four-indicators-measure

four-indicators-measure is a skill for Claude Code from Ludovic33Fr/product-ai-toolbox. It costs 69 tokens per session (1,498 once invoked), scanned A, original, MIT.

A quarterly method for calculating four measures of AI-assisted product management from decision logs, usage data, agent audits, and calendar comparisons. It produces current values, previous-quarter comparisons, and a written interpretation.

In plain words
What is it for?
Use it at the end of a quarter to calculate decision density, retrospective accuracy, switching delay, and reinvestment index. It is for product managers reviewing how their AI-supported work is changing.
Why use it?
It replaces a general end-of-quarter impression with a repeatable review of decisions, outcomes, switching speed, and reinvestment. The method also allows enough time to judge older decisions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the pm-augmente plugin — 12 skills, 6 agents shipped together

Good fit Use it at the end of a quarter to calculate decision density, retrospective accuracy, switching delay, and reinvestment index. It is for product managers reviewing how their AI-supported work is changing.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ludovic33fr/product-ai-toolbox/four-indicators-measure
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 Ludovic33Fr/product-ai-toolbox --skill four-indicators-measure
Clone the repo
git clone --depth 1 https://github.com/Ludovic33Fr/product-ai-toolbox

Made for: Claude Code.

Or install pm-augmente, the plugin that ships this one along with the rest of its 12 skills, 6 agents.

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 four-indicators-measure

README.md
[![agentmods](https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/four-indicators-measure/github.svg)](https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/four-indicators-measure)
Your own site
<a href="https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/four-indicators-measure"><img src="https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/four-indicators-measure/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 four-indicators-measure

Your own site · 80×15
<a href="https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/four-indicators-measure"><img src="https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/four-indicators-measure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,498 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 $0.00069 $0.01498
Opus 5 $0.00034 $0.00749
Sonnet 5 $0.00014 $0.00300
Haiku 4.5 $0.00007 $0.00150

Measured 11d ago against content hash b4edfcc50253, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

four-indicators-measure 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 11d 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.

skills/four-indicators-measure/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

four-indicators-measure

Fiche éditoriale

Objectif. Calculer les quatre indicateurs du PM augmenté (densité de décision, justesse rétrospective, délai de bascule, indice de réinvestissement).

Entrées. Journal de décision, indicateurs d'usage, audits agents, agendas comparés.

Sorties. Tableau des quatre indicateurs avec valeur actuelle, comparaison trimestre précédent, lecture qualitative.

Cadence d'usage. Trimestriellement.

Mode opératoire

Quand m'invoquer

L'utilisateur clôture un trimestre et veut faire le bilan factuel de sa transformation augmentée. Il fournit son journal de décision, ses logs d'usage IA, les rapports d'audit de ses agents, et l'extraction de son agenda sur le trimestre.

Procédure

Pour chaque indicateur, suivre la définition du chapitre 14.

  1. Densité de décision. Compter le nombre de décisions explicitement consignées dans le journal sur le trimestre. Diviser par le nombre de jours ouvrés. Comparer au trimestre précédent.

  2. Justesse rétrospective. Pour les décisions du trimestre N-2 (assez de recul pour observer le résultat), classer en : confirmée par les faits, infirmée, non concluante. Calculer le ratio confirmées / total.

  3. Délai de bascule. Mesurer le temps moyen entre la première mention d'un sujet (en réunion, en ticket, en verbatim) et la première décision documentée à son sujet. Comparer N et N-1.

  4. Indice de réinvestissement. Sur la base des agendas comparés, calculer la part du temps gagné par l'IA qui a été réinvestie en discovery / arbitrage / décision (vs. consommée par d'autres réunions ou lissée dans la journée). Le livre suggère 60% comme cible saine.

  5. Produire le tableau au format ci-dessous, suivi d'une lecture qualitative en 3-5 phrases.

Format de sortie

# Bilan trimestriel — Q{n} {année}

## Tableau des quatre indicateurs

| Indicateur | Valeur Q{n} | Valeur Q{n-1} | Évolution | Cible |
|------------|-------------|----------------|-----------|-------|
| Densité de décision | {x} décisions/jour | {y} décisions/jour | {↗ ↘ →} | — |
| Justesse rétrospective | {x}% (sur {n} décisions Q{n-2}) | {y}% | {↗ ↘ →} | ≥ 60% |
| Délai de bascule | {x} jours | {y} jours | {↗ ↘ →} | en baisse |
| Indice de réinvestissement | {x}% | {y}% | {↗ ↘ →} | ≥ 60% |

## Lecture qualitative

{3-5 phrases : ce que les chiffres disent au-delà de la valeur brute. Ce qui est en progrès, ce qui régresse, ce qu'il faut creuser.}

## Sources des données

- Journal de décision : {nombre d'entrées analysées sur la période}
- Audits agents : {nombre d'audits intégrés}
- Agenda : {nombre de semaines couvertes}
- Indicateurs d'usage : {référence}

## Limites de cette mesure

{1-3 phrases : ce qu'il faut savoir des biais ou des manques de cette mesure ce trimestre.}

Read the full file on GitHub · 110 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. 11d ago First seen · 110 lines · 69 tokens per session scan A b4edfcc50253

Subscribe to this mod's changes

four-indicators-measure is a skill published in the GitHub repository Ludovic33Fr/product-ai-toolbox (1 stars, last pushed 4mo ago), licensed MIT. It adds 69 tokens to every session and 1,498 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 2026-08-31.