modeling-fund-economics-sensitivity
Builds fund economic models with sensitivity across deployment pace, exit multiples, and fee/carry structures for LP and GP returns. Use when modeling fund economics, projecting LP net returns, or analyzing fee-adjusted performance.
Best use case
modeling-fund-economics-sensitivity is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Builds fund economic models with sensitivity across deployment pace, exit multiples, and fee/carry structures for LP and GP returns. Use when modeling fund economics, projecting LP net returns, or analyzing fee-adjusted performance.
Teams using modeling-fund-economics-sensitivity should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/modeling-fund-economics-sensitivity/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How modeling-fund-economics-sensitivity Compares
| Feature / Agent | modeling-fund-economics-sensitivity | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Builds fund economic models with sensitivity across deployment pace, exit multiples, and fee/carry structures for LP and GP returns. Use when modeling fund economics, projecting LP net returns, or analyzing fee-adjusted performance.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Modeling Fund Economics Sensitivity Builds fund economic models with sensitivity analysis across deployment pace, exit multiples, and fee/carry structures to project LP net returns and GP economics. ## When To Use - Modeling projected LP net IRR and net TVPI for a new fund's PPM or marketing materials - Comparing fee/carry structures during LPA negotiations (e.g., 2/20 vs. 1.5/20 with catch-up variations) - Stress-testing fund returns under different deployment and exit timing scenarios - Evaluating GP economics (carried interest, management fee revenue) across fund life - Presenting sensitivity tables to an LP advisory committee or investment committee ## Inputs To Gather - **Fund parameters**: target fund size, GP commitment percentage, fund term (investment period + harvest period), extension options - **Fee structure**: management fee rate and basis (committed vs. invested capital), fee step-down timing and rate, organizational expense cap, fund expense budget - **Carry structure**: carried interest percentage, preferred return (hurdle rate), catch-up split and rate (e.g., 80/20 catch-up to 20%), whole-fund vs. deal-by-deal waterfall, clawback provisions - **Deployment assumptions**: number of investments, average check size, deployment pace (e.g., 3–5 year investment period), recycling percentage of returned capital - **Exit assumptions**: target gross MOIC range (e.g., 1.5x–3.0x), holding period distribution (3–7 years), exit timing curve (early realizations vs. back-loaded) - **Sensitivity ranges**: define low/base/high cases for each key variable ## Workflow 1. **Build the capital call schedule** - Model LP capital calls based on deployment pace assumptions (front-loaded, even, or J-curve profile) - Layer in management fees drawn from commitments (or net against invested capital, depending on LPA terms) - Account for GP co-investment and any fee offsets (e.g., portfolio company monitoring fees credited against management fees) 2. **Model the portfolio and exit cash flows** - Project gross investment returns for each scenario using target MOIC and holding period - Distribute exits across the harvest period — apply a realization curve rather than assuming a single exit date - If recycling is permitted, model reinvestment of early proceeds within the investment period [VERIFY: confirm recycling cap in LPA, typically 100–125% of commitments] 3. **Apply the waterfall distribution** - Calculate return of capital, preferred return accrual, catch-up allocation, and residual carried interest split - For whole-fund waterfalls: aggregate all proceeds before splitting carry; track cumulative preferred return threshold - For deal-by-deal waterfalls: compute carry on each realization separately; model escrow/holdback for clawback protection [VERIFY: escrow percentage — commonly 20–30% of carry distributions] - Compute net distributions to LPs after carry and expenses 4. **Calculate return metrics** - **LP net IRR**: time-weighted return on LP cash flows (calls in, distributions out) net of all fees and carry - **LP net TVPI**: total value to paid-in capital (distributions + remaining NAV / total called capital) - **LP DPI**: distributions to paid-in (realized returns only) - **GP carry**: total carried interest dollars and as a multiple of GP commitment - **GP management fee revenue**: cumulative fees over fund life, before and after step-down 5. **Run sensitivity analysis** - Build a matrix varying **exit multiple** (rows) against **deployment pace** (columns) for LP net IRR and net TVPI - Run a second matrix varying **fee/carry structure** against **exit multiple** for LP net returns - Test specific scenarios: (a) rapid deployment with lower multiples, (b) slow deployment with higher multiples, (c) early realizations enabling recycling - Highlight breakeven exit multiple where LP net IRR equals the preferred return hurdle 6. **Stress-test edge cases** - Model a loss scenario (0.5x–0.8x gross MOIC) to show LP downside and confirm no carry is distributed - Test the impact of fund extensions (1–2 years) on IRR drag from continued fee payments - Verify clawback triggers under deal-by-deal waterfalls with mixed winner/loser outcomes ## Output - **Summary table**: base case LP net IRR, net TVPI, DPI, and GP carry for the primary scenario - **Sensitivity matrices**: 2–3 tables showing LP net IRR and net TVPI across variable combinations - **Cash flow schedule**: annual summary of calls, distributions, net cash flow, and cumulative metrics - **Fee analysis**: total management fees, fee offsets, organizational expenses, and net fee load as percentage of committed capital - **GP economics summary**: carry dollars by scenario, management fee revenue, and total GP compensation - **Assumptions register**: all inputs clearly stated with sources, including any [VERIFY] flags for terms pending LPA finalization ## Quality Checks - Confirm that LP net IRR is always lower than gross IRR — if not, the fee/carry layer is misapplied - Verify that at the preferred return hurdle, zero carry is distributed (waterfall integrity check) - Ensure capital calls never exceed total commitments (unless recycling is modeled and within permitted limits) - Cross-check that LP net TVPI = (total distributions + remaining NAV) / total called capital — arithmetic consistency - Validate that management fee step-down timing matches LPA terms (commonly steps down from committed to invested capital basis after investment period) [VERIFY: confirm step-down trigger and rate] - Compare modeled J-curve profile against industry benchmarks for the fund's strategy (e.g., buyout funds typically show positive net cash flow by years 5–6) - Flag any scenario where GP carry exceeds 30% of total fund profits as unusual and warranting review