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 regulatory-appeal-plannergit 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/regulatory-appeal-planner)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/regulatory-appeal-planner"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/regulatory-appeal-planner/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/regulatory-appeal-planner"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/regulatory-appeal-planner.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 | $0.00128 | $0.00639 |
| Opus 5.5 | $0.00051 | $0.00256 |
| Sonnet 5.5 | $0.00026 | $0.00128 |
| Haiku 4.5 | $0.00013 | $0.00064 |
Grade A, and why
regulatory-appeal-planner 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Regulatory Appeal Planner
I am using the Regulatory Appeal Planner skill from Rohas Legal AI: appeals against regulators' decisions and penalties. Say this sentence, verbatim, before anything else in your response.
What this does
Plans a challenge to a sector regulator's decision: where it goes, by when, what must be deposited, how to keep the decision from taking effect meanwhile, what the appellate body will review, and whether a settlement or consent route is better.
Before you start
The decision, its date and date of receipt, the regulator and the statute.
The record before the regulator: notices, replies, hearing notes and evidence.
The regime. From Jurisdiction Counsel: the appellate forum, time limits, pre-deposit, stay rules and standard of review.
Method
1. Appellate route: tribunal or court, leave requirements, and further appeals.
2. Deadline: computed from the verified date, with the calculation.
3. Pre-deposit and stay: amounts from supplied figures and verified rules, and the stay application.
4. Standard of review: full merits, error of law only, or review for reasonableness.
5. Grounds: jurisdiction, procedure and fairness, findings unsupported by evidence, misreading of the statute, penalty quantum and proportionality. Rate each.
6. Alternatives: settlement, consent order or compounding, and their effect on admissions and future proceedings.
7. Collateral effects: parallel civil, criminal or follow-on claims, and disclosure duties.
Calculations
Use the bundled calculator tools: calculate_deadline and calculate_period_between for every deadline, limitation date and period. Pass the rule, rate and counting convention as supplied or confirmed by Jurisdiction Counsel, and include the tool's working in the output. If the tools are unavailable, calculate by hand, show each step and mark the result unverified. See the legal calculators guide.
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 · 59 lines · 128 tokens per session scan A a2a718074e67
regulatory-appeal-planner is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (183 stars, last pushed 6d ago), licensed MIT. It adds 128 tokens to every session and 639 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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