datapack-builder

anthropics/financial-services-plugins · updated Apr 8, 2026

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$npx skills add https://github.com/anthropics/financial-services-plugins --skill datapack-builder
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summary

Build professional, standardized financial data packs for private equity, investment banking, and asset management. Transform financial data from CIMs, offering memorandums, SEC filings, web search, or MCP server access into polished Excel workbooks ready for investment committee review.

skill.md

Financial Data Pack Builder

Build professional, standardized financial data packs for private equity, investment banking, and asset management. Transform financial data from CIMs, offering memorandums, SEC filings, web search, or MCP server access into polished Excel workbooks ready for investment committee review.

Important: Use the xlsx skill for all Excel file creation and manipulation throughout this workflow.

CRITICAL SUCCESS FACTORS

Every data pack must achieve these standards. Failure on any point makes the deliverable unusable.

1. Data Accuracy (Zero Tolerance for Errors)

  • Trace every number to source document with page reference
  • Use formula-based calculations exclusively (no hardcoded values)
  • Cross-check subtotals and totals for internal consistency
  • Verify balance sheet balances: Assets = Liabilities + Equity
  • Confirm cash flow ties to balance sheet changes

2. ESSENTIAL RULES

RULE 1: Financial data (measuring money) → Currency format with $ Triggers: Revenue, Sales, Income, EBITDA, Profit, Loss, Cost, Expense, Cash, Debt, Assets, Liabilities, Equity, Capex Format: $#,##0.0 for millions, $#,##0 for thousands Negatives: $(123.0) NOT -$123

RULE 2: Operational data (counting things) → Number format, NO $ Triggers: Units, Stores, Locations, Employees, Customers, Square Feet, Properties, Headcount Format: #,##0 with commas Negatives: (123) consistent with rest of table

RULE 3: Percentages (rates and ratios) → Percentage format Triggers: Margin, Growth, Rate, Percentage, Yield, Return, Utilization, Occupancy Format: 0.0% for one decimal place Display: 15.0% NOT 0.15

RULE 4: Years → Text format to prevent comma insertion Format: Text or custom to prevent 2,024 Display: 2020, 2021, 2022, 2023A, 2024E

RULE 5: When context is mixed, each metric gets its own appropriate format Example:

Segment Analysis, 2022, 2023, 2024
Retail Revenue, $50.0, $55.0, $60.0
  Stores, 100, 110, 120
  Revenue per Store, $0.5, $0.5, $0.5

Revenue and per-store metrics use $, Store count uses number format.

RULE 6: Use formulas for all calculations → Never hardcode calculated values All subtotals, totals, ratios, and derived metrics must be formula-based, not hardcoded values. This ensures accuracy and allows for dynamic updates.

3. Professional Presentation Standards

Formatting Standards:

Color Scheme - Two Layers:

Layer 1: Font Colors (MANDATORY from xlsx skill)

  • Blue text (RGB: 0,0,255): ALL hardcoded inputs (historical data, assumptions), NOT normal text
  • Black text (RGB: 0,0,0): ALL formulas and calculations
  • Green text (RGB: 0,128,0): Links to other sheets

Layer 2: Fill Colors (Optional for enhanced presentation)

  • Fill colors are optional and should only be applied if requested by the user or if enhancing presentation
  • If the user requests colors or professional formatting, use this standard scheme:
    • Section headers: Dark blue (RGB: 68,114,196) background with white text
    • Sub-headers/column headers: Light blue (RGB: 217,225,242) background with black text
    • Input cells: Light green/cream (RGB: 226,239,218) background with blue text
    • Calculated cells: White background with black text
  • Users can override with custom brand colors if specified

How the layers work together (if fill colors are used):

  • Input cell: Blue text + light green fill = "User-entered data"
  • Formula cell: Black text + white background = "Calculated value"
  • Sheet link: Green text + white background = "Reference from another tab"

Font color tells you WHAT it is. Fill color tells you WHERE it is (if used).

IMPORTANT: Font colors from xlsx skill are mandatory. Fill colors are optional - default is white/no fill unless the user requests enhanced formatting or colors.

Always apply:

  • Bold headers, left-aligned
  • Numbers right-aligned
  • 2-space indentation for sub-items
  • Single underline above subtotals
  • Double underline below final totals
  • Freeze panes on row/column headers
  • Minimal borders (only where structurally needed)
  • Consistent font (typically Calibri or Arial 11pt)

Never include:

  • Borders around every cell
  • Multiple fonts or font sizes
  • Charts unless specifically requested
  • Excessive formatting or decoration

Structural Consistency

Use the standard 8-tab structure unless explicitly instructed otherwise:

  1. Executive Summary
  2. Historical Financials (Income Statement)
  3. Balance Sheet
  4. Cash Flow Statement
  5. Operating Metrics
  6. Property/Segment Performance (if applicable)
  7. Market Analysis
  8. Investment Highlights

Tab 1: Executive Summary

Purpose: One-page overview for busy executives

Contents:

  • Company overview (2-3 sentences on business model)
  • Key investment highlights (3-5 bullet points)
  • Financial snapshot table (Revenue, EBITDA, Growth for last 3 years + projections)
  • Transaction overview if applicable
  • Key metrics prominently displayed

Format: Clean, bold headers, minimal decoration, critical numbers emphasized

Tab 2: Historical Financials (Income Statement)

Purpose: Complete profit and loss history

Contents:

  • Revenue breakdown by segment/product line
  • Cost of goods sold / Cost of revenue
  • Gross profit and gross margin %
  • Operating expenses detailed (S&M, R&D, G&A)
  • EBITDA and Adjusted EBITDA
  • Below-the-line items (D&A, interest, taxes)
  • Net income

Format:

  • Years as columns (text format: 2020, 2021, 2022)
  • $ millions or $ thousands (specify units clearly at top)
  • Accounting format for all financial data
  • Single underline above subtotals, double underline below net income
  • Right-align all numbers

Tab 3: Balance Sheet

Purpose: Financial position at period end

Contents:

  • Current assets (cash, AR, inventory, prepaid, other)
  • Long-term assets (PP&E, intangibles, goodwill, other)
  • Current liabilities (AP, accrued expenses, current portion of debt, other)
  • Long-term liabilities (long-term debt, deferred taxes, other)
  • Shareholders' equity (common stock, retained earnings, other)

Format:

  • Verify formula: Assets = Liabilities + Equity
  • Consistent date labeling
  • Include working capital calculation
  • Single underline above major subtotals, double underline for final totals

Tab 4: Cash Flow Statement

Purpose: Cash generation and use analysis

Contents:

  • Operating cash flow (indirect method preferred)
  • Investing cash flow (capex, acquisitions, asset sales)
  • Financing cash flow (debt issuance/repayment, equity, dividends)
  • Net change in cash
  • Beginning and ending cash balances

Format:

  • Link to income statement and balance sheet where possible
  • Show reconciliation of net income to operating cash flow
  • Clear labeling of cash uses (outflows) vs sources (inflows)

Tab 5: Operating Metrics

Purpose: Non-financial KPIs and operational data

Contents (industry-dependent):

  • Unit volumes, customer counts, locations
  • Productivity metrics (revenue per employee, per store, per unit)
  • Capacity utilization
  • Market share
  • Customer retention/churn rates
  • Industry-specific KPIs

CRITICAL FORMAT NOTE: NO dollar signs on operational metrics. These are quantities, not currency.

Format:

  • Clear units specified (customers, employees, stores, square feet, etc.)
  • Whole numbers with commas: 1,250 NOT $1,250
  • Percentages for rates: 95.0%
  • Right-align numbers

Tab 6: Property/Segment Performance (if applicable)

Purpose: Detailed breakdown by business unit, property, or segment

Contents:

  • Revenue and profitability by segment
  • Key metrics by location/product
  • Segment-specific KPIs
  • Comparative performance analysis

Format: Consistent with financial tabs for revenue/EBITDA, number format for operational metrics

Tab 7: Market Analysis

Purpose: Industry context and competitive positioning

Contents:

  • Market size and growth trends
  • Competitive landscape overview
  • Market share analysis
  • Industry benchmarks and peer comparisons
  • Regulatory environment if relevant

Format: Mix of narrative text and tables, cite sources for market data

Tab 8: Investment Highlights

Purpose: Narrative summary of key investment thesis points

Contents:

  • Detailed writeup of competitive strengths
  • Growth opportunities and strategic initiatives
  • Risk factors and mitigation strategies
  • Management assessment and track record
  • Investment thesis summary

Format: Clear headers, bullet points, concise paragraphs

STEP-BY-STEP WORKFLOW

Phase 1: Document Processing and Data Extraction

Step 1.1: Analyze source data

  • Access source materials: uploaded documents, web search for public filings, or MCP server data
  • Review data structure and identify key sections
  • Locate financial statements (typically 3-5 years historical)
  • Identify management projections if included
  • Note fiscal year end date
  • Flag any data quality issues immediately

Step 1.2: Extract financial statements

  • Locate historical income statement data
  • Extract balance sheet snapshots (year-end or quarter-end)
  • Find cash flow statement
  • Extract management projections if available
  • Note all page references for traceability

Step 1.3: Extract operating metrics

  • Identify non-financial KPIs relevant to industry
  • Capture unit economics data
  • Extract customer/location/capacity data
  • Document growth metrics and trends

Step 1.4: Extract market and industry data

  • Competitive positioning information
  • Market size and growth rates
  • Industry benchmark data
  • Peer comparison information

Step 1.5: Note key context

  • Transaction structure and rationale
  • Management team background
  • Investment highlights from source materials
  • Risk factors and considerations
  • Any data gaps or inconsistencies

Phase 2: Data Normalization and Standardization

Step 2.1: Normalize accounting presentation

  • Ensure consistent line item names across all years
  • Standardize revenue recognition treatment
  • Identify and document one-time charges
  • Create "Adjusted EBITDA" reconciliation if needed
  • Note any accounting policy changes

Step 2.2: Apply format detection logic For each data point, determine format based on full context:

  • Read tab name, table title, column header, and row label
  • Apply essential rules (see above)
  • When uncertain, examine original source document
  • Default to cleaner formatting (less is more)

Step 2.3: Identify normalization adjustments Common adjustments to document:

  • Restructuring charges (add back if truly non-recurring)
  • Stock-based compensation (add back per industry standard)
  • Acquisition-related costs (add back, specify amounts)
  • Legal settlements or litigation costs (evaluate recurrence risk)
  • Asset sales or impairments (exclude from operating results)
  • Related party adjustments (normalize to market rates) Note: Source citation format varies by data source (page numbers for documents, URLs for web sources, server references for MCP data)

Step 2.4: Create adjustment schedule For every normalization:

  • Document what was adjusted and why
  • Cite source (document page number, URL, or data source reference)
  • Quantify dollar impact by year
  • Assess recurrence risk
  • Show calculation from reported to adjusted figures

Step 2.5: Verify data integrity

  • Confirm subtotals sum correctly using formulas
  • Verify balance sheet balances
  • Check cash flow ties to balance sheet changes
  • Cross-check numbers across tabs for consistency
  • Flag any discrepancies for investigation

Phase 3: Build Excel Workbook

CRITICAL: Use xlsx skill for all Excel file manipulation. Read xlsx skill documentation before proceeding.

Step 3.1: Create standardized tab structure Create workbook with tabs:

  • Executive Summary
  • Historical Financials
  • Balance Sheet
  • Cash Flow
  • Operating Metrics
  • Property Performance (if applicable)
  • Market Analysis
  • Investment Highlights

Step 3.2: Build each tab with proper formatting Apply formatting rules systematically:

  • Headers: Bold, left-aligned, 11pt font
  • Financial data: Currency format $#,##0.0 for millions
  • Operational data: Number format #,##0 (no $)
  • Percentages: 0.0% format
  • Years: Text format to prevent comma insertion
  • Negatives: Use accounting format with parentheses
  • Underlines: Single above subtotals, double below totals

Step 3.3: Insert formulas for calculations

  • All subtotals and totals must be formula-based
  • Link balance sheet to income statement where appropriate
  • Link cash flow to both income statement and balance sheet
  • Create cross-tab references for validation
  • Avoid hardcoding any calculated values

<correct_patterns>

Row Reference Tracking - Copy This Pattern

Store row numbers when writing data, then reference them in formulas:

# ✅ CORRECT - Track row numbers as you write
revenue_row = row
write_data_row(ws, row, "Revenue", revenue_values)
row += 1

ebitda_row = row
write_data_row(ws, row, "EBITDA", ebitda_values)
row += 1

# Use stored row numbers in formulas
margin_row = row
for col in year_columns:
    cell = ws.cell(row=margin_row, column=col)
    cell.value = f"={get_column_letter(col)}{ebitda_row}/{get_column_letter(col)}{revenue_row}"

For complex models, use a dictionary:

row_refs = {
    'revenue': 5,
    'cogs': 6,
    'gross_profit': 7,
    'ebitda': 12
}

# Later in formulas
margin_formula = f"=B{row_refs['ebitda']}/B{row_refs['revenue']}"

</correct_patterns>

<common_mistakes>

WRONG: Hardcoded Row Offsets

Don't use relative offsets - they break when table structure changes:

# ❌ WRONG - Fragile offset-based references
formula = f"=B{row-15}/B{row-19}"  # What is row-15? What is row-19?

# ❌ WRONG - Magic numbers
formula = f"=B{current_row-10}*C{current_row-20}"

Why this fails:

  • Breaks silently when you add/remove rows
  • Impossible to verify correctness by reading code
  • Creates debugging nightmares in the delivered Excel file

</common_mistakes>

Step 3.4: Apply professional presentation

  • Freeze top row and first column on each data tab
  • Set appropriate column widths (typically 12-15 characters)
  • Right-align all numeric data
  • Left-align all text and headers
  • Add single/double underlines per accounting standards
  • Ensure clean, minimal appearance

Phase 4: Scenario Building (if projections included)

Management Case: Present company's projections as provided in source materials:

  • Extract all management assumptions
  • Document growth rates, margin expansion, capital requirements
  • Note key drivers and sensitivities
  • Flag any "hockey stick" inflections that require skepticism
  • Present as "Management Case" with clear labeling

Base Case (Risk-Adjusted): Apply conservative adjustments to management projections based on company-specific risk factors:

  • Apply revenue growth haircut reflecting execution risk and historical forecast accuracy
  • Moderate margin expansion assumptions based on industry benchmarks and operating leverage
  • Increase capex assumptions if growth-dependent
  • Add working capital requirements if understated
  • Delay synergy realization if applicable, based on integration complexity
  • Document all adjustments with rationale and supporting analysis

Downside Case (optional but recommended for LBO analysis): Stress

how to use datapack-builder

How to use datapack-builder on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add datapack-builder
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/financial-services-plugins --skill datapack-builder

The skills CLI fetches datapack-builder from GitHub repository anthropics/financial-services-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/datapack-builder

Reload or restart Cursor to activate datapack-builder. Access the skill through slash commands (e.g., /datapack-builder) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.572 reviews
  • Aarav Reddy· Dec 28, 2024

    We added datapack-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sofia Gupta· Dec 28, 2024

    Solid pick for teams standardizing on skills: datapack-builder is focused, and the summary matches what you get after install.

  • Charlotte Verma· Dec 28, 2024

    datapack-builder fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Mateo Kapoor· Dec 20, 2024

    Useful defaults in datapack-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Fatima Jackson· Dec 20, 2024

    We added datapack-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Carlos Choi· Dec 16, 2024

    Keeps context tight: datapack-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Liam Huang· Dec 16, 2024

    datapack-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chaitanya Patil· Dec 8, 2024

    datapack-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Piyush G· Nov 27, 2024

    Useful defaults in datapack-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Fatima White· Nov 27, 2024

    Keeps context tight: datapack-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.

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