legal-claim-economics

Model litigation claim and portfolio economics with auditable assumptions registers.

46|25|Updated May 7, 2026
One-click install
npx skills add https://github.com/LegalQuants/lq-skills --skill legal-claim-economics
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: legal-claim-economics
Source: https://github.com/LegalQuants/lq-skills/tree/main/skills/legal-claim-economics
Command: npx skills add https://github.com/LegalQuants/lq-skills --skill legal-claim-economics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Legal teams and funders often need to quantify whether a claim or portfolio is economically viable across damages, costs, fee structures, funding returns, and recovery waterfalls—without producing unsupported conclusions or hidden assumptions.

Core Features & Use Cases

  • Claim/portfolio economics modelling: Build base, downside, and upside scenarios from user inputs covering damages, win probability, time to resolution, costs, ATE/adverse costs, and recovery waterfall.
  • Fee and funding structure handling: Model solicitor fee arrangements (including DBA/CFA-style structures) and funding return families such as MOIC, proceeds splits, structured debt interest, IRR hurdles, and hybrids.
  • Transparent assumptions and safety guardrails: Maintain an assumptions register with explicit sourcing labels (user-supplied, document-sourced, calculated, or assumptions-to-verify), and avoid fabricating Monte Carlo percentiles or legal/tax enforceability conclusions.

Quick Start

Use legal-claim-economics to model 1,000 claims with provided damages, win probability, costs, ATE assumptions, and a funder MOIC, then output the firm and funder economics plus the client residual using a specified recovery waterfall.

Frequently Asked Questions about legal-claim-economics

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

FAQPage Schema
How do I model litigation funding economics and recovery waterfalls?

Build base, downside, and upside scenarios from user-supplied damages, win probability, costs, ATE assumptions, and funder return structures to compute deterministic outputs for firm, funder, and client residuals via a specified recovery waterfall.

What funding return structures can I calculate for a litigation claim portfolio?

Calculate funding return structures including MOIC, proceeds splits, structured debt interest, IRR hurdles, and hybrid models, alongside solicitor fee arrangements like DBA or CFA-style structures across claim portfolio scenarios.

Can I use incomplete inputs to run scenario analysis for litigation funding viability?

Yes, run scenario analysis with incomplete inputs using specification_mode, producing an auditable assumptions register with labeled input provenance for user-supplied, document-sourced, calculated, or assumptions-to-verify data.

Does this approach generate Monte Carlo percentiles for claim economics?

No, it avoids fabricating Monte Carlo percentiles or legal and tax enforceability conclusions without runnable inputs, focusing instead on deterministic outputs and transparent assumptions registers for claim and portfolio economics.

How are adverse costs and ATE insurance handled in claim portfolio economics modelling?

Adverse costs and ATE insurance assumptions are incorporated as user-supplied inputs within base, downside, and upside scenarios to accurately model litigation funding viability and recovery waterfall distributions across the claim portfolio.