normalisation-juridique-fr-christophe-quezel-ambrunaz

normalisation-juridique-fr-christophe-quezel-ambrunaz is a skill for Claude Code, Codex from lawve-ai/awesome-legal-skills. It costs 219 tokens per session (2,867 once invoked), scanned A, original, no licence file.

A tool for cleaning up French legal documents in Microsoft Word. It applies consistent typography and distinguishes fixed formatting corrections from changes that require judgment.

In plain words
What is it for?
It is for standardising French legal documents, including punctuation, non-breaking spaces, legal references, ordinal numbers, and ligatures.
Why use it?
It removes common formatting inconsistencies—such as quotation marks, spacing, references, and ordinal forms—without treating every edit as a legal rewrite.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit It is for standardising French legal documents, including punctuation, non-breaking spaces, legal references, ordinal numbers, and ligatures.

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Install with agentmods
npx agentmods add skills/lawve-ai/awesome-legal-skills/normalisation-juridique-christophe-quezel-ambrunaz
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 lawve-ai/awesome-legal-skills --skill normalisation-juridique-christophe-quezel-ambrunaz
Clone the repo
git clone --depth 1 https://github.com/lawve-ai/awesome-legal-skills

Made for: Claude Code, Codex.

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 normalisation-juridique-fr-christophe-quezel-ambrunaz

README.md
[![agentmods](https://agentmods.dev/badge/skills/lawve-ai/awesome-legal-skills/normalisation-juridique-christophe-quezel-ambrunaz/github.svg)](https://agentmods.dev/skills/lawve-ai/awesome-legal-skills/normalisation-juridique-christophe-quezel-ambrunaz)
Your own site
<a href="https://agentmods.dev/skills/lawve-ai/awesome-legal-skills/normalisation-juridique-christophe-quezel-ambrunaz"><img src="https://agentmods.dev/badge/skills/lawve-ai/awesome-legal-skills/normalisation-juridique-christophe-quezel-ambrunaz/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 normalisation-juridique-fr-christophe-quezel-ambrunaz

Your own site · 80×15
<a href="https://agentmods.dev/skills/lawve-ai/awesome-legal-skills/normalisation-juridique-christophe-quezel-ambrunaz"><img src="https://agentmods.dev/badge/skills/lawve-ai/awesome-legal-skills/normalisation-juridique-christophe-quezel-ambrunaz.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 219 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,867 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 unknown 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 $0.00219 $0.02867
Opus 5.5 $0.00088 $0.01147
Sonnet 5.5 $0.00044 $0.00573
Haiku 4.5 $0.00022 $0.00287

Measured 1mo ago against content hash 66a5aa1560b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

normalisation-juridique-fr-christophe-quezel-ambrunaz 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 1mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/normaliser.py, scripts/registre.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/normalisation-juridique-christophe-quezel-ambrunaz/SKILL.md · 139 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

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. 1mo ago First seen · 139 lines · 219 tokens per session scan A 66a5aa1560b8

Subscribe to this mod's changes

normalisation-juridique-fr-christophe-quezel-ambrunaz is a skill published in the GitHub repository lawve-ai/awesome-legal-skills (903 stars, last pushed 8d ago), with no licence file. It adds 219 tokens to every session and 2,867 once invoked, about $0.0009 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 2026-09-03.

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