Data Analysis Skills

# Spreadsheet Analysis Skill Use this skill to analyze spreadsheet documents (Excel `.xlsx`, `.xls`, `.ods`, `.csv`) stored in Vertesia using Python (`pandas`, `openpyxl`) inside a Daytona sandbox. > **When to use this skill vs data-platform:** > - **Use this skill** for one-off analysis of uploaded files, quick exploration, or when working with Vertesia document IDs > - **Use `data_analysis` (data-platform)** when data is in a persistent data store, you need SQL queries, or want versioned/reusable analytics The goal is to: - **Discover the dataset shape first** (sheets, columns, types, ranges, missingness). - Then perform **targeted analysis** (aggregations, trends, segments) using that schema. - Use **artifacts** (`write_artifact`, `read_artifact`, `grep_artifacts`) to pass data and reuse scripts/results across steps. Important: - You **must** work with Vertesia document IDs, not local file system paths like `/Users/...`. - Whenever you need to analyze spreadsheets, you must fir...

# Create Spreadsheet Skill Use this skill to create spreadsheets (Excel `.xlsx` or `.csv`) from data available in Vertesia or artifacts. The recommended pattern is: 1. **Stage inputs as artifacts** (CSV/JSON/text) or pull them from Vertesia documents. 2. **Write a Python script as an artifact** under `scripts/`. 3. **Execute the script via `execute_shell`** in the Daytona sandbox. 4. **Write the spreadsheet into `/home/daytona/out/`** so it becomes an `out/*` artifact that can be reused or downloaded. For long‑lived knowledge base objects, a human or a follow‑up workflow can ingest the resulting `.xlsx` file (e.g. via `create_content_object` when it is available on a reachable URL). --- ## 1. Prepare input data as artifacts or documents ### 1.1 From existing documents If data already lives in Vertesia: 1. Use `search_documents` to find the right documents and get their `id`s. 2. Use `fetch_document` when you need text content for context, or: 3. Use `execute_shell` with `docu...

# Chart & Dashboard Creation Create visualizations using Vega-Lite via markdown code blocks. The UI renders them automatically with tooltips and interactivity. > **Related Skills:** > - **`output_reference`** - How to format chart code blocks and use artifact references > - **`vega_reference`** - Complete Vega-Lite reference (marks, encodings, transforms, scales, number formatting, color schemes) > - **`data_visualization`** - For persistent SQL-backed dashboards > **When to use this skill vs `data_visualization`:** > - **Use this skill** for one-off charts, inline data, artifact-backed data, or when exploring data interactively > - **Use `data_visualization`** when you need persistent dashboards saved to a data store, SQL-backed queries, or reusable parameterized dashboards --- ## When to Use What ### Use a Single Chart when: - Showing ONE visualization (bar chart, line chart, pie, heatmap, etc.) - User asks for "a chart", "show me", "visualize", "plot" - No need to click one t...

# Advanced Dashboard Patterns This skill covers **advanced dashboard patterns**. For basic dashboard creation, see the `make_chart` skill. > **Related Skills:** > - **`output_reference`** - General output formatting and when to use different visualization types > - **`vega_reference`** - Complete Vega-Lite documentation (marks, encodings, transforms, scales, number formatting) --- ## When to Use This Skill Use these patterns when: - User wants **input controls** (sliders, dropdowns, checkboxes) - Complex **multi-stage filtering** (filter A → filter B → filter C) - **Responsive layouts** that adapt to different screen sizes - **Multiple data sources** in one dashboard - **Advanced brush interactions** beyond basic zoom --- ## 1. Dashboard with Input Controls Add sliders, dropdowns, and checkboxes for user configuration. ```chart { "library": "vega-lite", "title": "Configurable Analysis", "description": "Use controls to adjust the visualization", "options": {"mode": "da...

# Mermaid Diagram Skill Use Mermaid syntax to create visual diagrams directly in your responses. Diagrams are rendered automatically when you use a `mermaid` code block. > **See also:** `output_reference` skill for general output formatting guidelines and when to use different visualization types. ## When to Use Use Mermaid diagrams to: - Illustrate workflows and processes (flowcharts) - Show system interactions (sequence diagrams) - Document data models (ER diagrams) - Explain state machines (state diagrams) - Visualize project timelines (Gantt charts) - Map out class relationships (class diagrams) - Create mind maps for brainstorming ## Syntax Wrap your Mermaid code in a fenced code block with `mermaid` as the language: ````markdown ```mermaid graph TD A[Start] --> B{Decision} B -->|Yes| C[Action] B -->|No| D[End] ``` ```` ### Export-Safe Label Formatting (PDF/DOCX) When diagrams may be exported to PDF or DOCX, prefer renderer-safe labels: - Use Mermaid line br...

# Update Spreadsheet Skill Use this skill to **update existing spreadsheet documents** stored in Vertesia: - Adjust or clean data. - Add or modify sheets. - Append new rows or computed metrics. - Regenerate summary tabs, while preserving raw data where appropriate. All updates are done via code running in the Daytona sandbox, with **scripts and data managed as artifacts**. --- ## 1. Locate and download the spreadsheet 1. Identify the document: - If the user knows the document ID, use it directly. - Otherwise, use `search_documents` (by name, type, or full_text) to find the correct spreadsheet document and get its `id`. 2. Download the file into the sandbox using `execute_shell` and the `documents` parameter: - `documents=[{"id": "<DOC_ID>", "mode": "file"}]` - `command="ls -l /home/daytona/documents"` The sandbox will contain: - `/home/daytona/documents/<DOC_ID>.<ext>` (e.g. `.xlsx` or `.csv`). > Treat `/home/daytona/documents` as the source of truth for the original s...

# View Chart (Model Self-Check) Render a chart to PNG so **you can see it** and verify it looks correct. The user already sees charts in the browser - this is for your own visual verification. ## When to Use - You want to verify a chart looks correct before finalizing - User asks you to check/verify the chart appearance - You need visual feedback on complex visualizations - Debugging chart layout or styling issues ## How to Render Use Python with `vl-convert` to render any Vega-Lite spec: ```python import vl_convert as vlc # Your Vega-Lite spec (same format as chart code blocks) spec = { "$schema": "https://vega.github.io/schema/vega-lite/v6.json", "width": 600, "height": 400, "data": { "values": [ {"category": "A", "value": 28}, {"category": "B", "value": 55}, {"category": "C", "value": 43} ] }, "mark": "bar", "encoding": { "x": {"field": "category", "type": "nominal"}, "y": {"field...

# Excel Editing Skill Use this skill to make **targeted edits to an existing `.xlsx` artifact** — changing cell values, applying formulas, modifying formatting, adding or removing rows/columns, managing sheets. > For creating a new spreadsheet from data, see the `create_spreadsheet` skill. > For analyzing/querying spreadsheet data, see the `analyze_spreadsheet` skill. > For DOCX editing, see the `docx_editing` skill. For PPTX editing, see the `pptx_editing` skill. Unlike DOCX/PPTX, Excel uses a shared-strings table and cross-referenced XML parts that aren't amenable to direct xpath patching. The correct approach is **openpyxl in the sandbox** — it handles the data model correctly and supports formulas, formatting, and multi-sheet workbooks. --- ## Workflow ### 1. Get the .xlsx artifact ``` # From a Vertesia document: fetch_document(id: "doc_123", output_artifact: true) # → creates docs/doc_123.xlsx # Already exists as an artifact: list_artifacts(prefix: "out/") # → out/report....