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 deciqAI/knowledge-skills --skill agent-ready-product-datagit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/agent-ready-product-data)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/agent-ready-product-data"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/agent-ready-product-data/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/deciqai/knowledge-skills/agent-ready-product-data"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/agent-ready-product-data.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 | $0.00158 | $0.01905 |
| Opus 5.5 | $0.00063 | $0.00762 |
| Sonnet 5.5 | $0.00032 | $0.00381 |
| Haiku 4.5 | $0.00016 | $0.00191 |
Grade A, and why
agent-ready-product-data 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.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent-Ready Product Data
Overview
An AI shopping agent does not respond to photography, mood or persuasive copy. It reads structured attributes, unambiguous variants, and machine-readable price, stock, shipping and returns. What it cannot parse, it skips; what it parses wrongly, it buys wrongly — and the merchant pays for the return and the complaint.
The core mechanism: the agent funnel is Seen (retrieved) → Understood (attributes and variants are unambiguous) → Bought (price, stock, policies and checkout have no blockers), and each layer is measured and repaired separately. A product that is perfectly described but invisible, or visible but uncheckoutable, loses the sale to the competitor whose data is more complete.
Compose with geo-generative-engine-optimization (getting recommended) · checklist (field completeness as a hard checklist) · theory-of-constraints (fix the narrowest funnel layer first).
When to Use
- Products appear in AI answers, but with stale information, the wrong variant, or the wrong price
- Test orders through an agent stall at variant selection, shipping cost, forced login or a CAPTCHA
- Preparing to submit a product feed, adopt an agent checkout protocol, or list in a platform catalog read by AI assistants
When NOT to use: no online transaction · products require a custom quote · categories where regulation bars automated sale · the problem is brand mentions, not catalog data.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has concrete SKUs and a channel → run The Process directly.
- Coach mode: user is unfamiliar → guide one step at a time.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- What it is: "An agent only trusts fields it can parse. One missing field can cost one order."
- Check fit: "Can a customer buy your product online, directly, without a quote?"
- Elicit the real case: "Pick 3 best-selling SKUs. Send the product-page links and the attributes you most want an agent to understand."
[WAIT — do not advance until user responds]
- Run The Process: "Run an agent through find → compare → add to cart → checkout on each SKU and record every point where it stalls."
[WAIT — do not advance until user responds]
- Name the insight: "Your loss sits in one layer — Seen, Understood or Bought. Fix that layer first."
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 100 lines · 158 tokens per session scan A 2887b2b498ae
agent-ready-product-data is a skill published in the GitHub repository deciqAI/knowledge-skills (13 stars, last pushed 12d ago), licensed MIT. It adds 158 tokens to every session and 1,905 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-10-26.
agent-ready-product-data compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agent-ready-product-datadeciqAI/knowledge-skills | Skill | 13 | 12d ago | MIT | A | 158 | Claude Code, Codex |
| maishouaahl/skills | Skill | 164 | 1mo ago | MIT | A | 103 | Claude Code, Codex |
| run-campaignalebgl77/claude-inc | Skill | 22 | 16d ago | MIT | A | 0 | Claude Code |
| scraperapi-price-monitoringscraperapi/scraperapi-skills | Skill | 10 | 3mo ago | MIT | A | 196 | Claude Code |
| payment-gateway-expertroedyrustam/vibes-plug | Skill | 122 | yesterday | MIT | A | 34 | Cursor |
| amazon-listing-imagesnexscope-ai/Amazon-Skills | Skill | 840 | 2mo ago | MIT | A | 51 | Claude Code, Codex |
| amazon-niche-findernexscope-ai/Amazon-Skills | Skill | 840 | 2mo ago | MIT | A | 74 | Claude Code, Codex |