weak-signals-weekly

weak-signals-weekly is a skill for Claude Code from Ludovic33Fr/product-ai-toolbox. It costs 57 tokens per session (1,302 once invoked), scanned A, original, MIT.

A weekly summary of early warning signs gathered from user comments, support tickets, external mentions, and usage logs. It produces a short Markdown note covering emerging themes, unusual signals, data mismatches, and open questions.

In plain words
What is it for?
Reviewing the last seven days of user feedback and activity, spotting unexpected changes, and deciding what needs further investigation.
Why use it?
It brings scattered weekly observations into one readable note and records when a source is empty or unavailable.

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 Reviewing the last seven days of user feedback and activity, spotting unexpected changes, and deciding what needs further investigation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ludovic33fr/product-ai-toolbox/weak-signals-weekly
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 weak-signals-weekly
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 weak-signals-weekly

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/weak-signals-weekly"><img src="https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/weak-signals-weekly.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,302 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 $0.00057 $0.01302
Opus 5 $0.00028 $0.00651
Sonnet 5 $0.00011 $0.00260
Haiku 4.5 $0.00006 $0.00130

Measured 12d ago against content hash 412105b955d9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

weak-signals-weekly 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.

skills/weak-signals-weekly/SKILL.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.

weak-signals-weekly

Fiche éditoriale

Objectif. Produire la synthèse hebdomadaire de signaux faibles à partir de sources multiples (verbatims, logs, mentions, support).

Entrées. Connecteurs vers les sources brutes, période d'observation, contexte stratégique du périmètre.

Sorties. Synthèse markdown structurée en quatre blocs (thèmes émergents, signaux faibles, anomalies, questions ouvertes), lisible en cinq minutes.

Cadence d'usage. Chaque lundi matin, en automatique.

Mode opératoire

Quand m'invoquer

L'utilisateur me demande la synthèse hebdo de signaux faibles, ou évoque un besoin d'observation utilisateur sur la semaine. Sources typiques : exports de verbatims (CSV ou markdown), tickets support, mentions externes (forums, social), logs d'usage agrégés.

Procédure

  1. Identifier la fenêtre d'observation (par défaut : 7 derniers jours ; ajustable).
  2. Charger les sources fournies. Si une source est inaccessible ou vide, le signaler dans la note.
  3. Catégoriser chaque entrée dans l'un des 4 blocs : thème émergent (récurrent et nouveau), signal faible (rare mais inattendu), anomalie (écart entre data et verbatims), question ouverte (chose à creuser).
  4. Anonymiser systématiquement : pas de nom, pas d'identifiant utilisateur, pas d'email.
  5. Sourcer chaque entrée : lien ou référence à la source d'origine, date, et indice de confiance (faible / moyenne / forte selon le nombre d'occurrences).
  6. Distinguer fait et hypothèse : un fait observé est repérable dans les sources ; une hypothèse interprétative est une lecture du PM, à signaler comme telle.
  7. Produire la note au format ci-dessous.

Format de sortie

# Signaux faibles — semaine du {date_debut} au {date_fin}

## Thèmes émergents
- {thème} — observé dans {n} verbatims / {m} tickets — confiance {faible|moyenne|forte}
  Sources : [{ref}], [{ref}]
- ...

## Signaux faibles
- {signal} — apparu pour la première fois cette semaine — confiance faible
  Source : [{ref}]
- ...

## Anomalies
- {description du gap} — confiance {niveau}
  Indicateur d'usage : {chiffre}
  Verbatim contradictoire : "{citation anonymisée}" — [{ref}]
- ...

## Questions ouvertes
- ?
- ?

## Sources consultées
{nombre} sources sur {total}, {nombre_indispo} indisponibles ou vides.

Read the full file on GitHub · 114 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. 12d ago First seen · 114 lines · 57 tokens per session scan A 412105b955d9

Subscribe to this mod's changes

weak-signals-weekly is a skill published in the GitHub repository Ludovic33Fr/product-ai-toolbox (1 stars, last pushed 4mo ago), licensed MIT. It adds 57 tokens to every session and 1,302 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.