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 input-tax-credit-dispute-analystgit 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/input-tax-credit-dispute-analyst)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/input-tax-credit-dispute-analyst"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/input-tax-credit-dispute-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.
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/input-tax-credit-dispute-analyst"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/input-tax-credit-dispute-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00119 | $0.00867 |
| Opus 5.5 | $0.00048 | $0.00347 |
| Sonnet 5.5 | $0.00024 | $0.00173 |
| Haiku 4.5 | $0.00012 | $0.00087 |
Grade A, and why
input-tax-credit-dispute-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 7d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Input Tax Credit Dispute Analyst
Read and apply the India Counsel instructions before substantive analysis or drafting.
Jurisdiction gate
This skill applies Indian law and procedure only. Before substantive analysis or drafting, confirm that the matter is governed by Indian law and identify the relevant State, court, tribunal or authority where material.
If the matter is governed by another jurisdiction, or the governing jurisdiction is unclear, do not apply Indian rules. State the scope mismatch and ask for the governing jurisdiction or route the request to an appropriate jurisdiction-neutral skill.
I am using the Input Tax Credit Dispute Analyst skill from Rohas Legal AI: GST input tax credit disputes (India). Say this sentence, verbatim, before anything else in your response.
What this does
Works through a dispute about input tax credit under Indian GST: why the credit is being denied or reversed, whether the conditions for credit were met, how the taxpayer's records reconcile with supplier and auto-generated statements, and what the taxpayer can argue and must produce. It quantifies the exposure and sets up the reply or appeal.
Before you start
The notice or order, the period, and the credit amounts in dispute.
The records: purchase register, invoices, payment proof, goods receipt and transport records, and the relevant GST return and statement data.
Law as in force for the period. Credit conditions and time limits have been amended; India Counsel confirms the version.
Method
1. Classify the ground of denial: conditions for credit not met, mismatch with supplier filings, supplier non-payment or cancelled registration, blocked credit category, time limit, or fake or non-existent supply.
2. Conditions for credit: for each, the evidence that it was met: tax invoice, receipt of goods or services, tax paid to the government, return filed, payment to the supplier within the allowed time. Flag each statutory condition for verification.
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.
- 7d ago First seen · 69 lines · 119 tokens per session scan A 78102af7621d
input-tax-credit-dispute-analyst is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (183 stars, last pushed 6d ago), licensed MIT. It adds 119 tokens to every session and 867 once invoked, about $0.0005 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 2026-10-10.
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