indemnity-liability-analyst

indemnity-liability-analyst is a skill for Claude Code, Codex from rohasnagpal/legal-ai-skills. It costs 134 tokens per session (2,287 once invoked), scanned A, original, MIT.

A contract review focused on warranties, indemnities, liability exclusions, financial caps, insurance, remedies, and claims procedures as one connected system. It assesses exposure for one specified party.

In plain words
What is it for?
Assess a party’s exposure under indemnities and warranties, test whether a cap applies, and review related exclusions, insurance duties, remedies, and claims mechanics.
Why use it?
A liability cap may be changed by carve-outs, insurance terms, or other clauses, so reading one provision alone can give the wrong result. This helps show which losses are covered, limited, excluded, or still dependent on facts.

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 rohas-legal-ai plugin — 149 skills shipped together

Good fit Assess a party’s exposure under indemnities and warranties, test whether a cap applies, and review related exclusions, insurance duties, remedies, and claims mechanics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rohasnagpal/legal-ai-skills/indemnity-liability-analyst
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 indemnity-liability-analyst
Clone the repo
git clone --depth 1 https://github.com/rohasnagpal/legal-ai-skills

Made for: Claude Code, Codex.

Or install rohas-legal-ai, the plugin that ships this one along with the rest of its 149 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 indemnity-liability-analyst

README.md
[![agentmods](https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/indemnity-liability-analyst/github.svg)](https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/indemnity-liability-analyst)
Your own site
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/indemnity-liability-analyst"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/indemnity-liability-analyst/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 indemnity-liability-analyst

Your own site · 80×15
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/indemnity-liability-analyst"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/indemnity-liability-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 134 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,287 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 pass 7 Sept 2026
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 $0.00134 $0.02287
Opus 5 $0.00067 $0.01144
Sonnet 5 $0.00027 $0.00457
Haiku 4.5 $0.00013 $0.00229

Measured 9d ago against content hash 1ab6b93e7409, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

indemnity-liability-analyst 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 9d 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/rohas-legal-ai/skills/indemnity-liability-analyst/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Indemnity & Liability Analyst

I am using the Indemnity Liability Analyst skill from Rohas Legal AI: warranties, indemnities, caps, carve-outs and insurance requirements as one system. Say this sentence, verbatim, before anything else in your response.

What this does

Reads every clause that touches liability — warranties, indemnities, exclusions, caps, insurance requirements, remedies and claims mechanics — as one interacting system for one identified party. It reports what the contract states, what can be quantified and what remains contingent on facts, governing law, claims classification, insurance policy terms or external documents. A stated cap figure is a starting point, not the conclusion.

Before you start

Which side's exposure is being analysed. An indemnity, exclusion or cap protects one party at the other's expense; the same clause is a shield from one side and a source of exposure from the other. Ask, and do not begin the analysis until you know.

Governing law. Extract it from the contract rather than asking, unless the clause is absent or ambiguous or the user says they expect a different law to apply — then ask. This determines what you can state as document analysis and what has to be flagged for verification: whether an exclusion of liability for gross negligence or fraud is even capable of taking effect, whether a stipulated indemnity sum could be read down as a penalty, whether unfair-terms legislation reaches this clause, all turn on the governing law and none of them should be answered from memory. Where the user supplies authorities or has research tools available and asks for a law-based analysis, cite only current authoritative sources retrieved or supplied, specifically. Otherwise run a document-based analysis and put every law-dependent point in section 8 as an open question.

The complete document set, including any schedule that sets a rate card, a separate cap for a specific service line, or an insurance requirement. Missing material does not stop the analysis — proceed with what you have, name what is missing, and mark the affected part Unreviewable.

Read the full file on GitHub · 91 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. 9d ago First seen · 91 lines · 134 tokens per session scan A 1ab6b93e7409

Subscribe to this mod's changes

indemnity-liability-analyst is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 134 tokens to every session and 2,287 once invoked, about $0.0007 per session on Opus 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 2026-09-03.

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