Datarails Financial Data Extraction

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Extract validated financial data from Datarails Finance OS to Excel workbooks. Generates sheets for P&L, Balance Sheet, KPIs (including ARR), and validation checks. Useful for financial analysts who need structured, cross-checked financial reports from the Datarails platform.

Sby Skills Guide Bot
Data & AIIntermediate
6/2/2026
Claude Code
#datarails#financial-data#excel#kpi#validation

Recommended for

Automated summary

Extracts validated financial data from Datarails Finance OS to an Excel workbook including P&L, Balance Sheet, KPIs, and cross-validation checks.

Strengths

  • Built-in validation with cross-references between P&L and KPI tables
  • Support for key metrics like ARR, Churn, and LTV
  • Automated generation of structured Excel workbooks
  • Handles pagination and automatic token refresh

Limitations

  • Requires prior authentication via /dr-auth
  • Depends on the 'reports' extra of the MCP server
  • Only works with Datarails Finance OS, not other data sources
When to use it

Use this skill when you need to export validated, structured financial data from Datarails to Excel for reporting or analysis.

When not to use it

Do not use it if you need real-time data or arbitrary database queries without built-in validation.

Security analysis

Safe
Quality score85/100

The skill uses only designated MCP tools for authentication, data extraction, and reading files; no destructive or exfiltration actions are performed.

No concerns found

How this analysis is made

Examples

Default extraction for current year
Extract financial data from Datarails and save to Excel.
Extraction with custom scenario and year
Extract financial data from Datarails for 2024 scenario Budget.
Extraction to custom file path
Extract financial data from Datarails and save to reports/financials.xlsx.

name: dr-extract description: Extract validated financial data from Datarails Finance OS to Excel. Creates workbooks with P&L, Balance Sheet, KPIs (including ARR), and validation checks. user-invocable: true allowed-tools:

  • mcp__datarails-finance-os__auth_status
  • mcp__datarails-finance-os__extract_financials
  • Read argument-hint: "[--output <file>] [--scenario <name>] [--year <YYYY>]"

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 Authentication

Use: auth_status
If not authenticated, guide to /dr-auth

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:

  1. Summary sheet: Key totals and metrics
  2. P&L sheet: Monthly breakdown by account category
  3. KPIs sheet: Quarterly KPI values
  4. 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 needs the reports extra installed: pip install datarails-finance-os-mcp[reports]

Token expires during extraction

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

  1. Run /dr-auth to get fresh credentials

Missing months in data

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

Related Skills

  • /dr-auth - Authenticate first
  • /dr-learn - Create/update client profile
  • /dr-tables - Explore available tables
  • /dr-query - Investigate specific records

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/datarails-financial-data-extraction-mm78nnlk/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/datarails-financial-data-extraction-mm78nnlk' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/datarails-financial-data-extraction-mm78nnlk/download' --output '.skill-downloads/datarails-financial-data-extraction-mm78nnlk/SKILL.md.part' && mv '.skill-downloads/datarails-financial-data-extraction-mm78nnlk/SKILL.md.part' '.skill-downloads/datarails-financial-data-extraction-mm78nnlk/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/datarails-financial-data-extraction-mm78nnlk/SKILL.md' && test -s '.skill-downloads/datarails-financial-data-extraction-mm78nnlk/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/dr-extract' && cp -R '.skill-downloads/datarails-financial-data-extraction-mm78nnlk' '.claude/skills/dr-extract'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/datarails-financial-data-extraction-mm78nnlk'

openskills read 'datarails-financial-data-extraction-mm78nnlk'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/datarails-financial-data-extraction-mm78nnlk/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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