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 AgentComputerAI/torch --skill nikegit clone --depth 1 https://github.com/AgentComputerAI/torchWrote 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/agentcomputerai/torch/nike)<a href="https://agentmods.dev/skills/agentcomputerai/torch/nike"><img src="https://agentmods.dev/badge/skills/agentcomputerai/torch/nike/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/agentcomputerai/torch/nike"><img src="https://agentmods.dev/badge/skills/agentcomputerai/torch/nike.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 | $0.00086 | $0.02305 |
| Opus 5.5 | $0.00034 | $0.00922 |
| Sonnet 5.5 | $0.00017 | $0.00461 |
| Haiku 4.5 | $0.00009 | $0.00231 |
Grade A, and why
nike scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
const res = await fetch(url, { headers }); How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nike (nike.com)
Custom React SPA with SSR bootstrap ("Web Shell"). The product grid hydrates from an internal
api.nike.com/discover/product_wall/v1JSON API. There is no__NEXT_DATA__blob; only the first ~24 cards are server-rendered, the rest load via XHR on scroll.Skip the browser entirely. Replay the API with
fetch. It's public, fast, and anti-bot-free — just send the right headers.
Detection
| Signal | Value |
|---|---|
| Framework | Custom React SPA with "Web Shell" SSR |
| API | api.nike.com/discover/product_wall/v1 — public JSON, cursor-paginated |
| Anti-bot | None on the API (for browsing). Akamai Bot Manager on www.nike.com, but the API bypasses it |
| Auth | Not required for catalog browse. SNKRS and checkout are gated. |
| Rate limit | None observed at ~7 req/s across 132 pages. 150ms sleep between pages is plenty. |
Strategy
Skip browser entirely
→ GET api.nike.com/discover/product_wall/v1/... with nike-api-caller-id header
→ Follow pages.next cursor until empty
→ Flatten productGroupings[].products[] to one row per colorway
Never launch Puppeteer for Nike catalog scraping — the API is faster, cleaner, and has no rate limits.
Endpoint template
US English "new releases" gridwall (/w/new-3n82y):
GET https://api.nike.com/discover/product_wall/v1/marketplace/US/language/en/consumerChannelId/d9a5bc42-4b9c-4976-858a-f159cf99c647
?path=/w/new-3n82y
&attributeIds=53e430ba-a5de-4881-8015-68eb1cff459f
&queryType=PRODUCTS
&anchor=0
&count=24
Parameters:
| Param | Value | Notes |
|---|---|---|
path |
The URL slug (e.g. /w/new-3n82y) |
Identifies the gridwall |
attributeIds |
GUID for the category filter | Sniff once per category (see Gotchas) |
queryType |
PRODUCTS |
Always |
anchor |
0, 24, 48, ... |
Pagination offset, increments by count |
count |
24 |
Required — larger values return HTTP 400 |
For other gridwalls, swap path and attributeIds to the target category's values. Find them by sniffing the page once in devtools.
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.
- 1mo ago First seen · 220 lines · 86 tokens per session scan A c83e107982f9
nike is a skill published in the GitHub repository AgentComputerAI/torch (5 stars, last pushed 6mo ago), licensed MIT. It adds 86 tokens to every session and 2,305 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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Coordinate AgentOS fulfillment and delivery work with milestones, supplier and courier tracking, exception handling, recovery options, Procurement handoffs, escalation approvals, evidence, and receipt closure. Use for order delays, shipment tracking, vendor exceptions, and delivery recovery.
agentos-procurement
Source products and services in AgentOS through scoped requirements, supplier discovery, invite-to-tender, quote comparison, buy-versus-make analysis, purchase proposals, and commercial approval. Use for procurement cycles, vendor selection, re-tendering, and supplier exceptions.
product-specialist
Product and commerce domain coordination for catalog, plans, pricing, inventory, orders, vouchers, fulfillment, and procurement handoffs. Use when designing product specialist sub-agents, routing commerce tasks, defining ownership boundaries, or coordinating product operations.
billing-payments-specialist
Payment authorization, settlement, refunds, credits, invoices, financial ledgers, and billing controls. Use when validating or executing product-related financial operations and payment state transitions.