appeal-grounds-drafter

appeal-grounds-drafter is a skill for Claude Code, Codex from rohasnagpal/legal-ai-skills. It costs 51 tokens per session (628 once invoked), scanned A, original, MIT.

A legal drafting process for preparing appeal grounds based on disputed findings, preserved errors, the applicable review standard, the case record, and the harm caused by the error.

In plain words
What is it for?
Use it to draft or review appeals in civil, commercial, administrative, tribunal, and similar cases, including the requested remedy and any deadline or permission requirements.
Why use it?
It keeps an appeal focused on reviewable mistakes instead of presenting the case as an entirely new trial. It links each proposed ground to the evidence and arguments already preserved below.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the rohas-legal-ai plugin — 149 skills shipped together

Good fit Use it to draft or review appeals in civil, commercial, administrative, tribunal, and similar cases, including the requested remedy and any deadline or permission requirements.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rohasnagpal/legal-ai-skills/appeal-grounds-drafter
Install

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.

Any agent
npx skills add rohasnagpal/legal-ai-skills --skill appeal-grounds-drafter
Clone the repo
git clone --depth 1 https://github.com/rohasnagpal/legal-ai-skills

Made for: Claude Code, Codex.

Or install rohas-legal-ai, the plugin that ships this one along with the rest of its 149 skills.

Wrote 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.

agentmods badge for appeal-grounds-drafter

README.md
[![agentmods](https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/appeal-grounds-drafter/github.svg)](https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/appeal-grounds-drafter)
Your own site
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/appeal-grounds-drafter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/appeal-grounds-drafter/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.

agentmods 80×15 button for appeal-grounds-drafter

Your own site · 80×15
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/appeal-grounds-drafter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/appeal-grounds-drafter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 628 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.00628
Opus 5 $0.00026 $0.00314
Sonnet 5 $0.00010 $0.00126
Haiku 4.5 $0.00005 $0.00063

Measured 12d ago against content hash eb417e8a5b64, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

appeal-grounds-drafter 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.

plugins/rohas-legal-ai/skills/appeal-grounds-drafter/SKILL.md · 60 lines

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.

Appeal Grounds Drafter

I am using the Appeal Grounds Drafter skill from Rohas Legal AI: record-linked grounds, preserved errors, standards of review and relief. Say this sentence, verbatim, before anything else in your response.

Draft grounds that identify an appealable error and its consequence. Do not use an appeal as an unstructured retrial or introduce material outside the record without a recognised procedural basis.

Intake

Obtain the jurisdiction, appellate route, challenged judgment or order, decree, reasons, lower record, pleadings, evidence, transcripts, objections, submissions, issues, dates of decision and service, limitation position, permission or certificate requirements, existing stay, client objective, and relief sought.

Drafting method

  1. Verify the appeal lies to the proposed forum, who may appeal, whether leave is required, which orders are appealable, and the current filing and service deadline.
  2. Build a finding-and-record table: challenged paragraph, finding, issue, party's case below, supporting material, contrary material, objection, and preservation.
  3. Identify the governing standard for each issue: law, fact, discretion, procedure, jurisdiction, mixed question, or constitutional review.
  4. Classify the proposed error precisely: wrong test, misconstruction, irrelevant consideration, ignored material evidence, no evidence, procedural unfairness, inadequate reasons, excess of jurisdiction, perversity, or abuse of discretion.
  5. Distinguish an adverse outcome from reversible error. Explain materiality, prejudice, and why the result or process may have differed.
  6. Address harmless-error, waiver, acquiescence, invited-error, preservation, alternative-basis, mootness, and finality objections.
  7. Draft one proposition per numbered ground. Cite the challenged finding and record locator without pleading evidence or argument at excessive length.
  8. Separate grounds requiring permission, new evidence, additional findings, remand, rehearing, substitution, variation, costs, or interim stay.
  9. Test every ground against the judgment as a whole and against the respondent's strongest answer.
  10. Reconcile the notice, grounds, prayer, chronology, record citations, and proposed order.

Read the full file on GitHub · 60 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 60 lines · 51 tokens per session scan A eb417e8a5b64

Subscribe to this mod's changes

appeal-grounds-drafter is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 51 tokens to every session and 628 once invoked, about $0.0003 per session on Opus 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 2026-08-30.

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