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gp-track-record-parser

Computes GP track record metrics like DPI, TVRI, IRR from raw cash flows with Python.

Updated May 15, 2026
Search Tags:#benchmarking#financial analysis#due diligence#cash flow analysis#track record analysis#GP diligence
One-click install
npx skills add https://github.com/TerminalGambit/fof-impact-nature-skills --skill gp-track-record-parser
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gp-track-record-parser
Source: https://github.com/TerminalGambit/fof-impact-nature-skills/tree/main/gp-track-record-parser
Command: npx skills add https://github.com/TerminalGambit/fof-impact-nature-skills --skill gp-track-record-parser

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides in-depth analysis of a General Partner's (GP) track record, using raw cash flow data to compute and interpret key metrics like DPI, TVPI, RVPI, net IRR, MOIC, PME, and more.

Core Features & Use Cases

  • Cash Flow Analysis: Compute and interpret DPI, TVPI, RVPI, net IRR, MOIC, PME, and other metrics from raw cash flow data.
  • J-Curve Analysis: Analyze the J-curve shape of cumulative net cash flow over time.
  • Loss Ratio Analysis: Calculate and interpret loss ratios for deals and capital weighted loss ratios.
  • Concentration of Returns: Analyze the contribution of top deals to the fund's gross gain and TVPI.
  • Stale-Mark Detection: Run six concrete tests to detect stale-mark signals in the GP's track record.
  • Vintage Benchmarking: Compare the GP's performance against vintage benchmarks from Cambridge Associates, Preqin, Burgiss, MSCI, and others.
  • Re-Up Reasoning: Provide a framework for evaluating whether to re-up a GP based on track record, process, and strategy.

Quick Start

Use the gp-track-record-parser skill to analyze the cash flow data for a GP's prior fund and generate a track record memo.

Frequently Asked Questions about gp-track-record-parser

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

FAQPage Schema
How do I analyze GP track records using raw cash flow data?▼

Analyzing GP track records requires processing raw cash flow data to compute DPI, TVPI, RVPI, net IRR, MOIC, and PME. This Skill applies Python to parse contributions and distributions, generating an investment memo for LP due diligence.

What is stale-mark detection in GP track record analysis?▼

Stale-mark detection identifies outdated net asset values in a GP's track record by running six concrete tests on the raw cash flow data. This helps LPs uncover artificially inflated TVPI or RVPI metrics during due diligence.

How do I benchmark private equity fund performance by vintage year?▼

Benchmarking fund performance by vintage year compares a GP's net IRR and TVPI against datasets from Cambridge Associates, Preqin, Burgiss, and MSCI. This Skill structures the raw cash flow data to enable direct vintage benchmarking.

Can I calculate capital weighted loss ratios and concentration of returns with pandas and numpy?▼

Yes, calculating capital weighted loss ratios and concentration of returns relies on pandas and numpy to process raw cash flows. The Skill quantifies how top deals drive gross gain and evaluates downside risk for GP diligence.

What is J-curve analysis in private equity cash flow analysis?▼

J-curve analysis maps the cumulative net cash flow of a private equity fund over time, showing early negative returns before distributions peak. This Skill processes raw cash flows to visualize the J-curve shape for GP diligence.

How do I evaluate whether to re-up a GP based on their track record?▼

Evaluating a GP re-up involves analyzing their prior fund's track record, investment process, and strategy. This Skill provides a structured framework to interpret cash flow metrics and concentration of returns for re-up reasoning.

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