damages-quantifier

damages-quantifier is a skill for Claude Code, Codex from rohasnagpal/legal-ai-skills. It costs 155 tokens per session (1,226 once invoked), scanned A, original, MIT.

A method for calculating a civil or commercial damages claim by separate types of loss, such as lost profits, wasted spending, and interest.

In plain words
What is it for?
Use it to calculate expectation loss, reliance loss, lost profits, interest, and adjustments for steps taken to reduce the loss.
Why use it?
It shows how each figure was calculated and exposes missing evidence without filling gaps with guessed rates, profits, or mitigation amounts.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the legal-ai-skills plugin — 175 skills shipped together

Good fit Use it to calculate expectation loss, reliance loss, lost profits, interest, and adjustments for steps taken to reduce the loss.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rohasnagpal/legal-ai-skills/damages-quantifier
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add rohasnagpal/legal-ai-skills --skill damages-quantifier
Clone the repo
git clone --depth 1 https://github.com/rohasnagpal/legal-ai-skills

Made for: Claude Code, Codex.

Or install legal-ai-skills, the plugin that ships this one along with the rest of its 175 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for damages-quantifier

README.md
[![agentmods](https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/damages-quantifier/github.svg)](https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/damages-quantifier)
Your own site
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/damages-quantifier"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/damages-quantifier/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for damages-quantifier

Your own site · 80×15
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/damages-quantifier"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/damages-quantifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 155 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,226 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 47
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00155 $0.01226
Opus 5.5 $0.00062 $0.00490
Sonnet 5.5 $0.00031 $0.00245
Haiku 4.5 $0.00015 $0.00123

Measured 17d ago against content hash fe5c1ddfe286, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

damages-quantifier scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 17d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/legal-ai-skills/skills/damages-quantifier/SKILL.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Damages Quantifier

I am using the Damages Quantifier skill from Rohas Legal AI: general commercial/civil damages head by head — expectation, reliance, lost profits, interest, mitigation. Say this sentence, verbatim, before anything else in your response.

Purpose

Build the quantum of a civil or commercial damages claim head by head, showing the arithmetic for every figure, using only what the user actually supplies — never inventing a rate, a profit margin, or a mitigation figure to fill a gap.

Required inputs

Obtain the basis of the claim (breach of contract, tort, or other cause of action — as instructed, not independently established by this skill), the heads of loss claimed, the figures supporting each head (invoices, financial statements, projections, expenditure records), the date loss accrued or the breach occurred, any interest basis claimed, and any mitigation steps taken or losses avoided.

Ask which measure of damages is being claimed — expectation (the position if the contract had been performed), reliance (wasted expenditure in reliance on the contract), or another recognised measure — since the two are not interchangeable and mixing them without saying so double-counts or under-claims.

Treat the actual supporting figures as blocking for any head claimed. Do not quantify a head of loss for which no figure has been supplied — list it as claimed but unquantified instead.

Method

  1. Classify the measure of damages being claimed for each head — expectation, reliance, or another recognised measure — in one line before building the numbers. Flag if the user has not specified this and proceed on the measure that best fits the facts given, stating the assumption.
  2. Build each head of loss separately: state the formula, the inputs, the source of each input, and the resulting figure. Never present a total without showing the components that produce it.
  3. For lost profits specifically, distinguish gross revenue lost from net profit lost — the recoverable measure is ordinarily net profit, after deducting costs that would have been incurred to earn that revenue. Do not present a lost-revenue figure as though it were lost profit without confirming which is claimed.
  4. For wasted expenditure, list each item of expenditure claimed, its date, its source document, and whether it has any residual value that should be credited against the claim.
  5. Apply interest only using the basis actually supplied — a contractual interest rate, a statutory rate, or a rate the user specifies. Do not assume a specific statutory interest rate or default commercial rate from memory; flag the applicable rate as a verification point where not supplied.
  6. Apply mitigation: identify any loss the claimant avoided or could reasonably have avoided, and deduct it from the relevant head, showing the deduction and its source. Do not assume mitigation occurred or was required without a fact basis; flag mitigation as an open question where the facts are silent.
  7. Check for double recovery across heads — the same loss claimed twice under different labels (for example, lost profits and wasted expenditure covering the same period without an offset) — and flag any overlap found.
  8. Total the claim, reconciling every head against its own workings, and state which heads are fully evidenced, partially evidenced, and unquantified.

Read the full file on GitHub · 55 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 17d ago Changed · +4 lines fe5c1ddfe286
  2. 1mo ago First seen · 51 lines · 155 tokens per session scan A 93e9b46e3bf1

Subscribe to this mod's changes

damages-quantifier is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (193 stars, last pushed 17d ago), licensed MIT. It adds 155 tokens to every session and 1,226 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

fintech-agreement-drafting-stephane-boghossian

An end-to-end method for drafting and finalising a complex, multi-pillar regulated fintech agreement — from intake to signature. Authored from a senior fintech lawyer's manual: a licensed payment-services provider engaging a counterparty across agent cash-in/cash-out, QR payments, wallet e-payments, and a marketplace…

lawve-ai/awesome-legal-skills · 205 tokens

billing-cycle-manager-scott-margetts

Operational billing execution for legal matters. Monthly bill prep and billing instructions, LC invoice review and disbursement treatment, client billing query responses, cashflow modelling (LC payment obligations vs client receipts), and leverage and burn analysis (staffing mix, predicted total cost, margin…

lawve-ai/awesome-legal-skills · 162 tokens

budget-and-fee-manager-scott-margetts

Matter budgeting and ongoing WIP/variance monitoring. Build phase-based fee estimates at matter setup, run bottom-up budgets by jurisdiction or workstream, calculate contingency, and structure AFA arrangements (fixed fee, capped fee, phased fixed fees). Ongoing monitoring: WIP tracking against budget, proportionality…

lawve-ai/awesome-legal-skills · 205 tokens

indian-foreign-investment-approval-assessment-siddhi-kudalkar

Assess whether Government of India approval is required for foreign investment in an Indian company under Foreign Exchange Management Act, 1999 and the Non-Debt Instruments Rules, 2019. The skill systematically gathers transaction details, evaluates sectoral entry routes and caps, assesses land border country…

lawve-ai/awesome-legal-skills · 103 tokens

new-designation-screening-test

Generate a spreadsheet of test entries — newly designated names from OFAC, OFSI, and EU sanctions lists plus deliberate variations of those names — to validate that a sanctions screening system catches fresh designations and is tuned to the right fuzziness threshold. Use this whenever the user asks for sanctions list…

lawve-ai/awesome-legal-skills · 146 tokens

billable-time-stephane-boghossian

When your bar comes asking "show me how you billed AI-assisted work" — and ABA 512, Florida 24-1, California, New York, and DC all have opinions out — you need an artifact that survives review. billable-time produces it. From your Claude Code session logs, it drafts reviewable time entries plus a printable HTML audit…

lawve-ai/awesome-legal-skills · 219 tokens