Agent skill

Datapack Builder

by w95 in w95/awesome-claude-corporate-skills

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers.

MITAuto-check passedBusiness, Finance & HR

Install Datapack Builder

skills CLI
$ npx skills add w95/awesome-claude-corporate-skills --skill datapack-builder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install w95/awesome-claude-corporate-skills datapack-builder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/02-finance-accounting/datapack-builder .claude/skills/datapack-builder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
datapack-builder
GitHub stars
235
Used in
1 other repo
Token cost
~6k tokens
SKILL.md length
2,824 words
Files
1
Skills in repo
43
Repo updated
First seen
Licence
MIT

At a glance

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers.

  • Works in 9 steps: Data Accuracy (Zero Tolerance for Errors) → ESSENTIAL RULES → Professional Presentation Standards → …
  • M&A due diligence
  • SKILL.md covers CRITICAL SUCCESS FACTORS, Structural Consistency, STEP-BY-STEP WORKFLOW and NORMALIZATION PATTERNS, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Datapack Builder is an agent skill from w95/awesome-claude-corporate-skills. Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations…

Its SKILL.md is about 6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Financial analysis, Excel spreadsheets and Web search. It works with Microsoft Excel, Model Context Protocol and SEC EDGAR. The repository describes itself as: 166 production-ready Claude AI skills organized by corporate role — executive leadership, finance, HR, marketing, sales, legal, operations, engineering, product, data, customer…. The licence is MIT.

When your agent uses it

  • M&A due diligence
  • Private equity analysis
  • Investment committee materials
  • Standardizing financial reporting across portfolio companies

Example prompts

  • “/datapack-builder”

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Data Accuracy (Zero Tolerance for Errors)
  2. ESSENTIAL RULES
  3. Professional Presentation Standards
  4. Document Processing and Data Extraction
  5. Data Normalization and Standardization
  6. Build Excel Workbook
  7. Scenario Building (if projections included)
  8. Quality Control and Validation
  9. Final Delivery

What it can do on your machine

Read from SKILL.md and the folder at commit 78dbc7c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Datapack Builder loads about 6k tokens when it runs. Until then it costs about 145 tokens; SKILL.md has 2,824 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~145
When it runs · the whole SKILL.md, loaded when a task matches
~6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from w95/awesome-claude-corporate-skills at commit 78dbc7c, republished under its MIT licence (© w95). 2,824 words, ~6,007 tokens.

Download SKILL.mdSave it as .claude/skills/datapack-builder/SKILL.md (or your agent's skills folder).
name
datapack-builder
description
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.

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:

python
# ✅ 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:

python
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:

python
# ❌ 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
Show full SKILL.md (1,115 more words)Show less
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 test scenario based on industry cyclicality and company vulnerabilities:

  • Model revenue decline reflecting recession risk or competitive pressure
  • Assume margin compression under stress (volume deleverage, pricing pressure)
  • Test covenant compliance and liquidity
  • Assess downside protection
  • Document key risks being stress-tested

Documentation requirements for scenarios: Create assumptions schedule showing:

  • Key assumptions by scenario (revenue growth, margins, capex %)
  • Rationale for each adjustment
  • Sensitivity analysis on key variables
  • Historical forecast accuracy if available
  • Comparison to industry benchmarks
Phase 5: Quality Control and Validation

Step 5.1: Data accuracy checks Validate:

  • Every number traces to source (check spot samples, cite documents/URLs/servers)
  • All calculations are formula-based (no hardcoded values)
  • Subtotals and totals are mathematically correct
  • Years display without commas (2024 NOT 2,024)
  • No formula errors: #REF!, #VALUE!, #DIV/0!, #N/A

Step 5.2: Format consistency checks Verify:

  • Financial data has $ signs in format
  • Operational data has NO $ signs
  • Percentages display as % (15.0% not 0.15)
  • Negative numbers use parentheses for financial data
  • Headers are bold and left-aligned
  • Numbers are right-aligned
  • Years are text format

Step 5.3: Structure and completeness checks Confirm:

  • All required tabs present and properly sequenced
  • Executive summary is concise (fits on one page)
  • All key metrics captured comprehensively
  • Logical flow from summary to detail
  • Appropriate level of granularity in each tab
  • No missing data or incomplete sections

Step 5.4: Professional presentation checks Review:

  • Minimal borders (only for structure)
  • Consistent indentation (2 spaces for sub-items)
  • Proper accounting underlines (single and double)
  • Clean, professional appearance throughout
  • Appropriate column widths (not too narrow or wide)

Step 5.5: Documentation and assumptions checks Ensure:

  • All normalization adjustments documented with rationale
  • Source citations included (document page numbers, URLs, or data source references)
  • Assumptions clearly stated and reasonable
  • Executive summary accurate and impactful
  • Filename includes company name and date
Phase 6: Final Delivery

Step 6.1: Create executive summary Write concise, impactful summary including:

  • Company overview: business model, products/services, geography (2-3 sentences)
  • Key financial metrics: Revenue, EBITDA, Growth rates (table format)
  • Investment highlights: 3-5 key strengths or opportunities
  • Notable risks or considerations (briefly)
  • Transaction context if applicable

Step 6.2: Final file preparation

  • Save workbook with proper naming: CompanyName_DataPack_YYYY-MM-DD.xlsx

NORMALIZATION PATTERNS

Common Adjustments to EBITDA

1. Restructuring charges

  • Add back if truly non-recurring (facility closure, one-time severance)
  • Do NOT add back if company restructures every year
  • Document specific nature and rationale for non-recurrence
  • Example: "2023 restructuring: $3.0M facility closure, documented in source materials, one-time event"

2. Stock-based compensation

  • Industry standard: add back for private equity analysis
  • Treat as non-cash operating expense
  • Be consistent across all periods
  • Note if unusually high or includes one-time grants

3. Acquisition-related costs

  • Add back transaction fees, integration costs
  • Document specific amounts by type
  • Do not add back ongoing integration investments
  • Cite source for each adjustment

4. Legal settlements and litigation

  • Add back if truly isolated incident
  • Assess recurrence risk (one settlement vs pattern of litigation)
  • Document nature of settlement
  • Consider if this is normal course of business

5. Asset sales or impairments

  • Exclude gains/losses on asset sales from operating EBITDA
  • Remove impairment charges if truly non-recurring
  • Document what assets were sold/impaired and why
  • Adjust revenue if assets generated operating income

6. Related party adjustments

  • Normalize above-market related party expenses (rent, management fees)
  • Adjust to market rates with supporting documentation
  • Remove personal expenses run through business
  • Document market rate comparison
Conservative vs Aggressive Normalization

Management Case:

  • Include all adjustments management proposes
  • Accept company's definition of "non-recurring"
  • More aggressive EBITDA adjustments
  • Use for understanding management's view

Base Case (Recommended for investment decisions):

  • Only clearly non-recurring items
  • Apply higher scrutiny to recurring "one-time" charges
  • Exclude speculative adjustments
  • More conservative, defensible to investment committee

INDUSTRY-SPECIFIC ADAPTATIONS

Technology/SaaS

Key metrics to capture:

  • ARR (Annual Recurring Revenue) and MRR
  • Customer count by cohort
  • CAC (Customer Acquisition Cost) and LTV (Lifetime Value)
  • Churn rate (gross and net)
  • Net revenue retention
  • Rule of 40 (Growth % + EBITDA Margin %)
  • Magic number (sales efficiency)

Format notes: ARR is currency ($), customer count is number (no $), rates are %

Manufacturing/Industrial

Key metrics to capture:

  • Production capacity and capacity utilization %
  • Units produced by product line
  • Inventory turns
  • Gross margin by product line
  • Order backlog

Format notes: Units, capacity are numbers (no $), utilization is %, revenue/costs are currency

Real Estate/Hospitality

Key metrics to capture:

  • Properties/rooms/square footage
  • Occupancy rates %
  • ADR (Average Daily Rate) - currency format
  • RevPAR (Revenue per Available Room) - currency format
  • NOI (Net Operating Income) - currency format
  • Cap rates %
  • FF&E reserve

Format notes: Rooms/sqft are numbers, occupancy is %, ADR/RevPAR are currency

Healthcare/Services

Key metrics to capture:

  • Locations/facilities
  • Providers/employees
  • Patients/visits (volume metrics)
  • Revenue per visit - currency
  • Payor mix %
  • Same-store growth %

Format notes: Locations/visits are numbers, revenue per visit is currency, rates are %

FINAL DELIVERY CHECKLIST

Complete this checklist before delivering the data pack:

Structure:

  • All required tabs present and in logical sequence
  • Each tab has clear header and title
  • Executive summary is concise (fits on one page)

Data Accuracy:

  • All numbers trace to source (documents, URLs, or data servers)
  • Source references documented for key figures (page numbers, URLs, etc.)
  • All calculations are formula-based (no hardcoded calculated values)
  • Subtotals and totals verified
  • Balance sheet balances (Assets = Liabilities + Equity)
  • No #REF!, #VALUE!, or #DIV/0! errors

Formatting - Years and Numbers:

  • Years display correctly: 2020, 2021, 2022 (no commas)
  • Financial data has $ signs: $50.0, $125.5
  • Operational metrics have NO $ signs: 100 stores, 250 employees
  • Percentages formatted correctly: 15.0%, 25.5%
  • Negatives in parentheses: $(15.0) not -$15.0

Formatting - Professional Standards:

  • Headers bold and left-aligned
  • Numbers right-aligned
  • Consistent indentation (2 spaces for sub-items)
  • Single underline above subtotals
  • Double underline below final totals
  • Frozen panes on headers
  • Consistent font throughout
  • Minimal borders (only for structure)
  • Clean, professional appearance throughout

Content Completeness:

  • Financial statements complete (IS, BS, CF)
  • Operating metrics comprehensively captured
  • Normalization adjustments documented
  • Assumptions clearly stated
  • Executive summary clear, concise, and impactful
  • Investment highlights compelling
  • Market analysis provides context

Documentation:

  • All normalization adjustments explained
  • Every data cell cited from source with comments and links (document page numbers, URLs, or data source references)
  • Assumptions documented with rationale
  • Any data limitations noted
  • Filename follows convention: CompanyName_DataPack_YYYY-MM-DD.xlsx

Final Output:

  • File saved to outputs with proper naming convention
  • All quality control checks passed

© w95, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in 02-finance-accounting/datapack-builder of w95/awesome-claude-corporate-skills.

Open the folder on GitHubat commit 78dbc7c

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in w95/awesome-claude-corporate-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Datapack Builder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Datapack Builder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Datapack Builder this skillw95/awesome-claude-corporate-skills2351 repos~6kAutomated safety check: PassMIT
Dcf ModelWind-Alice/AliceMarket1283 repos~12kAutomated safety check: PassNone
Sec Footnotes AnalysisOctagonAI/skills127—~1.9kAutomated safety check: PassMIT
MCP Gatewaytmustier/pi-for-excel434—~241Automated safety check: PassMIT
Bilig Workpapersickn33/agentic-awesome-skills47k1 repos~1.3kAutomated safety check: PassMIT
Officecli Document Workflowskuramatata/my-pi-agent114—~1kAutomated safety check: PassNone

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More from w95/awesome-claude-corporate-skills

All 43 skills in this repo
  • Data Context Extractor

    w95/awesome-claude-corporate-skills

    Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts.

    235 GitHub starsUsed in 1 repo~1.8k tokens
    Auto-check passed
  • Competitive Analysis

    w95/awesome-claude-corporate-skills

    Framework for competitive landscape analysis across any industry.

    235 GitHub starsUsed in 1 repo~4k tokens
    Auto-check passed
  • Account Research

    w95/awesome-claude-corporate-skills

    Research a company using Common Room data. An agent skill from w95/awesome-claude-corporate-skills.

    235 GitHub stars~1.5k tokensUpdated 7 mo ago
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  • Call Prep

    w95/awesome-claude-corporate-skills

    Prepare for a customer or prospect call using Common Room signals.

    235 GitHub stars~1.5k tokensUpdated 7 mo ago
    Auto-check passed
  • Compose Outreach

    w95/awesome-claude-corporate-skills

    Generate personalized outreach messages using Common Room signals.

    235 GitHub stars~1.4k tokensUpdated 7 mo ago
    Auto-check passed
  • SQL Queries

    w95/awesome-claude-corporate-skills

    Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.).

    235 GitHub starsUsed in 3 repos~2.8k tokens
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Questions about Datapack Builder

What does Datapack Builder do?

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Datapack Builder is an agent skill from w95/awesome-claude-corporate-skills. Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers.

When should I use Datapack Builder?

Datapack Builder fits situations like: M&A due diligence; private equity analysis; investment committee materials; standardizing financial reporting across portfolio companies.

How do I install Datapack Builder in Claude Code?

Run `npx skills add w95/awesome-claude-corporate-skills --skill datapack-builder -a claude-code`. Or copy the skill folder (02-finance-accounting/datapack-builder in w95/awesome-claude-corporate-skills) into .claude/skills/datapack-builder in your project. Claude Code loads it when a task matches its description.

How do I install Datapack Builder in Codex?

Run `npx skills add w95/awesome-claude-corporate-skills --skill datapack-builder -a codex`. Or copy the skill folder (02-finance-accounting/datapack-builder in w95/awesome-claude-corporate-skills) into .agents/skills/datapack-builder in your project. Codex loads it when a task matches its description.

Can I use Datapack Builder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add w95/awesome-claude-corporate-skills --skill datapack-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/datapack-builder, .gemini/skills/datapack-builder, .github/skills/datapack-builder and .opencode/skills/datapack-builder in your project.

What does Datapack Builder need to run?

SKILL.md names no scripts, command-line tools or credentials: Datapack Builder is instructions for the agent only.

Does Datapack Builder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Datapack Builder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Datapack Builder use?

Datapack Builder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Datapack Builder use?

About 6k tokens (SKILL.md is roughly 24k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Datapack Builder?

Skills that share tags, products or a category with Datapack Builder: Dcf Model (Wind-Alice/AliceMarket, 128 stars), Sec Footnotes Analysis (OctagonAI/skills, 127 stars), MCP Gateway (tmustier/pi-for-excel, 434 stars) and Bilig Workpaper (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Datapack Builder?

w95 (a GitHub user) maintains it in w95/awesome-claude-corporate-skills, which has 235 GitHub stars. The repository holds 43 skills in this directory. The repository was last updated on February 26, 2026.

Source: w95/awesome-claude-corporate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.