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.
npx skills add Ertinox7711/SGRR-AGI-V2 --skill demand-validatorgit clone --depth 1 https://github.com/Ertinox7711/SGRR-AGI-V2Wrote 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.
[](https://agentmods.dev/skills/ertinox7711/sgrr-agi-v2/demand-validator)<a href="https://agentmods.dev/skills/ertinox7711/sgrr-agi-v2/demand-validator"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/demand-validator/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.
<a href="https://agentmods.dev/skills/ertinox7711/sgrr-agi-v2/demand-validator"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/demand-validator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00176 | $0.01461 |
| Opus 5 | $0.00088 | $0.00731 |
| Sonnet 5 | $0.00035 | $0.00292 |
| Haiku 4.5 | $0.00018 | $0.00146 |
Grade A, and why
demand-validator 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demand Validator
Outil local de validation de demande produit e-com. Stdlib-only, gratuit, sans clé API.
Vit dans C:\Users\YOU\Documents\BUSINESS\recherche produit\.
Quand l'utiliser
- Valider la demande d'un produit / mot-clé avant de l'ajouter au store
- Lire tendance (12 mois) + saisonnalité (5 ans, pic/creux) d'un mot-clé
- Mesurer l'ampleur d'intention d'achat (autocomplete Google + Amazon)
- Donner un verdict chiffré GO / WATCH / SKIP + niveau de confiance
- Vérifier le gate marge : CPC×150 (CPA pire-cas) vs marge nette (prix − coût − 10% frais)
CLI (ce que TOI Claude tu appelles dans les autres conversations)
Depuis C:\Users\YOU\Documents\BUSINESS\recherche produit\ :
PYTHONIOENCODING=utf-8 python demand_validator.py "remontoir montre" --gl fr --json
Options : --gl fr|us|de|uk|es · --hl <langue> · --price X --cost Y --cpc Z (gate marge)
· --seed-keyword "montre automatique" --seed-volume 40000 (estimation volume par ancrage Trends)
· --amazon-fr (vraie largeur amazon.fr via Playwright, lent) · --no-scout · --no-cache
Sortie --json = dict : score, verdict, confidence, partial, signals_known,
detail.S1..S8 (chaque signal : level GO/WATCH/SKIP/?, why), cpa_gate, manual_checks, disclaimer.
Sans --json = rapport lisible FR.
Cache disque 24h (demand_cache.json) → 2e appel instantané + évite le 429 Google Trends.
Les 8 signaux (poids)
| Signal | Source | Poids | |
|---|---|---|---|
| S1 | Tendance 12m (hausse/stable/déclin) | Google Trends | 1.5 |
| S2 | Saisonnalité (ratio bas/haut, pic) | Google Trends 5y | 1.5 |
| S3 | Volume cumulé estimé (si seed fourni) | Trends ancrage | 2.0 |
| S4 | Concurrence ecom | serp_scout c2 | 1.5 |
| S5 | Anti-marques (kill switch) | serp_scout c5 | 1.5 |
| S6 | Intention d'achat Google | Google Suggest | 1.0 |
| S7 | Demande physique Amazon | Amazon Suggest | 0.5 |
| S8 | Viabilité Search (ads texte) | serp_scout c12 | 0.5 |
Score = signaux connus renormalisés sur 100. GO exige ≥5 signaux mesurés (sinon bridé à WATCH — un 100/100 sur 2 signaux ne prouve rien). Kill switches : S5 SKIP, ou (S3 SKIP & prix<200€), ou (S1+S2 SKIP).
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.
- 2d ago First seen · 80 lines · 0 tokens per session scan A 8893ea950fe9
demand-validator is a skill published in the GitHub repository Ertinox7711/SGRR-AGI-V2 (1 stars, last pushed 3d ago), licensed MIT. It adds 176 tokens to every session and 1,461 once invoked, about $0.0009 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 2026-09-09.
Other skills, from other repositories
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shop-research
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workflow-optimization
Read the customer request, account record, and supplied policies before choosing an action. Policy overrides the customer's requested remedy.
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franchise-inventory
Analyze franchise inventory management for par level optimization, waste tracking and root cause analysis, and theoretical vs. actual usage variance.
merchandising-analytics
Analyze retail merchandising systems including planogram optimization (space-to-sales alignment, fair share index, sales per linear foot), visual merchandising effectiveness for in-store displays and e-commerce product pages, market basket analysis with association rule mining (Apriori.