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
Use this skill when you need to export validated, structured financial data from Datarails to Excel for reporting or analysis.
Do not use it if you need real-time data or arbitrary database queries without built-in validation.
Security analysis
SafeThe 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 madeExamples
Extract financial data from Datarails and save to Excel.Extract financial data from Datarails for 2024 scenario Budget.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:
- 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 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:
- Run
/dr-authto 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 filesThis 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.mdFrom 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 installationopenskills 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 syncManual 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.
Create Interactive Maps with kepler.gl
Data & AI
Create interactive map visualizations and export standalone HTML using the keplergl Python package. Use to plot geospatial data from DataFrames, GeoJSON, or CSV.
Agent-Driven Parameter Optimization
Data & AI
Guide for optimizing configurable systems with a measurable objective, using bounded trials and evidence-based recommendations.
ICP Visitor Outreach
Data & AI
Identify signed-up users matching the ideal customer profile but stalled, and draft personalized outreach to re-engage them.