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exit-proof-pack

Generate an AI EBITDA proof pack from OpportunityMap JSONs into an HTML report.

Updated Apr 25, 2026
Search Tags:#sensitivity analysis#private equity#exit preparation#AI EBITDA#proof pack
One-click install
npx skills add https://github.com/bolnet/private-equity --skill exit-proof-pack
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exit-proof-pack
Source: https://github.com/bolnet/private-equity/tree/main/finance-mcp-plugin/skills/private-equity/exit-proof-pack
Command: npx skills add https://github.com/bolnet/private-equity --skill exit-proof-pack

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires json, html, json_sidecar, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill generates a comprehensive, defensible AI EBITDA proof pack for private equity firms preparing for exit, ensuring that every dollar of AI-attributable EBITDA is pre-audited and documented.

Core Features & Use Cases

  • Exit Preparation: Provides a seller-side twin for buyer-side AI diligence, offering a defensible AI EBITDA proof pack.
  • Data Traceability: Each dollar in the proof pack is traced back to a specific row in the source artifact.
  • Sensitivity Analysis: Offers conservative, base, and aggressive sensitivity analysis for robustness.
  • Defensibility Checklist: Includes a checklist for each claim to ensure defensibility against buyer challenges.

Quick Start

Generate an exit-proof pack for the 'MortgageCo' portco using the 'dx_report_MortgageCo.json' and 'bx_report_hmda_states.json' files.

Frequently Asked Questions about exit-proof-pack

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prepare a defensible AI EBITDA proof pack for private equity exit preparation?▼

To prepare a defensible AI EBITDA proof pack for private equity exit preparation, generate a seller-side HTML report and JSON sidecar using OpportunityMap JSONs to trace AI-attributable EBITDA back to source rows.

What is AI EBITDA sensitivity analysis and why is it needed for exit preparation?▼

AI EBITDA sensitivity analysis models conservative, base, and aggressive financial scenarios during exit preparation, providing robustness against buyer-side diligence challenges and ensuring every AI-attributable dollar is defensible.

How do I trace AI-attributable EBITDA claims back to source data for buyer diligence?▼

You trace AI-attributable EBITDA claims back to source data by processing OpportunityMap JSONs and optional BX corpus rollups, generating a structured ledger that maps each dollar to a specific source artifact row.

Can I use BX corpus rollups and OpportunityMap JSONs together to build an exit readiness report?▼

Yes, you can use BX corpus rollups and OpportunityMap JSONs together to build an exit readiness report, producing a structured JSON sidecar and HTML output with a defensibility checklist for each claim.

Does exit preparation require a defensibility checklist for every AI EBITDA claim?▼

Exit preparation requires a defensibility checklist for every AI EBITDA claim to ensure seller-side documentation withstands buyer-side AI diligence, verifying data traceability and claim robustness across sensitivity scenarios.

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