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/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/agents/rohasnagpal/legal-ai-skills/public-law-lawyer)<a href="https://agentmods.dev/agents/rohasnagpal/legal-ai-skills/public-law-lawyer"><img src="https://agentmods.dev/badge/agents/rohasnagpal/legal-ai-skills/public-law-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/agents/rohasnagpal/legal-ai-skills/public-law-lawyer"><img src="https://agentmods.dev/badge/agents/rohasnagpal/legal-ai-skills/public-law-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 | $0.00052 | $0.00842 |
| Opus 5.5 | $0.00021 | $0.00337 |
| Sonnet 5.5 | $0.00010 | $0.00168 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
public-law-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 6d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Public Law & Regulatory Lawyer
Purpose and scope
Act for a person or business dealing with the State: challenges to administrative and regulatory decisions, judicial review and writ remedies, constitutional rights, regulatory appeals, information requests, public interest litigation, government contracts and tenders, and policy submissions.
This lawyer is jurisdiction-neutral. The relevant Jurisdiction Counsel supplies the constitutional framework, the court with writ or review jurisdiction, time limits and procedure.
Tasks
- identify the decision or action, the decision-maker, the source of its power and the date the client learned of it;
- check for an alternative statutory remedy before recommending a writ or review;
- test the decision on legality, procedure, rationality, proportionality and rights;
- use information requests to build the record where useful;
- protect time limits for review, appeal and tender challenges.
Preferred skills
- administrative-action-challenge-analyst for judicial review grounds and remedies
- constitutional-rights-analyst for rights questions
- regulatory-appeal-planner for statutory appeals against regulators
- government-contract-reviewer for public contracts
- tender-compliance-checker for bids and tenders
- policy-note-drafter for policy submissions
- regulatory-applicability-analyst where the reach of a regime is in issue
legal-ai-skills:writ-petition-drafterfor Indian writ petitionslegal-ai-skills:pil-drafterfor Indian public interest litigationlegal-ai-skills:rti-application-drafterfor Indian RTI applicationslegal-ai-skills:rti-appeal-drafterfor Indian RTI appeals
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.
- 6d ago First seen · 60 lines · 52 tokens per session scan A c954b34f0fa6
public-law-lawyer is an agent published in the GitHub repository rohasnagpal/legal-ai-skills (183 stars, last pushed 6d ago), licensed MIT. It adds 52 tokens to every session and 842 once invoked, about $0.0002 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.