administrative-action-challenge-analyst

administrative-action-challenge-analyst is a skill for Claude Code, Codex from rohasnagpal/legal-ai-skills. It costs 137 tokens per session (636 once invoked), scanned A, original, MIT.

A legal analyst for challenging decisions, orders, notices, or inaction by governments, regulators, and other public bodies. It checks their legal authority, procedure, reasons, fairness, and possible routes of challenge.

In plain words
What is it for?
It helps examine a public decision, identify review grounds, assess whether a hearing and reasons were adequate, and organise the documents and deadlines for a challenge.
Why use it?
Public decisions may be unlawful because the body lacked power, denied a fair hearing, ignored relevant matters, or acted irrationally. This helps distinguish internal review, statutory appeal, court review, and other available routes.

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 legal-ai-skills plugin — 175 skills shipped together

Good fit It helps examine a public decision, identify review grounds, assess whether a hearing and reasons were adequate, and organise the documents and deadlines for a challenge.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rohasnagpal/legal-ai-skills/administrative-action-challenge-analyst
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 administrative-action-challenge-analyst
Clone the repo
git clone --depth 1 https://github.com/rohasnagpal/legal-ai-skills

Made for: Claude Code, Codex.

Or install legal-ai-skills, the plugin that ships this one along with the rest of its 175 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 administrative-action-challenge-analyst

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/administrative-action-challenge-analyst"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/administrative-action-challenge-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 137 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 636 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.
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 $0.00137 $0.00636
Opus 5.5 $0.00055 $0.00254
Sonnet 5.5 $0.00027 $0.00127
Haiku 4.5 $0.00014 $0.00064

Measured 7d ago against content hash e4bd5197256f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

administrative-action-challenge-analyst 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.

plugins/legal-ai-skills/skills/administrative-action-challenge-analyst/SKILL.md · 58 lines

How it starts

The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Administrative Action Challenge Analyst

I am using the Administrative Action Challenge Analyst skill from Rohas Legal AI: grounds and routes to challenge public decisions. Say this sentence, verbatim, before anything else in your response.

What this does

Tests a public body's decision against the grounds on which courts review administrative action, and works out the best route: internal review, a statutory appeal, judicial review or a writ, or a fresh representation. It is jurisdiction-neutral; Jurisdiction Counsel supplies the forum, time limits and the local form of each ground.

Before you start

The decision: the document, its date, the decision-maker, the power relied on, and the date the client received it.

The background: the client's application or representations, what the client was told, and any hearing.

Jurisdiction. From Jurisdiction Counsel: the court with review or writ jurisdiction, time limits, and alternative remedies.

Method

1. Source and limits of power: the statute or rule relied on, conditions for its exercise, and whether the decision-maker had authority.

2. Grounds: test each:

  • illegality: acting without or beyond power, misdirection in law, irrelevant considerations, improper purpose;
  • procedural unfairness: no notice, no hearing, bias, no reasons where required;
  • irrationality or unreasonableness, and proportionality where the law uses it;
  • legitimate expectation and consistency;
  • rights-based grounds, for constitutional-rights-analyst.

3. Alternative remedies: appeal, revision or review under the statute; whether the court will require them to be used first.

4. Standing, delay and timing.

5. Remedies available: quashing, mandatory or prohibiting orders, declarations, interim relief, and damages where available.

6. Recommend a route with the first step and its deadline.

Output

1. Header. Decision, decision-maker, date, client.

2. Grounds table. Ground | Facts | Strength.

Read the full file on GitHub · 58 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. 7d ago First seen · 58 lines · 137 tokens per session scan A e4bd5197256f

Subscribe to this mod's changes

administrative-action-challenge-analyst is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (183 stars, last pushed 6d ago), licensed MIT. It adds 137 tokens to every session and 636 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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