggestions are based on static analysis, not kernel introspection; may miss variables defined in previous cells if the language server doesn't have access to kernel state","Language server support varies by language; Python (Pylance) is well-supported, but R, Julia, and other languages have limited or no IntelliSense","Slow language servers (e.g., Pylance on large projects) can cause UI lag when typing in cells","No built-in integration with kernel state; language server sees only the current cell's code, not variables from previous cells (unless explicitly passed)"],"requires":["VS Code language server for the notebook's language (Pylance for Python, etc.)","Language server must be installed and enabled in VS Code"],"input_types":["cell code being typed","kernel state (variables, imports) from previous cells"],"output_types":["completion suggestions dropdown","parameter hints and documentation","inline error/warning squiggles"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_6","uri":"capability://automation.workflow.cell.tagging.and.slide.show.presentation.mode","name":"cell tagging and slide show presentation mode","description":"Allows users to tag cells with metadata (e.g., 'slide', 'fragment', 'skip') via the Jupyter Cell Tags extension (auto-installed). Tagged cells can be organized into slide shows using the Jupyter Slide Show extension, which converts the notebook into a presentation format. Cells tagged as 'slide' become new slides, 'fragment' cells appear as bullet points, and 'skip' cells are hidden. The presentation can be viewed in a browser or exported to HTML/PDF.","intents":["Convert notebooks into presentations for sharing analysis results with non-technical audiences","Organize notebook cells into logical presentation sections","Hide implementation details (e.g., data loading code) from presentations","Export notebooks as standalone HTML or PDF presentations"],"best_for":["researchers and data scientists presenting findings to stakeholders","educators using notebooks for teaching and lectures","teams sharing analysis results in presentation format"],"limitations":["Slide show functionality is provided by the Jupyter Slide Show extension, not the core Jupyter extension; requires separate installation or auto-installation","Presentation styling and themes are limited; no built-in support for custom CSS or advanced layout control","Slide transitions and animations are basic (reveal.js-based); no support for complex presentation effects","Exporting to PDF requires external tools (e.g., pandoc, browser print-to-PDF); no built-in PDF export","Cell outputs (plots, tables) may not render correctly in presentation mode if they depend on interactive features"],"requires":["Jupyter Cell Tags extension (auto-installed)","Jupyter Slide Show extension (auto-installed or manually installed)","For PDF export: pandoc or browser with print-to-PDF capability"],"input_types":["notebook cells with slide/fragment/skip tags","cell outputs (plots, tables, text)"],"output_types":["HTML presentation (reveal.js format)","PDF presentation (via export)","in-browser slide show viewer"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_7","uri":"capability://code.generation.editing.debugging.with.breakpoints.and.step.through.execution","name":"debugging with breakpoints and step-through execution","description":"Enables setting breakpoints in notebook cells and stepping through code execution using VS Code's debugger UI. When a breakpoint is hit, the debugger pauses kernel execution and displays the call stack, local variables, and allows step-over/step-into/step-out operations. The extension communicates with the kernel's debugger protocol (e.g., Python's pdb or debugpy) to control execution. Breakpoints are set by clicking the gutter next to line numbers in cells.","intents":["Debug code errors by pausing execution at specific lines","Inspect variable values and call stack at breakpoints","Step through code line-by-line to understand execution flow","Identify where exceptions are raised in complex data processing pipelines"],"best_for":["Python developers debugging complex notebooks with errors","teams troubleshooting data processing pipelines","developers learning how code executes by stepping through it"],"limitations":["Debugging support depends on kernel debugger implementation; Python (debugpy) is well-supported, but R, Julia, and other languages have limited debugging capabilities","Breakpoints in cells that import or execute other code may not work correctly if the debugger doesn't have access to the full call stack","Debugging can be slow for large notebooks or complex code; stepping through many lines is tedious","No built-in support for conditional breakpoints or watch expressions (VS Code's debugger may support these, but kernel support varies)","Debugging state is lost if the kernel crashes or is restarted; no persistent breakpoint state across kernel restarts"],"requires":["Python 3.6+ with debugpy package (pip install debugpy) for Python debugging","VS Code debugger UI and configuration","Kernel must support the debugger protocol (e.g., pdb, debugpy for Python)"],"input_types":["cell code with breakpoints set","kernel execution state"],"output_types":["debugger UI with call stack, variables, and controls","paused kernel execution at breakpoint","step-through execution results"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_8","uri":"capability://automation.workflow.notebook.export.to.multiple.formats","name":"notebook export to multiple formats","description":"Exports notebooks to various formats (HTML, PDF, Markdown, Python scripts) using nbconvert, a Jupyter tool that converts .ipynb files to other formats. The extension provides UI commands to trigger exports; the actual conversion is performed by nbconvert running on the system. Exported files preserve cell code and outputs, with formatting tailored to the target format (e.g., HTML includes interactive outputs, Markdown includes code blocks).","intents":["Share notebooks as standalone HTML files without requiring Jupyter installation","Export analysis results as PDF reports for stakeholders","Convert notebooks to Python scripts for production deployment","Generate Markdown documentation from notebooks"],"best_for":["data scientists sharing analysis results with non-technical stakeholders","teams converting notebooks to production Python code","researchers publishing computational narratives as HTML or PDF"],"limitations":["Export functionality requires nbconvert to be installed on the system (pip install nbconvert); extension does not bundle nbconvert","PDF export requires additional dependencies (e.g., pandoc, LaTeX); setup can be complex","Interactive outputs (Plotly, ipywidgets) may not render correctly in exported formats; static images are used instead","Exported Python scripts lose cell structure and execution order; no automatic handling of cell dependencies","Custom export templates or styling requires nbconvert configuration; no built-in UI for customization"],"requires":["nbconvert package (pip install nbconvert)","For PDF export: pandoc and LaTeX installation","For HTML export: Jinja2 (usually installed with nbconvert)"],"input_types":[".ipynb notebook files"],"output_types":["HTML files","PDF files","Markdown files","Python scripts (.py)","reStructuredText files"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__cap_9","uri":"capability://automation.workflow.notebook.creation.and.file.management","name":"notebook creation and file management","description":"Provides commands to create new notebooks (.ipynb files) and manage notebook files within VS Code. Users can create a new notebook via the Command Palette ('Jupyter: Create New Jupyter Notebook') or the File menu, which creates an empty notebook with a default kernel selection. Notebooks are stored as .ipynb files in the file system and can be organized in folders like regular files. The extension integrates with VS Code's file explorer for notebook management.","intents":["Create new notebooks for exploratory analysis or prototyping","Organize notebooks in project directories","Open and edit existing .ipynb files from the file system","Manage notebook files alongside other project files in VS Code"],"best_for":["data scientists starting new analysis projects","teams organizing notebooks in shared repositories","developers transitioning from Jupyter to VS Code"],"limitations":["No built-in notebook templates; new notebooks are always empty with default kernel","File management is basic (create, rename, delete); no advanced organization features like notebook collections or projects","No built-in support for notebook discovery or search across multiple notebooks","Notebooks are stored as .ipynb files in the file system; no database or centralized notebook storage"],"requires":["VS Code file system access (local or remote)","Write permissions to the target directory"],"input_types":["notebook name and location"],"output_types":["new .ipynb file with default structure","notebook editor opened in VS Code"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jupyter__headline","uri":"capability://data.processing.analysis.jupyter.notebook.extension.for.visual.studio.code","name":"jupyter notebook extension for visual studio code","description":"A powerful extension that integrates Jupyter notebook support into Visual Studio Code, enabling interactive data analysis and programming with Python, R, and Julia in a familiar coding environment.","intents":["best Jupyter notebook extension","Jupyter support for data analysis in VS Code","interactive programming tools for Python","VS Code extensions for Jupyter notebooks","how to run Jupyter notebooks in Visual Studio Code"],"best_for":["data scientists","developers using Jupyter notebooks"],"limitations":["requires Visual Studio Code","performance may vary with heavy computations"],"requires":["Visual Studio Code installed"],"input_types":["Jupyter notebooks"],"output_types":["interactive data visualizations","code execution results"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+ with Jupyter package installed (pip install jupyter)","VS Code 1.67+ (specific minimum version not documented)","Jupyter kernel matching the notebook language (ipykernel for Python, IRkernel for R, etc.)","For remote kernels: SSH access or cloud kernel endpoint URL","Jupyter Notebook Renderers extension (auto-installed with main extension)","Output-generating code that produces MIME-typed results (e.g., matplotlib.pyplot.show() produces image/png)","For custom renderers: VS Code extension development environment and knowledge of Notebook Renderer API","Remote Jupyter kernel running on accessible server (SSH, HTTP, or cloud endpoint)","Network connectivity to remote kernel (SSH access or HTTP endpoint)","For SSH: SSH client and key-based authentication configured"],"failure_modes":["Requires external Jupyter kernel installation — extension does not bundle or manage kernels","Kernel process must be running on local machine or accessible remote server; no built-in kernel provisioning","In browser contexts (vscode.dev, github.dev), local kernel execution is unavailable — requires pre-configured remote kernel","Cell execution order is user-controlled; no automatic dependency resolution between cells","Kernel crashes or hangs require manual restart via UI; no automatic recovery","Only MIME types explicitly supported by registered renderers are displayed; unsupported types fall back to text/plain representation","Interactive widgets (ipywidgets) have limited interactivity in VS Code compared to JupyterLab — some widget state updates may not propagate correctly","Large outputs (e.g., high-resolution images, massive JSON) can degrade editor performance; no built-in pagination or lazy loading","Custom renderers require VS Code extension development; no simple configuration-based approach for custom MIME types","Web-based contexts (vscode.dev) may have restricted access to certain MIME types or rendering libraries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"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=jupyter","compare_url":"https://unfragile.ai/compare?artifact=jupyter"}},"signature":"Z8OPAyNWpFx+RfxifFuuGvPps4wP61qfP+TyEOoLBY81SYdW8+8q8yd8LXXOo/pZl69AhL+1lnYS5Jt0h7iwBg==","signedAt":"2026-06-23T13:11:46.300Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jupyter","artifact":"https://unfragile.ai/jupyter","verify":"https://unfragile.ai/api/v1/verify?slug=jupyter","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"}}