{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ms-vscode-vscode-copilot-data-analysis","slug":"data-analysis-for-copilot","name":"Data Analysis for Copilot","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ms-vscode.vscode-copilot-data-analysis","page_url":"https://unfragile.ai/data-analysis-for-copilot","categories":["data-analysis","code-editors"],"tags":["ai","analysis","chat","chat-participant","co-pilot","data","github-copilot","python"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_0","uri":"capability://code.generation.editing.sandboxed.python.code.execution.for.llm.generated.analysis","name":"sandboxed python code execution for llm-generated analysis","description":"Executes Python code generated by Copilot in a Pyodide WebAssembly-based sandbox environment, enabling the LLM to perform computational tasks it cannot execute natively. The extension intercepts code generation requests from the Copilot chat interface, routes them to the Pyodide runtime, captures execution results (stdout, stderr, return values), and streams outputs back to the chat context. This architecture isolates untrusted LLM-generated code from the host system while providing a Python 3.x-compatible execution environment.","intents":["Run Python analysis code that Copilot generates without leaving the editor","Execute data transformations and calculations on CSV files in real-time","Validate that Copilot's generated Python code actually works before copying it","Iterate on analysis by re-running modified code snippets within chat"],"best_for":["Data analysts using VS Code who want to prototype analyses in natural language","Developers building data pipelines who need quick validation of transformation logic","Teams using GitHub Copilot as their primary IDE assistant"],"limitations":["Pyodide sandbox cannot access native file system — only in-memory data structures and explicitly loaded files","Limited to Python packages available in Pyodide distribution (numpy, pandas, matplotlib available; scipy, scikit-learn availability unknown)","No native system command execution or subprocess spawning possible","Network access capabilities unknown — may be restricted or unavailable in sandbox","Memory and CPU constraints of WebAssembly runtime may limit large dataset processing","Execution latency unknown but likely higher than native Python due to WASM interpretation overhead"],"requires":["VS Code with GitHub Copilot extension installed and authenticated","VS Code version (minimum version unknown from documentation)","Modern browser engine support for WebAssembly (implicit, as Pyodide requires it)"],"input_types":["Python code (generated by LLM or user-written)","CSV file data (loaded into memory)","Structured data (JSON, dictionaries, lists)"],"output_types":["Execution results (stdout/stderr text)","Return values (numeric, string, structured data)","Visualization objects (matplotlib figures, plotly charts)","Error messages and stack traces"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_1","uri":"capability://memory.knowledge.csv.file.context.injection.into.copilot.chat","name":"csv file context injection into copilot chat","description":"Integrates CSV files as first-class context objects within the Copilot chat interface, allowing users to reference files via natural language (e.g., 'Analyze the file #filename.csv') and enabling the LLM to access file metadata, schema, and sample data. The extension parses CSV headers, infers data types, and provides row counts and column statistics to the LLM without requiring manual copy-paste of file contents. This context is maintained across multiple chat turns, allowing iterative refinement of analyses.","intents":["Ask Copilot to analyze a CSV file without manually copying its contents into chat","Reference multiple CSV files in a single analysis request","Get Copilot to understand data schema and suggest appropriate transformations","Maintain file context across multiple follow-up questions in a chat session"],"best_for":["Data analysts who want to explore datasets through natural language conversation","Non-technical users who need to understand what's in a CSV without writing SQL or Python","Teams collaborating on data analysis where context needs to be shared in chat"],"limitations":["File context injection mechanism unknown — unclear if full file is loaded into memory or only sampled","Maximum file size for context injection not documented","Only CSV format explicitly supported — no mention of Excel, Parquet, or other tabular formats","Context window limitations of underlying LLM may truncate large files","No incremental or streaming context loading documented"],"requires":["CSV file open in VS Code editor or accessible via file explorer","GitHub Copilot extension with chat interface enabled","File must be in workspace or explicitly opened"],"input_types":["CSV files (.csv extension)","Natural language queries referencing file names"],"output_types":["LLM responses with file-aware suggestions","Generated Python code tailored to the file schema","Data summaries and insights"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_2","uri":"capability://planning.reasoning.intelligent.error.handling.and.code.retry.with.llm.feedback","name":"intelligent error handling and code retry with llm feedback","description":"When Python code execution fails in the Pyodide sandbox, the extension captures the error (exception type, message, stack trace) and feeds it back to Copilot with context about the original code and input data. The LLM then generates corrected code based on the error, which is automatically re-executed. The mechanism for 'smart' retry is not documented, but likely involves prompt engineering to guide the LLM toward common fixes (type errors, missing imports, logic errors). This creates a feedback loop where the LLM iteratively refines code until execution succeeds.","intents":["Automatically fix common Python errors without manual debugging","Get Copilot to suggest alternative approaches when code fails","Reduce iteration time by having Copilot re-generate code based on actual error messages","Learn what went wrong with generated code through LLM explanation"],"best_for":["Users with limited Python expertise who need help understanding and fixing errors","Rapid prototyping workflows where iteration speed matters more than code quality","Exploratory data analysis where trial-and-error is expected"],"limitations":["Retry mechanism and retry limits not documented — unclear if there's a maximum number of retries","No control over retry behavior — users cannot disable auto-retry or set custom retry logic","May mask underlying issues by repeatedly trying variations instead of addressing root cause","LLM may generate incorrect fixes that pass execution but produce wrong results (silent failures)","Retry loop could consume significant LLM API quota if enabled (cost implications unknown)","Error context passed to LLM may include sensitive data from CSV files"],"requires":["Python code execution to fail with an exception","GitHub Copilot with sufficient context window to receive error details","Active chat session with @data agent"],"input_types":["Python code (failed execution)","Error messages and stack traces","Original input data context"],"output_types":["Corrected Python code","Explanation of the error and fix","Execution results from retry"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_3","uri":"capability://image.visual.data.visualization.generation.from.natural.language","name":"data visualization generation from natural language","description":"Copilot generates Python visualization code (using matplotlib, plotly, or other Pyodide-compatible libraries) based on natural language requests like 'create a bar chart of sales by region'. The extension executes this code in the Pyodide sandbox and renders the resulting visualization (image or interactive chart) directly in the chat interface or as an exportable artifact. The visualization code is also made available for export to Jupyter notebooks or standalone Python files, enabling users to refine or reuse visualizations outside the chat context.","intents":["Generate charts and plots from CSV data using natural language descriptions","Explore data visually without writing matplotlib or plotly code manually","Export generated visualizations as standalone Python code for use in notebooks","Iterate on chart design by asking Copilot to modify colors, labels, or chart type"],"best_for":["Business analysts who need quick visualizations for presentations or reports","Data scientists exploring datasets before formal analysis","Non-technical stakeholders who want to understand data through charts"],"limitations":["Supported visualization libraries not fully documented — matplotlib and plotly likely available, but scipy.stats, seaborn, plotly express availability unknown","Interactive chart rendering in chat interface may have performance limitations for large datasets","Export format options not documented — unclear if SVG, PNG, PDF, or HTML export is supported","Chart customization options limited to what Copilot can express in code — no interactive chart editor","Visualization code quality depends on LLM's understanding of data and user intent — may generate misleading or incorrect charts","No built-in validation of chart appropriateness for data type (e.g., LLM might suggest pie chart for continuous data)"],"requires":["CSV file with data loaded in context","Copilot understanding of visualization libraries (implicit in model training)","Pyodide-compatible visualization library installed"],"input_types":["Natural language description of desired visualization","CSV data in memory","Optional: existing chart code to modify"],"output_types":["Rendered visualization (image or interactive chart)","Python code (matplotlib/plotly)","Exportable chart file (format unknown)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_4","uri":"capability://data.processing.analysis.predictive.modeling.and.statistical.analysis.code.generation","name":"predictive modeling and statistical analysis code generation","description":"Copilot generates Python code for statistical analysis and predictive modeling tasks (e.g., 'build a linear regression model to predict sales') based on natural language requests and CSV data context. The extension executes this code in the Pyodide sandbox, capturing model outputs (coefficients, R-squared, predictions) and making them available in chat. Specific model types and algorithms supported are not documented, but likely include regression, classification, and clustering models from scikit-learn or similar libraries. Generated code is exportable for use in Jupyter notebooks or production pipelines.","intents":["Generate predictive models from CSV data without writing scikit-learn code manually","Get statistical summaries and model performance metrics from natural language requests","Understand model results through LLM-generated explanations","Export trained models or modeling code for use in production"],"best_for":["Data scientists prototyping models quickly in the editor","Business analysts building simple predictive models without ML expertise","Teams validating model ideas before investing in formal ML pipelines"],"limitations":["Supported algorithms and model types not documented — unclear which scikit-learn, statsmodels, or other libraries are available","Model training happens in Pyodide sandbox — performance may be poor for large datasets or complex models","No hyperparameter tuning or cross-validation automation documented — LLM must explicitly request these","Model persistence not documented — unclear if trained models can be saved/loaded or only exist in memory during chat session","No feature engineering automation — LLM must generate feature transformation code explicitly","Model evaluation metrics and validation approaches depend entirely on LLM's knowledge and user guidance","No guardrails against generating inappropriate models (e.g., biased models, overfitting on small datasets)"],"requires":["CSV file with labeled data (for supervised learning)","Pyodide-compatible ML library (scikit-learn availability unknown but likely)","Sufficient data and computational resources for model training in WASM sandbox"],"input_types":["Natural language model specification","CSV data with features and target variable","Optional: model parameters or algorithm preferences"],"output_types":["Trained model object (in-memory)","Model performance metrics (accuracy, R-squared, etc.)","Predictions on test data","Python code for model training and evaluation"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_5","uri":"capability://data.processing.analysis.csv.data.cleaning.and.transformation.code.generation","name":"csv data cleaning and transformation code generation","description":"Copilot generates Python code for common data cleaning tasks (handling missing values, removing duplicates, type conversion, filtering, aggregation) based on natural language descriptions of desired transformations. The extension executes this code in the Pyodide sandbox on the loaded CSV data, displaying the transformed dataset and making the transformation code available for export. This enables users to clean and prepare data for analysis without writing pandas code manually, with immediate feedback on the results of each transformation.","intents":["Remove or impute missing values in CSV files using natural language instructions","Filter, sort, and aggregate data without writing pandas code","Convert data types and rename columns based on natural language requests","Export data cleaning code for reproducibility or use in production pipelines"],"best_for":["Data analysts preparing datasets for analysis or reporting","Non-technical users who need to clean data but lack Python skills","Teams building repeatable data preparation workflows"],"limitations":["Pandas availability in Pyodide not explicitly confirmed — likely available but version unknown","Complex transformations may require multiple iterations if LLM doesn't understand intent correctly","No data validation or quality checks beyond what LLM explicitly generates","Transformation code quality depends on LLM's understanding of data semantics — may generate incorrect transformations","No undo/rollback mechanism documented — transformations are applied sequentially without checkpoints","Performance may degrade for large CSV files due to WASM execution overhead","No support for streaming or chunked processing of very large files"],"requires":["CSV file loaded in VS Code","Pandas library available in Pyodide environment","Natural language description of desired transformation"],"input_types":["CSV data (loaded into pandas DataFrame)","Natural language transformation requests","Optional: data quality rules or constraints"],"output_types":["Transformed CSV data (in-memory or exportable)","Python code (pandas transformations)","Data quality report (if requested)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_6","uri":"capability://code.generation.editing.code.export.to.jupyter.notebooks.and.python.files","name":"code export to jupyter notebooks and python files","description":"The extension captures all Python code generated and executed during a chat session (data cleaning, analysis, visualization, modeling) and makes it available for export as a Jupyter notebook (.ipynb) or standalone Python script (.py). This enables users to take exploratory work done in chat and convert it into reproducible, shareable artifacts. The exported code includes markdown cells with explanations (likely generated by Copilot) and preserves the logical flow of the analysis.","intents":["Convert exploratory analysis from chat into a Jupyter notebook for sharing with colleagues","Export generated code as a Python script for integration into data pipelines","Create reproducible analysis documentation from chat interactions","Transition from interactive chat-based exploration to formal analysis workflows"],"best_for":["Data scientists who want to document exploratory work formally","Teams collaborating on analyses and needing shareable notebooks","Users transitioning from chat-based exploration to production code"],"limitations":["Export format options not fully documented — Jupyter and Python files mentioned, but no mention of HTML, PDF, or markdown export","Notebook structure and cell organization logic not documented — unclear how chat turns map to notebook cells","Markdown explanations quality depends on LLM — may be incomplete or inaccurate","Exported code may not run outside Pyodide environment if it relies on Pyodide-specific packages or APIs","No automatic dependency documentation — exported code may reference packages not listed in requirements.txt","No version control integration documented — exported notebooks are static snapshots"],"requires":["At least one code execution in the chat session","VS Code with Jupyter extension installed (for notebook export)","File system access to save exported files"],"input_types":["Generated Python code from chat session","Chat history and explanations"],"output_types":["Jupyter notebook (.ipynb)","Python script (.py)","Optional: markdown documentation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_7","uri":"capability://tool.use.integration.right.click.context.menu.integration.for.csv.files","name":"right-click context menu integration for csv files","description":"The extension registers a right-click context menu option on CSV files in the VS Code file explorer, allowing users to trigger data analysis workflows directly from the file tree without opening the file first. Selecting this option likely opens the Copilot chat interface with the CSV file pre-loaded as context, enabling immediate natural language analysis requests. This integration reduces friction for users who want to analyze files without navigating to the editor first.","intents":["Quickly analyze a CSV file by right-clicking it in the file explorer","Start a data analysis chat session without manually opening the file","Discover the data analysis feature through familiar VS Code context menu patterns"],"best_for":["Users who prefer file explorer navigation over editor-based workflows","Teams with many CSV files who need quick analysis without opening each file","Users discovering the extension for the first time through context menu discoverability"],"limitations":["Context menu option only available for .csv files — no support for other tabular formats","Behavior when right-clicking on files outside the workspace not documented","No customization of context menu label or icon documented","May not work with very large CSV files if file loading is triggered immediately"],"requires":["CSV file in VS Code workspace or open folder","Data Analysis for Copilot extension installed and enabled","GitHub Copilot extension with chat interface"],"input_types":["CSV file path (from file explorer context)"],"output_types":["Copilot chat interface with file pre-loaded"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_8","uri":"capability://tool.use.integration.inline.csv.file.analysis.trigger.via.editor.icon","name":"inline csv file analysis trigger via editor icon","description":"When a CSV file is open in the VS Code editor, the extension displays an icon (likely in the editor toolbar or gutter) that users can click to trigger data analysis workflows. Clicking this icon opens the Copilot chat interface with the current CSV file pre-loaded as context, enabling immediate analysis requests. This provides an alternative to right-click context menus and makes the feature more discoverable to users actively editing CSV files.","intents":["Analyze the currently open CSV file without switching to file explorer","Discover the data analysis feature through an inline editor icon","Quickly start a chat session while viewing the file being analyzed"],"best_for":["Users actively editing or viewing CSV files in the editor","Workflows where file analysis is a natural next step after opening","Users who prefer toolbar-based UI over context menus"],"limitations":["Icon placement and appearance not documented — may be inconsistent with other VS Code extensions","Icon may not be visible if editor toolbar is hidden or customized","Only works for CSV files currently open in editor — not available for unopened files","No keyboard shortcut documented as alternative to clicking icon"],"requires":["CSV file open in VS Code editor","Data Analysis for Copilot extension installed and enabled","GitHub Copilot extension with chat interface"],"input_types":["CSV file currently open in editor"],"output_types":["Copilot chat interface with file pre-loaded"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-vscode-vscode-copilot-data-analysis__cap_9","uri":"capability://memory.knowledge.multi.turn.chat.context.preservation.across.analysis.iterations","name":"multi-turn chat context preservation across analysis iterations","description":"The extension maintains CSV file context and execution state across multiple chat turns, allowing users to ask follow-up questions and request modifications without re-loading the file or repeating context. The LLM retains awareness of previous analyses, generated code, and execution results, enabling iterative refinement of analyses through natural conversation. This context includes the current state of the data (after transformations), previously generated visualizations, and model outputs.","intents":["Ask follow-up questions about data without re-specifying the file or previous analysis","Iteratively refine visualizations or models through multiple chat turns","Build complex analyses incrementally through conversation","Maintain analysis narrative and reasoning across multiple steps"],"best_for":["Exploratory data analysis workflows requiring iterative refinement","Users building complex analyses through natural conversation","Teams collaborating on analyses where context needs to persist across multiple questions"],"limitations":["Context window size limits how much history can be maintained — very long chat sessions may lose earlier context","State management of transformed data not documented — unclear if data transformations persist across turns or are recalculated","No explicit context management UI — users cannot see or control what context is being maintained","Context may include sensitive data from CSV files — no documented privacy controls","No session persistence documented — context is lost if chat session is closed"],"requires":["Active chat session with @data agent","CSV file loaded in initial chat turn","GitHub Copilot with sufficient context window"],"input_types":["Natural language follow-up questions","Requests for modifications to previous analyses"],"output_types":["LLM responses aware of previous context","Incremental code generation based on prior work","Cumulative analysis results"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["VS Code with GitHub Copilot extension installed and authenticated","VS Code version (minimum version unknown from documentation)","Modern browser engine support for WebAssembly (implicit, as Pyodide requires it)","CSV file open in VS Code editor or accessible via file explorer","GitHub Copilot extension with chat interface enabled","File must be in workspace or explicitly opened","Python code execution to fail with an exception","GitHub Copilot with sufficient context window to receive error details","Active chat session with @data agent","CSV file with data loaded in context"],"failure_modes":["Pyodide sandbox cannot access native file system — only in-memory data structures and explicitly loaded files","Limited to Python packages available in Pyodide distribution (numpy, pandas, matplotlib available; scipy, scikit-learn availability unknown)","No native system command execution or subprocess spawning possible","Network access capabilities unknown — may be restricted or unavailable in sandbox","Memory and CPU constraints of WebAssembly runtime may limit large dataset processing","Execution latency unknown but likely higher than native Python due to WASM interpretation overhead","File context injection mechanism unknown — unclear if full file is loaded into memory or only sampled","Maximum file size for context injection not documented","Only CSV format explicitly supported — no mention of Excel, Parquet, or other tabular formats","Context window limitations of underlying LLM may truncate large files","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.51,"quality":0.45,"ecosystem":0.45,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:33.198Z","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=data-analysis-for-copilot","compare_url":"https://unfragile.ai/compare?artifact=data-analysis-for-copilot"}},"signature":"7U3aieW1VnghkQ2prRJDdP6OYUBNrZaMKRFbzhvmaTm+RrSGHopzH2Ierr3KESo7U/geQdN8oXFDHmPc/t5dAg==","signedAt":"2026-06-15T17:50:09.305Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/data-analysis-for-copilot","artifact":"https://unfragile.ai/data-analysis-for-copilot","verify":"https://unfragile.ai/api/v1/verify?slug=data-analysis-for-copilot","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"}}