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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondatapack-builderExecute the skills CLI command in your project's root directory to begin installation:
Fetches datapack-builder from anthropics/financial-services-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate datapack-builder. Access via /datapack-builder in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
0
total installs
0
this week
7.3K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
7.3K
stars
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.
Every data pack must achieve these standards. Failure on any point makes the deliverable unusable.
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.
Formatting Standards:
Color Scheme - Two Layers:
Layer 1: Font Colors (MANDATORY from xlsx skill)
Layer 2: Fill Colors (Optional for enhanced presentation)
How the layers work together (if fill colors are used):
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:
Never include:
Use the standard 8-tab structure unless explicitly instructed otherwise:
Purpose: One-page overview for busy executives
Contents:
Format: Clean, bold headers, minimal decoration, critical numbers emphasized
Purpose: Complete profit and loss history
Contents:
Format:
Purpose: Financial position at period end
Contents:
Format:
Purpose: Cash generation and use analysis
Contents:
Format:
Purpose: Non-financial KPIs and operational data
Contents (industry-dependent):
CRITICAL FORMAT NOTE: NO dollar signs on operational metrics. These are quantities, not currency.
Format:
Purpose: Detailed breakdown by business unit, property, or segment
Contents:
Format: Consistent with financial tabs for revenue/EBITDA, number format for operational metrics
Purpose: Industry context and competitive positioning
Contents:
Format: Mix of narrative text and tables, cite sources for market data
Purpose: Narrative summary of key investment thesis points
Contents:
Format: Clear headers, bullet points, concise paragraphs
Step 1.1: Analyze source data
Step 1.2: Extract financial statements
Step 1.3: Extract operating metrics
Step 1.4: Extract market and industry data
Step 1.5: Note key context
Step 2.1: Normalize accounting presentation
Step 2.2: Apply format detection logic For each data point, determine format based on full context:
Step 2.3: Identify normalization adjustments Common adjustments to document:
Step 2.4: Create adjustment schedule For every normalization:
Step 2.5: Verify data integrity
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:
Step 3.2: Build each tab with proper formatting Apply formatting rules systematically:
Step 3.3: Insert formulas for calculations
<correct_patterns>
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>
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:
</common_mistakes>
Step 3.4: Apply professional presentation
Management Case: Present company's projections as provided in source materials:
Base Case (Risk-Adjusted): Apply conservative adjustments to management projections based on company-specific risk factors:
Downside Case (optional but recommended for LBO analysis): Stress
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
anthropics/claude-code
mblode/agent-skills
github/awesome-copilot
sickn33/antigravity-awesome-skills
leonxlnx/taste-skill
erichowens/some_claude_skills
We added datapack-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: datapack-builder is focused, and the summary matches what you get after install.
datapack-builder fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in datapack-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added datapack-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: datapack-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.
datapack-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
datapack-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in datapack-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: datapack-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.
showing 1-10 of 72