preparer-evaluation

preparer-evaluation is a skill for Claude Code, Codex from ai-swiss/base. It costs 28 tokens per session (1,279 once invoked), scanned A, original, Apache-2.0.

A tool for preparing a classroom assessment that matches the learning goals of a teaching unit, including tasks, criteria, a scoring scale, and a suggested answer key.

In plain words
What is it for?
Use it to prepare formative assessments, which support learning, or summative assessments, which certify learning, in written, oral, or practical formats.
Why use it?
It removes the work of turning lesson goals into a consistent assessment. The teacher still makes the final judgment about students.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/ai-swiss/base/preparer-evaluation
Any agent
npx skills add ai-swiss/base --skill preparer-evaluation
Clone the repo
git clone --depth 1 https://github.com/ai-swiss/base

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 preparer-evaluation

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai-swiss/base/preparer-evaluation.svg)](https://agentmods.dev/skills/ai-swiss/base/preparer-evaluation)
Your own site
<a href="https://agentmods.dev/skills/ai-swiss/base/preparer-evaluation"><img src="https://agentmods.dev/badge/skills/ai-swiss/base/preparer-evaluation.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,279 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00028 $0.01279
Opus 5 $0.00014 $0.00639
Sonnet 5 $0.00006 $0.00256
Haiku 4.5 $0.00003 $0.00128

Measured 5d ago against content hash 99211ec4ba31, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

preparer-evaluation 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 5d 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.

exemples/assistant-enseignant/.ai/agents/assistant-enseignant/skills/processes/preparer-evaluation/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Préparer une évaluation

Préparer une évaluation alignée sur une séquence: reprendre les objectifs, construire la grille de critères et le barème, rédiger les tâches et un corrigé proposé. L'assistant prépare le matériel d'évaluation; évaluer les élèves demeure un acte de l'enseignant.

Inputs

Demande à l'utilisateur:

  • La séquence concernée: pour la lire dans sequences/
  • Le type d'évaluation: formative (pour apprendre) ou sommative (pour attester)
  • La durée et le format: écrit, oral, travail pratique

Si aucune séquence n'existe pour ce sujet, demande directement les objectifs visés, ou propose de préparer d'abord la séquence.

Lis la compétence skills/competences/metier-enseignement/SKILL.md pour l'alignement et le feedback.

Si .ai/journal/ contient des entrées récentes, lis-les pour le contexte.

Étapes

1. Reprendre les objectifs

Lis la séquence dans sequences/ et résume:

«Pour rappel, la séquence [titre] vise ces objectifs: [liste]. Lesquels voulez-vous évaluer? Tous, ou une sélection?»

← Reformulation

2. Vérifier l'alignement

Pour chaque objectif évalué, vérifie qu'une activité de la séquence l'a effectivement travaillé. Signale tout écart:

«[ATTENTION: l'objectif 3 n'a été travaillé que dans le prolongement pour élèves avancés. L'évaluer pour toute la classe serait inéquitable.] On le garde, on l'adapte, ou on le retire?»

← Reformulation

3. Construire la grille de critères

Propose une grille reliant chaque objectif à des critères observables:

«Voici la grille que je propose:

Objectif évalué Critère observable Points
[objectif 1] [ce qu'une réussite montre concrètement] [n]
[objectif 2] [critère] [n]

Les critères sont-ils justes et observables?»

← Reformulation

4. Barème

Propose la répartition des points et le seuil de suffisance, marqués [A VALIDER: ...]:

«[A VALIDER: total de 30 points, seuil de suffisance à 18 points, conformément à votre pratique habituelle]. Le barème final relève de votre responsabilité et des règles de votre établissement. Cette répartition vous convient-elle?»

Read the full file on GitHub · 118 lines

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. 5d ago First seen · 118 lines · 28 tokens per session scan A 99211ec4ba31

Subscribe to this mod's changes

preparer-evaluation is a skill published in the GitHub repository ai-swiss/base (43 stars, last pushed yesterday), licensed Apache-2.0. It adds 28 tokens to every session and 1,279 once invoked, about $0.0001 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.