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.
git clone --depth 1 https://github.com/shashionline/indian-law-pluginWrote 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/commands/shashionline/indian-law-plugin/practice-lawyer)<a href="https://agentmods.dev/commands/shashionline/indian-law-plugin/practice-lawyer"><img src="https://agentmods.dev/badge/commands/shashionline/indian-law-plugin/practice-lawyer/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/commands/shashionline/indian-law-plugin/practice-lawyer"><img src="https://agentmods.dev/badge/commands/shashionline/indian-law-plugin/practice-lawyer.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 | $0.00000 | $0.05243 |
| Opus 5 | $0.00000 | $0.02622 |
| Sonnet 5 | $0.00000 | $0.01049 |
| Haiku 4.5 | $0.00000 | $0.00524 |
Grade A, and why
practice-lawyer 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 12d 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.
This is a copy
100% identical to practice-lawyer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 607 lines — stays where its author put it; the contents beside it link to each section on GitHub.
description: Interactive mock court practice across ALL legal domains (labour, consumer, civil, writ petitions, criminal) with AI judge, opposing counsel, and witnesses. Provides realistic court simulations, performance feedback, and skill development for lawyers, law students, and aspiring litigators. argument-hint: [domain] [case-type] [difficulty]
Command: Universal Litigation Practice Simulator
Purpose
Interactive mock court practice across ALL legal domains (labour, consumer, civil, writ petitions, criminal) with AI judge, opposing counsel, and witnesses. Provides realistic court simulations, performance feedback, and skill development for lawyers, law students, and aspiring litigators.
Usage
/practice-lawyer [domain] [case-type] [difficulty]
Parameters:
domain: labour | consumer | civil | writ | criminal | propertycase-type: Specific case within domain (e.g., wrongful-termination, rera-complaint, mandamus)difficulty: easy (70% win rate) | medium (50% win rate) | hard (30% win rate)
What This Command Does
1. Generates Realistic Case Scenario
Creates fact pattern with:
- Background facts (employment details, transaction details, dispute history)
- Legal issues (what needs to be proven/defended)
- Evidence available (documents, witnesses)
- Procedural posture (first hearing, evidence stage, arguments stage)
- Your role (applicant's counsel OR respondent's counsel)
2. AI Court Simulation
AI Judge: Adapts behavior to court type
- Labour Court: Patient, worker-friendly, less formal ("Your Honour")
- Consumer Forum: Semi-formal, pro-consumer bias
- High Court: Very formal, strict, interventionist ("My Lord")
- Civil Court: Moderately formal, neutral
AI Opposing Counsel: Domain-appropriate strategies
- Labour: Employer's lawyer (attacks worker credibility, claims misconduct proven)
- Consumer: Company's lawyer (claims no deficiency, buyer's contributory negligence)
- Writ: Government's lawyer (cites alternative remedy, questions locus standi)
- Civil: Defendant's lawyer (denies breach, claims force majeure)
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.
- 12d ago First seen · 607 lines · 0 tokens per session scan A 382526301049
practice-lawyer is a command published in the GitHub repository shashionline/indian-law-plugin (2 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,243 tokens. A static security scan graded it A with 0 findings. It is 100% identical to practice-lawyer, differing in 0 lines, and is treated as a copy.
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