skills/capital/modeling-venture-fund-economics/SKILL.md
Builds LP-level fund models with management fees, carried interest, clawback provisions, and waterfall distributions. Use when modeling fund economics, projecting LP returns, or analyzing fund terms.
npx skillsauth add casemark/skills modeling-venture-fund-economicsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Builds LP-level fund models projecting LP net returns through management fees, carried interest waterfalls, clawback mechanics, and portfolio-level cash flow timing.
Map the fee schedule — Calculate annual management fees over the full fund life. Model the fee basis shift (committed capital during investment period → invested capital or NAV post-investment period). Apply any fee offsets from portfolio company monitoring/transaction fees. Compute total fee load as a percentage of committed capital.
Build the deployment schedule — Lay out capital calls by quarter or year across the investment period. Allocate between new investments and follow-on reserves. Track invested capital, unfunded commitments, and recycled capital at each period.
Model portfolio outcomes — Assign gross return multiples and exit timing to each investment (or investment cohort). For early-stage VC, apply a power-law distribution: ~50–65% write-offs/minimal returns, ~20–30% moderate (1–3x), ~5–10% outsized (5x+). Calculate gross proceeds per exit event.
Run the waterfall — Apply the distribution waterfall per LPA terms:
Calculate clawback exposure — Model scenarios where early profitable exits generate carry, but later write-offs reduce aggregate fund returns below the hurdle. Quantify the GP's clawback obligation. Note whether the clawback is net-of-tax [VERIFY: confirm clawback tax gross-up provisions in LPA].
Compute LP net metrics — Calculate net MOIC, net IRR (using actual cash flow timing), DPI (distributions to paid-in), RVPI (residual value to paid-in), and TVPI at key intervals (end of investment period, year 7, year 10, final liquidation).
Run sensitivity analysis — Vary key assumptions across a matrix:
development
name: automated-contract-summary language: en description: Generates structured executive summaries of contracts using ML — captures key terms, party obligations, risk allocations, and compliance requirements in a standardized format. Optimized for high-volume review where speed and consistency matter. tags: - summarization - agreement - corporate --- # Automated Contract Summarization Produces standardized executive summaries of contracts using machine learning, capturing essential term
tools
Extracts regulatory obligations from dense regulations across jurisdictions. Breaks down multi-level regulations into clear article-level obligations, classifies applicability to a business, and prioritizes by risk level. Use when translating regulations into actionable compliance requirements.
development
Continuously monitors regulatory landscapes for changes relevant to a specific business. Ingests global regulatory updates, filters by relevance, summarizes impact, and produces an actionable change advisory. Use when tracking regulatory developments affecting a particular product or market.
testing
Compares an organization's existing compliance controls, policies, and procedures against extracted regulatory obligations to identify coverage gaps. Produces a remediation plan with prioritized actions. Use when assessing compliance maturity or preparing for regulatory audits.