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 product-huntergit 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/product-hunter)<a href="https://agentmods.dev/skills/ertinox7711/sgrr-agi-v2/product-hunter"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/product-hunter/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/product-hunter"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/product-hunter.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 | $0.00085 | $0.01011 |
| Opus 5 | $0.00043 | $0.00505 |
| Sonnet 5 | $0.00017 | $0.00202 |
| Haiku 4.5 | $0.00009 | $0.00101 |
Grade A, and why
product-hunter 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 3d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Hunter — recherche + validation produit methode ()
Overview
Trouver et valider des produits e-commerce EN DATA, jamais à l'intuition. Source de vérité = C:\Users\YOU\Documents\BUSINESS\shopify\validation\ (grilles reconstruites depuis la formation) + docs/coach-doctrine.md. Un produit n'existe que s'il a une fiche remplie et un verdict chiffré.
Pipeline obligatoire (dans l'ordre, aucun saut)
- Découverte : appliquer 1-2 des 9 méthodes de
validation/00-METHODE-COMPLETE.md(Flippa filtres exacts, Amazon Movers & Shakers, BigBuy, Vevor best-sellers, Pinterest Trends, Temu, Europages, DotMarket, Cdiscount). Sortie = liste brute, zéro jugement. - Pré-filtre 30 s/produit : prix ≥ 300 € plausible · trouvable recherche image AliExpress · requête précise · zéro institutionnel dans les ads. Échec = poubelle immédiate.
- Métriques : lancer
python scripts/validation-probe.py "<keyword>" --gl <pays> --hl <langue> --price X --cost Y [--cpc Z](cwd =C:\Users\YOU\Documents\BUSINESS\shopify). PAS optionnel et ne demande AUCUN navigateur : le script rend la SERP via Scrapling et compte les blocs d'annonces (id="tads") + extrait les domaines concurrents — « pas de navigateur dans cette session » n'est PAS une excuse valable. Volume/CPC précis = Keyword Planner (compte <ADS_ACCOUNT_ID>) ; suivrevalidation/04-METRIQUES-PROTOCOLE.md. Toute métrique non vérifiée = marquée[estimé], jamais présentée comme un fait. - Concurrents :
validation/03-CONCURRENTS-SANS-SEMRUSH.md(Transparency Center, opérateurs Google, Shopping tab). Compter les drop (2-6 = idéal). - Grille + verdict : remplir UNE fiche
validation/produits/_TEMPLATE.mdPAR produit. Test S1-S5 (01-GRILLE-SEARCH.md) et P1-P5 (02-GRILLE-SHOPPING.md) → score /105 → verdict GO / RISKY / DROP en une phrase.
Gates durs (un seul déclenché = DROP, peu importe le score)
Marge < 50 % (GO ≥ 65 %) · institutionnels dans les ADS · recherche de marque cachée (« chaise de bureau de luxe » = Herman Miller) · volume < 20-30k/mois cumulé SAUF prix > 500 € · introuvable AliExpress · CPA worst (CPC × 150) ≥ MARGE en € — comparer à la MARGE, jamais au prix de vente · cycle d'achat > 1 mois.
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.
- 3d ago First seen · 39 lines · 85 tokens per session scan A 740084bf3092
product-hunter is a skill published in the GitHub repository Ertinox7711/SGRR-AGI-V2 (1 stars, last pushed 4d ago), licensed MIT. It adds 85 tokens to every session and 1,011 once invoked, about $0.0004 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
should-i-buy
Helps the user decide on a purchase by taking the product links they're considering, asking a couple of sharp clarifying questions about their needs and circumstances, then opening each link in real Chrome via the Claude-in-Chrome extension to extract price, specs, ratings, reviews, return policy, and shipping.…
shop-research
Researches products across Amazon, Google Shopping, and relevant specialty sites using the Claude-in-Chrome browser extension. Finds candidates matching user-specified criteria (gift, gadget, gear, home goods, etc.), captures screenshots and key data per candidate, then generates a modern 2026 HTML report with…
workflow-optimization
Read the customer request, account record, and supplied policies before choosing an action. Policy overrides the customer's requested remedy.
apparel-demand
Analyzes apparel demand prediction systems for trend forecasting, size curve optimization, color and style analytics, sell-through rate tracking, and markdown optimization following CPFR collaborative planning and GTIN product identification standards..
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.