DataLion

Use this skill for DataLion project, project-settings, data-import, codebook, chart-table, report, report-tab, dashboard, and export workflows.

In DataLion, "tab" and "dashboard" are used interchangeably for the report subpages managed under a report.

Default Strategy

1. Verify the datalion MCP server is enabled and inspect which datalion__* tools are actually available in the current session.
2. Use MCP first for supported actions.
3. Do not invent Datalion MCP tools. If a matching tool is missing, switch to a REST or browser/UI path from references/setup-and-capabilities.md.
4. Read first, write second. Restate the exact target object and proposed mutation before calling a write path.

Setup

• Run HybridClaw in host sandbox mode for this local Node-based MCP server.
• Install the bridge dependencies before first use: npm --prefix <path-to-your-datalion-repo>/mcp install
• Keep DATALION_API_TOKEN inside MCP server config env, never in tracked files or chat.
• Use the MCP server name datalion so tools appear as datalion__....
• For hybridclaw gateway mcp add ..., pass the JSON config as one quoted shell argument. In zsh, unquoted {...} and [...] will be expanded before HybridClaw sees them.
• See references/setup-and-capabilities.md for ready-to-paste CLI and TUI examples, dependency notes, and ability requirements.

Working Rules

• Always state whether you are using MCP, REST API, or browser/UI automation.
• Resolve the exact project, report, dashboard, export, or chart before mutating anything.
• The current bridge directly supports project listing, project reads, project creation, project settings updates, data source listing, CSV upload, full Excel/CSV import, report list/create and tab CRUD, chart-table reads, and codebook list, download, generation, deletion, and upload.
• The bridge also exposes 4 browser URL helpers for project/report/dashboard opening and widget insertion.
• The current bridge tool surface has 22 tools total: 18 backend MCP actions and 4 browser URL helpers.
• Prefer datalion__list_projects and datalion__read_project before writes when the exact target project is not already pinned down.
• Treat datalion__upload_data as a data-import tool, not a generic project-update tool.
• Treat datalion__edit_project as a defsettings merge tool. It updates only the keys you pass and keeps the existing settings for all other keys.
• Use datalion__list_data_sources to inspect what is already loaded into a project before uploading or troubleshooting data.
• Prefer datalion__import_excel_data when the source is an .xlsx workbook or when you want Datalion's full import pipeline, including optional codebook generation during import.
• Use datalion__list_codebook when the user needs a question-level inventory; use datalion__download_codebook for the tree structure.
• For workbook imports, prefer an absolute localPath so the bridge can read the file directly.
• For uploads, confirm filename, header and delimiter assumptions, data source name if relevant, and whether existing rows should be truncated.
• For import_excel_data, confirm projectId, file path or base64 file content, filename if you are not using localPath, whether you want the default main data source (useDefaultDataSource=true) or a named data source, and whether replaceData, runCalculations, convertComma, skipLines, comment, and createCodebook should be enabled.
• For project settings edits, confirm the target projectId and the exact defsettings keys and values before calling.
• For chart-table reads, confirm projectId, chartId, and any filter string before calling.
• For codebook uploads, confirm projectId, CSV filename, import mode, and whether any explicit column mapping is needed. The current backend expects columnMapping as an array aligned to the CSV header order.
• For reports, prefer direct MCP coverage first.
• For dashboard and export tasks without direct MCP coverage, inspect the local Datalion repo and its OpenAPI or route definitions before choosing a fallback path.
• Keep tokens, auth headers, and exported files out of logs unless the user explicitly asks for them.

Current MCP Coverage

The current datalion bridge exposes these direct tools:

• datalion__list_projects
• datalion__read_project
• datalion__create_project
• datalion__edit_project
• datalion__list_data_sources
• datalion__upload_data
• datalion__import_excel_data
• datalion__list_reports
• datalion__create_report
• datalion__create_report_tab
• datalion__edit_report_tab
• datalion__delete_report_tab
• datalion__open_project_browser
• datalion__open_report_browser
• datalion__open_dashboard_browser
• datalion__open_add_widget_browser
• datalion__get_chart_table
• datalion__list_codebook
• datalion__download_codebook
• datalion__generate_codebook
• datalion__delete_codebook
• datalion__upload_codebook

That means:

• project discovery and project detail reads are supported directly
• project creation is supported directly
• project settings updates through defsettings merges are supported directly
• project data sources can be listed directly
• CSV-style data import into an existing project is supported directly
• workbook and full-pipeline Excel/CSV import are supported directly
• the full-pipeline import path uses DataLion's datasource service rather than the raw CSV upload path, so it handles type detection, replace/append mode, optional codebook generation, and optional calculations
• report listing and report creation are supported directly
• chart tables can be read directly
• codebooks can be listed, downloaded, generated, deleted, and uploaded directly
• report editing beyond report-tab CRUD, dashboard editing, and export generation require fallback API or UI paths until the bridge grows more tools

Common Workflows

For explicit TUI testing, prefer /skill datalion ....

• /skill datalion create a project named "MCP Smoke Test"
• plain natural-language prompts that mention DataLion also work
• /datalion ... may still be routed as a normal message, but it is not a built-in slash-menu command, so do not use slash-menu visibility as the test for whether the skill is installed

Create a Project

1. Confirm the project name and any optional identcode or defsettings.
2. Call datalion__create_project.
3. Return the new project ID and recommend the next step, usually data import or report/dashboard setup.

Create a Report

1. Confirm the project ID and report name.
2. Call datalion__create_report.
3. Note that the report is seeded with a first tab and return both the report ID and first tab ID.

Manage Report Tabs

1. Call datalion__create_report_tab to add a tab to an existing report.
2. Call datalion__edit_report_tab to rename or update a tab.
3. Call datalion__delete_report_tab to remove a tab after confirming the report and tab IDs.
4. Keep projectId aligned with the report or tab/dashboard you are mutating.

Open In Browser

1. Use datalion__open_project_browser for the project screen.
2. Use datalion__open_report_browser for the report editor or a specific report tab/dashboard.
3. Use datalion__open_dashboard_browser for a specific tab/dashboard view.
4. Use datalion__open_add_widget_browser to get the modal URL used to add a question/widget to a tab/dashboard. The actual insertion still happens in the browser UI after the modal is opened.

Find or Read a Project

1. Call datalion__list_projects when the user gives a fuzzy project name or identcode.
2. Call datalion__read_project once you know the projectId.
3. Use the returned defsettings, categoriesCount, and dataSourcesCount to guide the next step.

Edit Project Settings

1. Confirm projectId and the exact defsettings keys to merge.
2. Call datalion__edit_project.
3. Return the updated keys and note that untouched settings stay as they were.

Upload Data

1. Confirm the target project ID.
2. Call datalion__list_data_sources first if you need to inspect existing data sources.
3. Prefer CSV text input for the current bridge.
4. Call datalion__upload_data.
5. Return the job ID and the import assumptions you used.

Import Excel or CSV via Datalion Pipeline

1. Confirm projectId and the source file path or file content.
2. Prefer localPath for .xlsx imports when the file exists on disk.
3. Use useDefaultDataSource=true when you want the workbook imported into the main project data table and codebook generation to read from that same table. Otherwise confirm dataSourceName.
4. Confirm whether createCodebook should run during import.
5. Call datalion__import_excel_data.
6. Return the data source name, imported row count, and whether codebook generation was requested.

Read a Chart Table

1. Confirm projectId, chartId, and filters.
2. Call datalion__get_chart_table.
3. Summarize the result and surface obvious caveats.

Codebook Workflows

1. For question-level inspection, call datalion__list_codebook.
2. For tree-structured inspection, call datalion__download_codebook.
3. For regenerate-from-data workflows, call datalion__generate_codebook.
4. For destructive cleanup, call datalion__delete_codebook and confirm whether includeTextboxes should be true.
5. For CSV import, call datalion__upload_codebook with the filename, file content, import mode, and any index-based columnMapping array that should align with the CSV header order.

Reports

1. Call datalion__list_reports to inspect existing reports for a project.
2. Call datalion__create_report to create a new report once the target project and report name are confirmed. The new report is seeded with a first tab and the response includes both IDs.
3. Use the report-tab tools for tab-level create/edit/delete work.
4. Treat report edits beyond tab CRUD as fallback work until direct MCP coverage exists.

Dashboards and Exports

1. Check current datalion__* tool coverage first.
2. If no direct tool exists, inspect openapi.yaml, routes/dashboard.php, and routes/export.php in the local Datalion checkout or use browser automation against the Datalion UI.
3. Prefer REST endpoints for API-backed CRUD and browser/UI paths for web-only export flows.
4. Be explicit about which path you chose and why.