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 nexscope-ai/eCommerce-Skills --skill supply-chain-optimization-tiktokgit clone --depth 1 https://github.com/nexscope-ai/eCommerce-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/nexscope-ai/ecommerce-skills/supply-chain-optimization-tiktok)<a href="https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/supply-chain-optimization-tiktok"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/supply-chain-optimization-tiktok/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/nexscope-ai/ecommerce-skills/supply-chain-optimization-tiktok"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/supply-chain-optimization-tiktok.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 15 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00057 | $0.01096 |
| Opus 5 | $0.00028 | $0.00548 |
| Sonnet 5 | $0.00011 | $0.00219 |
| Haiku 4.5 | $0.00006 | $0.00110 |
Grade A, and why
supply-chain-optimization-tiktok 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 7d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Supply Chain Optimization — TikTok Shop 📦
Supply chain bottleneck analyzer for TikTok Shop sellers. Diagnose cash flow, inventory, affiliate costs, and return rates.
Installation
npx skills add nexscope-ai/eCommerce-Skills --skill supply-chain-optimization-tiktok -g
Platform Characteristics
| Feature | TikTok Shop | vs Amazon |
|---|---|---|
| Fulfillment | FBT / Self-ship | FBA |
| Commission | 2-8% (category) + 2% transaction | 8-15% |
| Payment cycle | 7-15 days | 14 days |
| Traffic source | Content-driven | Search-driven |
| Return rate | Higher (impulse buying) | Medium |
Cost Structure (TikTok Shop)
Selling Price $XX
├── Product Cost
├── Inbound Shipping
├── FBT Fulfillment / Self-ship
├── Platform Fee (2%)
├── Referral Fee (2-8%)
├── Affiliate Commission (10-30%) ← TikTok-specific
├── Advertising (Spark Ads)
└── Net Profit
Benchmark Configuration
BENCHMARKS = {
"tiktok": {
"gross_margin": {
"healthy": 0.45, # Need to cover affiliate commission
"warning": 0.35,
"danger": 0.25
},
"shipping_ratio": {
"healthy": 0.05,
"warning": 0.08,
"danger": 0.12
},
"inventory_days": {
"healthy": 30, # TikTok viral cycle is short
"warning": 45,
"danger": 60
},
"cash_cycle": {
"healthy": 60, # Fast payment
"warning": 90,
"danger": 120
},
"net_margin": {
"healthy": 0.15, # After affiliate split
"warning": 0.08,
"danger": 0.03
},
# TikTok-specific metrics
"return_rate": {
"healthy": 0.10, # <10% healthy
"warning": 0.20,
"danger": 0.30
},
"affiliate_ratio": {
"healthy": 0.20, # Affiliate commission ratio
"warning": 0.30,
"danger": 0.40
}
}
}
What ships with it
3 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.
- 7d ago First seen · 167 lines · 57 tokens per session scan A f58d0618033b
supply-chain-optimization-tiktok is a skill published in the GitHub repository nexscope-ai/eCommerce-Skills (908 stars, last pushed 16d ago), licensed MIT. It adds 57 tokens to every session and 1,096 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 2026-09-03.
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