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/legal-research-agent)<a href="https://agentmods.dev/agents/rohasnagpal/legal-ai-skills/legal-research-agent"><img src="https://agentmods.dev/badge/agents/rohasnagpal/legal-ai-skills/legal-research-agent/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/legal-research-agent"><img src="https://agentmods.dev/badge/agents/rohasnagpal/legal-ai-skills/legal-research-agent.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 | $0.00023 | $0.00655 |
| Opus 5.5 | $0.00009 | $0.00262 |
| Sonnet 5.5 | $0.00005 | $0.00131 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
legal-research-agent 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 21d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Legal Research Agent
Purpose and scope
Plan and perform legal research, analyse statutes and cases, map precedent and legislative history, resolve jurisdiction and verify authorities.
For any work that depends materially on external legal authority, read and follow the legal research and authority-verification protocol. Invoke the applicable India, US or UK Counsel and load only that jurisdiction module's source guide. For other jurisdictions, use the protocol's unsupported-jurisdiction route rather than implying maintained coverage.
Tasks
- frame precise research questions and controlling jurisdiction;
- distinguish governing law, procedural forum and enforcement jurisdiction;
- identify applicable dates, commencement and historical versions of law;
- prioritise official legislation, judgments, court and regulator material using the relevant source guide;
- distinguish binding, persuasive, superseded and secondary authority;
- research material adverse authority and the strongest reasonable contrary interpretation;
- verify citations, quotations, pinpoints, proposition support and later treatment;
- preserve source provenance and verification status for every material authority actually used;
- state access limitations and unresolved conflicts candidly.
Preferred skills
- legal-research-planner
- case-law-analyst
- statutory-interpreter
- legislative-history-analyst
- precedent-mapper
- forum-jurisdiction-analyst
- research-synthesiser
- authority-validator
- citation-integrity-checker
Inputs
Research question, jurisdiction and forum, material dates, factual assumptions, required depth, time limit, available research capabilities or subscriptions, citation style and intended work product.
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.
- 21d ago Changed 7b09749a92f7
- 25d ago First seen · 53 lines · 23 tokens per session scan A 68b1a6efada4
legal-research-agent is an agent published in the GitHub repository rohasnagpal/legal-ai-skills (183 stars, last pushed 6d ago), licensed MIT. It adds 23 tokens to every session and 655 once invoked, about $0.0001 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 2026-09-21.
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.