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 linkfox-ai/linkfox-skills --skill linkfox-fastmoss-product-rank-top-sellinggit clone --depth 1 https://github.com/linkfox-ai/linkfox-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/linkfox-ai/linkfox-skills/linkfox-fastmoss-product-rank-top-selling)<a href="https://agentmods.dev/skills/linkfox-ai/linkfox-skills/linkfox-fastmoss-product-rank-top-selling"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-fastmoss-product-rank-top-selling/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/linkfox-ai/linkfox-skills/linkfox-fastmoss-product-rank-top-selling"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-fastmoss-product-rank-top-selling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high System Prompt Leakage · line 125 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00162 | $0.02648 |
| Opus 5 | $0.00081 | $0.01324 |
| Sonnet 5 | $0.00032 | $0.00530 |
| Haiku 4.5 | $0.00016 | $0.00265 |
Grade A, and why
linkfox-fastmoss-product-rank-top-selling 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 9d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastMoss - TikTok Top Selling Rankings
This skill guides you on how to query and analyze the TikTok top selling product rankings via the FastMoss data source, helping cross-border e-commerce sellers identify hot-selling products across TikTok's global markets by day, week, or month.
Core Concepts
The TikTok Top Selling Ranking tracks the best-performing products on TikTok Shop across 9 global markets. It reveals which products are leading in sales volume, GMV, and growth rate over configurable time windows (daily, weekly, monthly). This is an essential tool for product scouting, trend analysis, and competitive intelligence in TikTok e-commerce.
Data scope: The ranking covers 9 TikTok Shop markets and supports three time granularities — day, week, and month — via the dateInfo parameter. Each product entry includes sales volume, GMV, growth rate, commission rate, shop information, category, and more.
dateInfo format is important:
- type:
"day"-> value:"2025-02-01"(YYYY-MM-DD) - type:
"week"-> value:"2025-18"(year-weekNumber) - type:
"month"-> value:"2025-02"(year-month)
Pagination: Results are paginated. Use page (page number, starting from 1) and pageSize (items per page, max 10, default 10) to navigate through the result set.
Parameter Guide
| Parameter | Type | Required | Description |
|---|---|---|---|
| region | string | Yes | Market region code (US, GB, MX, ES, ID, VN, MY, TH, PH) |
| dateInfo | object | Yes | Date specification with type (day/week/month) and value (see format above) |
| category | string | No | Category name in English, matched to TikTok category ID. Non-English input should be translated first |
| orderby | object | No | Sorting: field (units_sold/gmv/total_units_sold/total_gmv/growth_rate) + order (desc/asc). Default: desc |
| page | integer | No | Page number, default 1 |
| pageSize | integer | No | Items per page, max 10, default 10 |
Supported Markets
What ships with it
4 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.
- 9d ago First seen · 185 lines · 162 tokens per session scan A 1e07a4aa2c35
linkfox-fastmoss-product-rank-top-selling is a skill published in the GitHub repository linkfox-ai/linkfox-skills (101 stars, last pushed 22d ago), licensed MIT. It adds 162 tokens to every session and 2,648 once invoked, about $0.0008 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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