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-junglescout-keyword-historygit 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-junglescout-keyword-history)<a href="https://agentmods.dev/skills/linkfox-ai/linkfox-skills/linkfox-junglescout-keyword-history"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-junglescout-keyword-history/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-junglescout-keyword-history"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-junglescout-keyword-history.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk pass
- 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 130 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 | $0.00148 | $0.02498 |
| Opus 5 | $0.00074 | $0.01249 |
| Sonnet 5 | $0.00030 | $0.00500 |
| Haiku 4.5 | $0.00015 | $0.00250 |
Grade A, and why
linkfox-junglescout-keyword-history 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jungle Scout — 关键词历史搜索量
This skill queries the historical exact search volume for Amazon keywords via the Jungle Scout data source, returning weekly search volume data points over a specified date range across 10 Amazon marketplaces.
Core Concepts
Jungle Scout 关键词历史搜索量工具提供亚马逊各站点关键词的周维度精确匹配搜索量历史数据。卖家可以通过查询指定时间范围内的搜索量变化来判断：
- 季节性规律：关键词在哪些月份是旺季/淡季
- 趋势方向：搜索量是持续上升、下降还是平稳
- 波动幅度：判断市场需求的稳定性
- 节假日效应：大促、节日前后的搜索量飙升
数据粒度：每条记录代表一个 7 天周期，包含该周内的精确匹配搜索量估算值。
Data Fields
Output Fields
| Field | API Name | Description | Example |
|---|---|---|---|
| 周期标识 | id | 数据周期标识（市场/关键词/日期范围） | us_sushi_20250105_20250111 |
| 周期开始日期 | estimateStartDate | 7天统计周期的起点 | 2025-01-05 |
| 周期结束日期 | estimateEndDate | 7天统计周期的终点 | 2025-01-11 |
| 精确搜索量 | estimatedExactSearchVolume | 该周期内精确匹配搜索量（次/周） | 12500 |
| 资源类型 | type | 固定值 | historical_keyword_search_volume |
| 消耗Token | costToken | 本次调用消耗的 token 数 | 1 |
Supported Marketplaces
| 站点 | marketplace 值 | 说明 |
|---|---|---|
| 美国 | us | Amazon.com |
| 英国 | uk | Amazon.co.uk |
| 德国 | de | Amazon.de |
| 印度 | in | Amazon.in |
| 加拿大 | ca | Amazon.ca |
| 法国 | fr | Amazon.fr |
| 意大利 | it | Amazon.it |
| 西班牙 | es | Amazon.es |
| 墨西哥 | mx | Amazon.com.mx |
| 日本 | jp | Amazon.co.jp |
默认站点为 us。当用户未指定站点时，使用 us。
调用方式
- API 端点：
POST /tool-jungle-scout/keywords/historical-search-volume（完整参数/响应/错误码见references/api.md） - Python 脚本：
python scripts/junglescout_keyword_history.py '<JSON 参数>' [--inline] - 成本约束：本工具会消耗积分；同一会话同一参数组合默认只调用一次，脚本带 24h 本地缓存。失败/空结果不得自动换关键词、翻页或改邮编连续试探；需要继续检索时先向用户说明会产生额外消耗。
输出策略（脚本默认行为）：
- 始终将完整响应写入
<cwd>/linkfox/<YYYY-MM-DD>/<session>/data/linkfox-junglescout-keyword-history-<timestamp>.json（<cwd>为脚本执行时的工作目录，在 Claude Code 里即当前项目目录；<session>取自环境变量SESSION_ID，按用户任务自动聚合；禁止写入 /tmp，当前目录不可写则报错） - 响应体 ≤ 8 KB：落盘后把完整 JSON 打印到 stdout
- 响应体 > 8 KB：落盘后 stdout 只输出摘要（顶层字段、常见计数如
total/costToken、最大列表字段的长度 + 前 3 条样本） - 加
--inline强制全量打印到 stdout（同样落盘）
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 · 187 lines · 148 tokens per session scan A 78c32277f213
linkfox-junglescout-keyword-history is a skill published in the GitHub repository linkfox-ai/linkfox-skills (101 stars, last pushed 22d ago), licensed MIT. It adds 148 tokens to every session and 2,498 once invoked, about $0.0007 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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