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.
npx skills add rohasnagpal/legal-ai-skills --skill damages-quantifiergit clone --depth 1 https://github.com/rohasnagpal/legal-ai-skillsWrote 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.
[](https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/damages-quantifier)<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.
<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>- NVIDIA SkillSpector warn
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.
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.
| Model | Per session | Once 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 |
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.
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Total the claim, reconciling every head against its own workings, and state which heads are fully evidenced, partially evidenced, and unquantified.
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.
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.
- 17d ago Changed · +4 lines fe5c1ddfe286
- 1mo ago First seen · 51 lines · 155 tokens per session scan A 93e9b46e3bf1
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.
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