Dr Extract

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dr-extract

Dr Extract is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Extract validated financial data from Datarails Finance OS to Excel. Creates workbooks with P&L, Balance Sheet, KPIs (including ARR), and validation checks.

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QUICK START

How to use this skill

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Prompt to paste

Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/extract-datarails-dr-claude-code-plugi/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/dr-extract, but wait for my approval before writing files or running scripts.

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Datarails Financial Data Extraction

Extract validated financial data from Finance OS to Excel workbooks with:

P&L Data: Revenue, COGS, Operating Expenses by month
KPI Data: ARR, Net New ARR, Churn, LTV, Revenue by quarter
Validation: Cross-checks between P&L and KPI tables

Arguments

Argument	Description	Default
`--output <file>`	Output filename	`tmp/Financial_Extract_YYYY.xlsx`
`--scenario <name>`	Primary scenario	`Actuals`
`--year <YYYY>`	Calendar year to extract	Current year

Workflow

Step 1: Verify Connection

If any Datarails tool call fails with an authentication or connection error, tell the user:

The Datarails connector isn't connected. Click the "+" button next to the prompt, select Connectors, find Datarails, and click Connect.

Then STOP — do not retry until the user has reconnected.

Step 2: Run Extraction via MCP Tool

Call the extract_financials MCP tool with the parsed arguments:

Use: extract_financials
Arguments:
  year: <parsed year, default current year>
  scenario: <parsed scenario, default "Actuals">
  output_path: <parsed output, or omit for default>

The tool handles:

Loading the client profile for the environment
Pagination (500 rows per request) with auto token refresh
Client-side aggregation
Excel generation with openpyxl

Step 3: Report Results

Present the extraction summary to the user:

Output file path
Year and scenario extracted
Any errors or warnings

Expected Output

The tool generates an Excel workbook with:

Summary sheet: Key totals and metrics
P&L sheet: Monthly breakdown by account category
KPIs sheet: Quarterly KPI values
Validation sheet: Cross-checks and profile info

Output location: tmp/ folder (configurable via --output)

Troubleshooting

"profile_not_found" error

Run /dr-learn first to create a profile.

"missing_dependency" error

The MCP server is hosted remotely — this error should not occur. If it does, contact support.

Token expires during extraction

The script auto-refreshes tokens every 20K rows. If you still get 401 errors:

Reconnect via Connectors UI ("+" > Connectors > Datarails)

Missing months in data

Check System_Year filter value - must be a string ("2025"), not integer.

Related Skills

Connect via Connectors UI first
/dr-learn - Create/update client profile
/dr-tables - Explore available tables
/dr-query - Investigate specific records