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Jungle Scout-关键词历史

Jungle Scout关键词历史搜索量查询，按7天周期返回亚马逊关键词的精确搜索量趋势，覆盖美国、英国、德国、日本等10个站点。当用户提到关键词搜索量趋势、历史搜索量、搜索热度变化、关键词季节性、搜索量波动、Jungle Scout搜索量、keyword search volume history, keyword trend, search volume over time, seasonal search volume, keyword popularity trend时触发此技能。即使用户未明确提及"Jungle Scout"，只要其需求涉及查看某个亚马逊关键词在一段时间内的搜索量变化趋势，也应触发此技能。 Skill: Jungle Scout-关键词历史 Owner: linkfox-ai Summary: Jungle Scout关键词历史搜索量查询，按7天周期返回亚马逊关键词的精确搜索量趋势，覆盖美国、英国、德国、日本等10个站点。当用户提到关键词搜索量趋势、历史搜索量、搜索热度变化、关键词季节性、搜索量波动、Jungle Scout搜索量、keyword search volume history, keyword trend, search volume over time, seasonal search volume, keyword popularity trend时触发此技能。即使用户未明确提及"Jungle Scout"，只要其需求涉及查看某个亚马逊关键词在一段时间内的搜索量变化趋势，也应触发此技能。 Tags: latest:1.0.7 Version history: v1.0.7 | 2026-08-14

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Downloads

1.5k

Updated

Oct 10, 2026

Version

1.0.7

Source

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About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.5K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.5K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.7release · observed Aug 14, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s171g8b6m2khwdy9ye8bxj0wx183vd4z:linkfox-junglescout-keyword-history

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-linkfox-ai-linkfox-junglescout-keyword-history/snapshot"

Documentation

CLAWHUB

93,919 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: linkfox-junglescout-keyword-history
description: Jungle Scout关键词历史搜索量查询，按7天周期返回亚马逊关键词的精确搜索量趋势，覆盖美国、英国、德国、日本等10个站点。当用户提到关键词搜索量趋势、历史搜索量、搜索热度变化、关键词季节性、搜索量波动、Jungle Scout搜索量、keyword search volume history, keyword trend, search volume over time, seasonal search volume, keyword popularity trend时触发此技能。即使用户未明确提及"Jungle Scout"，只要其需求涉及查看某个亚马逊关键词在一段时间内的搜索量变化趋势，也应触发此技能。
---

# 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）

**读数据建议**：先看摘要判断是否足够；需要具体字段时优先用 `jq`或`ConvertFrom-Json` 从保存的 json 文件按需抽取，避免整份 JSON 进入上下文。

## 解决认证和积分问题
发生以下异常情况时，采用 references/onboarding.md 引导解决问题：

### 异常情况
- **未配置API Key**：环境变量未配置 `LINKFOX_AGENT_API_KEY`，也未配置 `LINKFOXAGENT_API_KEY`。
- **响应401或402状态码**
- **响应提示积分或余额不足**：消息含"积分余额不足/计费不足/余额不足/quota exceeded/insufficient balance/套餐到期/需充值/请充值"，或类似含义的内容。

## How to Build Queries

所有四个参数均为**必填**：`marketplace`、`keyword`、`startDate`、`endDate`。

### Principles for Building API Calls

1. **站点映射**：用户说"美国站"→ `us`，"日本站"→ `jp`，"德国站"→ `de`；未指定时默认 `us`
2. **日期格式**：必须为 `YYYY-MM-DD`，如 `2025-01-05`
3.

_meta.json

{
  "ownerId": "kn7dmc1t4j28hem1twwyav85p182pb1j",
  "slug": "linkfox-junglescout-keyword-history",
  "version": "1.0.7",
  "publishedAt": 1786719322159
}

references/api.md

# Jungle Scout 关键词历史搜索量 API 参考

## 调用规范

- **请求地址**：`${LINKFOX_TOOL_GATEWAY}/tool-jungle-scout/keywords/historical-search-volume`
- **请求方式**：POST，Content-Type: application/json
- **认证方式**：Header `Authorization: <api_key>`，api_key 从环境变量 `LINKFOX_AGENT_API_KEY` 或 `LINKFOXAGENT_API_KEY` 读取（如未配置 按 SKILL.md 的 **## 解决认证和积分问题** 处理）

## 请求参数

POST Body（JSON）：

| 参数 | 类型 | 必填 | 说明 |
|------|------|------|------|
| marketplace | string | 是 | 目标市场代码。可选值：`us`、`uk`、`de`、`in`、`ca`、`fr`、`it`、`es`、`mx`、`jp` |
| keyword | string | 是 | 要查询的关键词 |
| startDate | string | 是 | 开始日期（格式：YYYY-MM-DD） |
| endDate | string | 是 | 结束日期（格式：YYYY-MM-DD）；与 startDate 间隔最大 366 天 |

### 站点映射

| 站点 | marketplace 值 |
|------|---------------|
| 美国 | us |
| 英国 | uk |
| 德国 | de |
| 印度 | in |
| 加拿大 | ca |
| 法国 | fr |
| 意大利 | it |
| 西班牙 | es |
| 墨西哥 | mx |
| 日本 | jp |

## 响应结构

| 字段 | 类型 | 说明 |
|------|------|------|
| costToken | integer | 消耗 token 数 |
| historicalSearchVolumeList | array | 历史搜索量周期列表 |

### historicalSearchVolumeList 数组中每个对象

| 字段 | 类型 | 说明 |
|------|------|------|
| id | string | 数据周期标识（市场/关键词/日期范围） |
| estimateStartDate | string | 周期开始日期（YYYY-MM-DD，7天统计周期起点） |
| estimateEndDate | string | 周期结束日期（YYYY-MM-DD，7天统计周期终点） |
| estimatedExactSearchVolume | integer | 该周期内精确匹配搜索量（次/周） |
| type | string | 资源类型，固定值 `historical_keyword_search_volume` |

## 错误码

正常情况下，接口的 HTTP 状态码均为 200，业务的成功与否通过响应体中的 errorCode 字段区分（errorCode = 200 表示成功，其他值表示业务错误）。当遇到未授权等情况时，HTTP 状态码为 401，且对应的 errorCode 也是 401。

| errcode | 含义 | 处理建议 |
|---------|------|----------|
| 200 | 成功 | 正常解析 `historicalSearchVolumeList` |
| 401 | 认证失败 | HTTP 401 或 authorized error：按 SKILL.md 的 **## 解决认证和积分问题** 处理。 |
| 402 | 积分/余额不足 | HTTP 402：按 SKILL.md 的 **## 解决认证和积分问题** 处理。 |
| 其他非200值 | 业务异常 | 参考 `errmsg` 字段获取具体错误原因 |

错误响应示例：

```json
{
    "errcode": 401,
    "errmsg": "authorized error"
}
```

## curl 示例

```bash
curl -X POST https://tool-gateway.linkfox.com/tool-jungle-scout/keywords/historical-search-volume \
  -H "Authorization: $LINKFOXAGENT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"marketplace": "us", "keyword": "yoga mat", "startDate": "2025-10-01", "endDate": "2026-03-31"}'
```

## 响应示例

```json
{
  "costToken": 1,
  "historicalSearchVolumeList": [
    {
      "id": "us_yoga_mat_20251005_20251011",
      "estimateStartDate": "2025-10-05",
      "estimateEndDate": "2025-10-11",
      "estimatedExactSearchVolume": 85420,
      "type": "historical_keyword_search_volume"
    },
    {
      "id": "us_yoga_mat_20251012_20251018",
      "estimateStartDate": "2025-10-12",
      "estimateEndDate": "2025-10-18",
      "estimatedExactSearchVolume": 87650,
      "type": "historical_keyword_search_volume"
    }
  ]
}
```

---

## Feedback API

> This endpoint is **separate** from the tool API above. Do not mix the two base URLs.

- **POST** `https://skill-api.linkfox.com/api/v1/public/feedback`
- **Co

references/onboarding.md

# 解决认证和积分问题

调用本 skill 时若网关返回 **auth** 或 **billing** 错误，走本 skill 自带的 `scripts/onboarding.py` 完成引导。

**auth 场景**：`errcode=401` 或消息含 `authorized error`/`鉴权失败`/`未授权`/`unauthorized`；或 `LINKFOX_AGENT_API_KEY` 与 `LINKFOXAGENT_API_KEY` 均为空。
1. 若已配置 key → 先让用户重启会话（最常见误判），仍失败让用户重新取 key 或换手机号重注册
2. 未配置 → 询问：自助去 https://agent.linkfox.com/ 取 key，或提供手机号让脚本注册
3. 手机号路径：
   - `python scripts/onboarding.py send-code <phone>` → 展示 JSON 里的 phone/agreements
   - 收到验证码后：`python scripts/onboarding.py login <phone> <code>`（workbuddy 宿主加 `--channel workbuddy`）
   - 拿到 `api_key` 后把下面三平台配置转发给用户，提示重启会话生效：
     - Windows PowerShell（永久）：`setx LINKFOX_AGENT_API_KEY "<key>"`
     - macOS zsh：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.zshrc && source ~/.zshrc`
     - Linux bash：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.bashrc && source ~/.bashrc`
     - 变量名 `LINKFOX_AGENT_API_KEY`（主推）或 `LINKFOXAGENT_API_KEY`（老规范）任一即可

**billing 场景**：`errcode=402` 或消息含 `积分/余额/quota/insufficient/充值/套餐到期`。
- `python scripts/onboarding.py list-plans` → 有 AskUserQuestion 就弹菜单，否则输出编号清单让用户选
- 校验 `plan_id` ∈ 清单、支付方式 ∈ 该套餐 `available_methods`（通常 `wechat/alipay`）
- `python scripts/onboarding.py order <plan_id> <method>` → 展示优先级 PNG > `pay_url` > `ascii_qr`（标注兜底）
- 已付款可选调 `python scripts/onboarding.py query <order_id>`，不主动轮询

排除 `errcode=403`（无权限，不归入这两类）。所有子命令输出 stdout JSON，`error` 字段已含阶段前缀，透传给用户即可。完整用法：`python scripts/onboarding.py --help`。

skill-card.md

## Description:

Jungle Scout关键词历史搜索量查询，按7天周期返回亚马逊关键词的精确搜索量趋势，覆盖美国、英国、德国、日本等10个站点。

This skill is ready for commercial/non-commercial use.

## Publisher:

[linkfox-ai](https://clawhub.ai/user/linkfox-ai)

### License/Terms of Use:

MIT-0

## Use Case:

Amazon sellers and ecommerce analysts use this skill to query Jungle Scout historical exact-match keyword search volume and evaluate seasonal demand, trend direction, and peak or low periods across supported Amazon marketplaces.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The workflow can consume paid LinkFox/Jungle Scout credits.

Mitigation: Confirm user intent before repeated, expanded, or exploratory queries, and disclose that the skill documentation lists a 64-credit cost.

Risk: Account onboarding and billing flows can handle phone-code login, API keys, package selection, and payment orders.

Mitigation: Run onboarding or payment commands only after explicit user request, and avoid storing API keys in shell profiles on shared machines.

Risk: Automatic feedback reporting can send interaction details to LinkFox without a separate confirmation.

Mitigation: Do not include sensitive user or business details in feedback content, and disclose feedback submission when it is relevant to the user.

Risk: Keyword query results and cache files are persisted locally under a linkfox data directory.

Mitigation: Use the saved files only for the current task, and review or remove local data on shared workspaces when queries may reveal sensitive product research.

## Reference(s):

- [Jungle Scout keyword history API reference](references/api.md)
- [Authentication and billing onboarding](references/onboarding.md)
- [ClawHub skill page](https://clawhub.ai/linkfox-ai/skills/linkfox-junglescout-keyword-history)

## Skill Output:

**Output Type(s):** [text, markdown, JSON, shell commands, configuration, guidance]

**Output Format:** [JSON API responses with optional Markdown tables, trend summaries, shell commands, and configuration guidance.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [The query result is saved under a local linkfox session data directory; large responses are summarized unless full inline output is requested.]

## Skill Version(s):

1.0.7 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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