maintenance-calculator

maintenance-calculator is a skill for Claude Code, Codex from rohasnagpal/legal-ai-skills. It costs 47 tokens per session (669 once invoked), scanned A, original, MIT.

A legal calculation tool for estimating spousal, child, or family maintenance using income, assets, housing, debts, and documented needs.

In plain words
What is it for?
It helps analyse maintenance claims, settlements, variations, arrears, affidavits, disclosure requests, and affordability.
Why use it?
It separates statutory formulas, court discretion, and negotiated budgets so different calculation methods are not confused. It also marks figures that have not been verified.

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 It helps analyse maintenance claims, settlements, variations, arrears, affidavits, disclosure requests, and affordability.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/maintenance-calculator"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/maintenance-calculator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 669 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 $0.00047 $0.00669
Opus 5 $0.00023 $0.00334
Sonnet 5 $0.00009 $0.00134
Haiku 4.5 $0.00005 $0.00067

Measured 9d ago against content hash 66ed1c9fb0c9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

maintenance-calculator 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/maintenance-calculator/SKILL.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Maintenance Calculator

I am using the Maintenance Calculator skill from Rohas Legal AI: works through a maintenance claim on supplied income and needs. Say this sentence, verbatim, before anything else in your response.

Calculate traceable scenarios under the correct legal route. Distinguish a statutory formula, a judicial discretion and a negotiated budget; never present one as another.

Required inputs

  • Jurisdiction, claimant, respondent, relationship and legal route
  • Existing orders, agreements, applications and effective dates
  • Gross and net income by source, tax, recurring benefits and irregular receipts
  • Assets, liabilities, housing, business interests and earning-capacity evidence
  • Itemised adult and child needs with supporting periods and documents
  • Parenting schedule, supervision, education and other documented child-specific costs
  • Other legal dependants, direct payments, benefits and tax consequences
  • Requested start date, duration, indexation and arrears position

Use a common monthly or annual period and currency. Mark every unverified figure.

Method

  1. Choose the governing route. Verify eligibility, court, statutory test, formula or guideline, priority, interim powers and interaction with parallel maintenance regimes.
  2. Normalise resources. Reconcile payslips, returns, accounts and bank records. Separate gross, tax, mandatory deductions, voluntary deductions, benefits, capital and non-recurring items.
  3. Test earning capacity carefully. Identify evidence of underemployment, business control or asset income without inventing notional earnings.
  4. Build needs schedules. Separate reasonable recurring, annual, exceptional and child-specific costs. Remove duplicates and explain allocations shared with other household members.
  5. Map direct support. Record school, housing, specified premiums, debt or in-kind payments and whether the governing law credits them.
  6. Calculate scenarios. Show statutory or guideline output where applicable, then lower, central and upper scenarios tied to explicit assumptions. Test both households' post-payment cash flow.
  7. Handle timing. Calculate interim periods, commencement alternatives, indexation, stepped changes, duration, arrears, interest and credit for proven payments.
  8. Stress test. Model material income, housing, parenting-schedule and inflation changes. Identify review or variation triggers.
  9. Build the evidence plan. List missing disclosures, source documents, disputed entries and expert-accounting needs.

Read the full file on GitHub · 48 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 · 48 lines · 47 tokens per session scan A 66ed1c9fb0c9

Subscribe to this mod's changes

maintenance-calculator is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 47 tokens to every session and 669 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

specification-writing

A workflow for writing complete patent specifications from patent claims and an invention disclosure. It adapts the document to a chosen jurisdiction, such as the US, Europe, or China.

wanshuiyin/Auto-claude-code-research-in-sleep · 49 tokens

regulatory-research-fallback

Fallback workflow for regulatory research when web extraction tools fail on government PDFs.

HKUDS/OpenSpace · 20 tokens

x-scorecard

OpenSSF Scorecard for assessing open source project security. Check security best practices and compliance. Dependency: This is an x-cmd module. Install x-cmd first (see x-cmd skill for installation options). see x-cmd skill for installation.

x-cmd/x-cmd · 57 tokens

gesellschaftsrechtliche-satzungen-agb

Für Gesellschaftsrechtliche Satzungen AGB Abgrenzung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: gesellschaftsrechtliche-satzungen-agb.

Klotzkette/claude-fuer-deutsches-recht · 69 tokens

memstack-business-gdpr

Use this skill when the user says 'GDPR', 'data protection', 'privacy compliance', 'DPA', 'DSAR', 'data subject request', 'cookie consent', 'privacy audit', 'CCPA', or asks 'do I need GDPR for this repo'. Scans the repository to detect what personal data is collected, classifies sensitivity, determines whether GDPR…

cwinvestments/memstack · 121 tokens

nda-review

Use when the user uploads or pastes a non-disclosure agreement and asks for review, redline, risk assessment, or a recommendation on whether to sign. Identifies missing standard protections, one-sided or unusual provisions, and operational issues; produces a structured report with severity ratings and citations to…

LegalQuants/lq-ai · 79 tokens