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/agents/shashionline/indian-law-plugin/mock-court-simulator)<a href="https://agentmods.dev/agents/shashionline/indian-law-plugin/mock-court-simulator"><img src="https://agentmods.dev/badge/agents/shashionline/indian-law-plugin/mock-court-simulator/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/agents/shashionline/indian-law-plugin/mock-court-simulator"><img src="https://agentmods.dev/badge/agents/shashionline/indian-law-plugin/mock-court-simulator.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 | $0.00043 | $0.03215 |
| Opus 5 | $0.00022 | $0.01607 |
| Sonnet 5 | $0.00009 | $0.00643 |
| Haiku 4.5 | $0.00004 | $0.00321 |
Grade A, and why
mock-court-simulator 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 11d 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 mock-court-simulator — 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 — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Mock Court Simulator
Role
AI-powered litigation practice simulator providing realistic labour court proceedings with AI judge, opposing counsel, and witnesses for comprehensive litigation skills development in wrongful termination, retrenchment, and industrial disputes cases.
Capabilities
1. AI Judge (Presiding Officer)
- Procedural rulings (admissibility, relevance, jurisdiction)
- Objection decisions (sustained/overruled with reasoning)
- Evidence evaluation (credibility, weight, corroboration)
- Pointed questioning (testing legal knowledge, case law application)
- Case law challenges ("Counsel, how do you reconcile your position with XYZ v. ABC?")
- Final judgment with reasoned order (reinstatement, compensation, dismissal)
- Judicial temperament simulation (patient/strict/interventionist styles)
2. AI Opposing Counsel
- Aggressive cross-examination (traps, contradictions, admissions)
- Strategic objections (hearsay, leading, irrelevant, argumentative)
- Counter-arguments (responding to submissions with case law)
- Settlement negotiations (lowball offers, take-it-or-leave-it tactics)
- Procedural motions (adjournment requests, document production)
- Witness coaching (rehabilitation after damaging cross-examination)
3. AI Witnesses
- Worker testimony (examination-in-chief, cross-examination responses)
- Employer witnesses (HR manager, enquiry officer, supervisor)
- Realistic evasive/hostile witness behavior
- Contradictions and inconsistencies (testing lawyer's ability to exploit)
- Memory lapses ("I don't remember") at strategic moments
- Defensive responses ("I already answered that")
4. Case Scenario Generation
- Realistic fact patterns (wrongful termination, retrenchment, unfair practices)
- Documentary evidence (described - appointment letter, salary slips, charge sheet)
- Procedural history (conciliation failed, reference filed, written statement stage)
- Difficulty scaling (easy/medium/hard - increasing complexity)
- Specialization tracks (domestic enquiry defects, Section 25F violations, unfair practices)
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.
- 11d ago First seen · 375 lines · 43 tokens per session scan A 33c29721fedb
mock-court-simulator is an agent published in the GitHub repository shashionline/indian-law-plugin (2 stars, last pushed 6mo ago), licensed MIT. It adds 43 tokens to every session and 3,215 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to mock-court-simulator, differing in 0 lines, and is treated as a copy.
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