skills/capital/pricing-lp-interest-portfolios/SKILL.md
Evaluates LP interest portfolios with fund-by-fund NAV assessment, J-curve positioning, and portfolio-level pricing methodology. Use when pricing secondary portfolios, evaluating LP interest bids, or analyzing fund vintages.
npx skillsauth add casemark/skills pricing-lp-interest-portfoliosInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Evaluates LP interest portfolios with fund-by-fund NAV assessment, J-curve positioning, and portfolio-level pricing methodology.
Organize the portfolio into fund-level line items. For each fund, record: GP name, vintage, strategy, fund size, LP commitment, called %, NAV as of reporting date, unfunded commitment, DPI, TVPI, and RVPI.
Classify each fund by J-curve position.
Apply strategy-specific discount/premium adjustments.
Price unfunded commitments separately. Unfunded obligations represent a future capital call liability. Value them as: Unfunded × (1 − expected loss ratio) discounted at buyer's required return, or apply a fixed cent-on-the-dollar rate consistent with market convention (often 0–5% cost for high-quality GPs, higher for lower-conviction names).
Calculate fund-level bid prices. For each fund: Bid Price = (NAV × Discount/Premium Factor) − (Unfunded Commitment Cost). Express as % of NAV and as absolute dollar value.
Aggregate to portfolio-level pricing.
Run sensitivity analysis. Stress-test portfolio pricing across:
Produce a Portfolio Pricing Summary containing:
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.