Token导航 LogoToken导航TokenDH.com
研究检索操作浏览器clawhub未标认证来源可访问clear审计通过

price-gap-monitor价格差距监测

Agent Skill

price-gap-monitor 用于处理浏览器自动化、网页检查和页面信息提取,适合在 OpenClaw 中需要让 Agent 打开页面、读取网页或验证前端流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

9,385

周安装

376

GitHub Stars

公开资料未说明

下载量

3,038
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:price-gap-monitor(价格差距监测)
来源仓库:https://github.com/leooooooow/price-gap-monitor
安装命令:
openclaw skills install price-gap-monitor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install price-gap-monitor

简介

利用浏览器自动化整理市场价格差异与趋势信号。price-gap-monitor 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 用于产品级和类别级的促销变化分析与竞争监测。
  • 提取网页上的价格数据,识别市场波动与机会点。
  • 安装命令:openclaw skills install price-gap-monitor。
  • 可能涉及复杂页面交互,建议测试兼容性后再正式使用。

SKILL.md

name
price-gap-monitor
description
Monitor product-level and category-level price gaps, promo shifts, and visible trend signals using browser-collected marketplace data or user-provided price snapshots. Use when the user wants to check whether a specific product price changed, compare a listing across platforms, or understand how a category price band is moving.

Price Gap Monitor

Track visible price movement without pretending to know private marketplace data.

This skill now supports two operating modes under the same name.

Mode A — Product-level price trend monitoring

Use this mode when the user asks about:

  • one specific product
  • one brand-specific model
  • one ASIN / listing / SKU
  • one named product across multiple platforms

Mode B — Category price-band monitoring

Use this mode when the user asks about:

  • a product category
  • a keyword-defined market
  • a visible price band
  • cross-platform category pricing patterns

Browser-first guidance

When live public pages are available, prefer OpenClaw managed browser for page inspection.

Recommended order:

  1. Use user-provided price snapshots if the user already has structured data.
  2. If page URLs or searchable listings are available, use OpenClaw managed browser to inspect current public pricing and promo signals.
  3. If the target marketplace gates pricing, ranking, or browsing depth behind login friction, explicitly remind the user to log in first so the agent can inspect fuller public results with fewer blockers.
  4. Only use Browser Relay / attached Chrome when the user explicitly asks to inspect their current browser tab.

Do not default to Playwright-style assumptions in the user-facing guidance. The preferred browsing path is OpenClaw managed browser.

Login reminder rule

For marketplaces such as Amazon, trigger a login reminder when any of these conditions appear:

  • search or category pages truncate, block, or degrade result visibility
  • best-seller/category pages fail to load correctly
  • location, cart, or account state is clearly affecting visible listings
  • the task requires going deeper than a shallow guest snapshot

Suggested user-facing reminder:

  • “If you want a cleaner and more complete Amazon read, log in first. Logged-in browsing usually gives more stable category pages, better listing continuity, and fewer interruptions.”

Do not claim login guarantees full data access. Present it as a practical way to improve visibility and continuity.


Core job

The goal is to produce a decision-ready price snapshot with honest trend interpretation.

This skill may use:

  1. user-provided price snapshots, or
  2. browser-collected public marketplace data

It should:

  • collect visible price and promo signals
  • compare listings or price bands
  • distinguish current snapshot from repeated trend evidence
  • recommend whether to watch, react, or gather more data first

It must not fabricate hidden marketplace history, real sales counts, or full coverage when only partial evidence is available.


Inputs

Input type A — user-provided snapshots

  • competitor price tables
  • prior exported marketplace snapshots
  • your current price baseline
  • target margin floor
  • promo windows or campaign timing

Input type B — browser-collected public data

  • a product model name
  • an ASIN / SKU / listing URL
  • a category keyword
  • target platforms (Amazon, Temu, TikTok Shop, Walmart, etc.)
  • market / locale (US, UK, JP, DE, etc.)

Workflow

Mode A — Product-level workflow

  1. Define the exact product scope.
  2. Collect visible public signals.
  3. Normalize comparison points.
  4. Determine evidence strength.
  5. Produce result.

Mode B — Category-level workflow

  1. Define the category scope.
  2. Collect visible top listings.
  3. Cluster the market.
  4. Determine evidence strength.
  5. Produce result.

Trend interpretation rules

  1. Single snapshot rule

- If only one fresh snapshot is available, describe the result as a current price snapshot, not a full historical trend.

  1. Repeated evidence rule

- Only describe an observed trend when supported by repeated visible price points or timestamped snapshots.

  1. Sales honesty rule

- Never claim true sales volume unless the platform explicitly shows sold count. - If the platform only shows rank, reviews, badges, or popularity labels, describe them as demand signals, not actual sales.

  1. Coverage rule

- If only part of the market is visible, clearly label the result as partial coverage. - Never present partial scraping as full category or full brand coverage.

  1. History rule

- Never fabricate prior price history. - Never imply long-term movement when only current public pages were checked once.


Output format

For Mode A — product-level

  1. Executive summary (max 5 lines)
  2. Current product snapshot
  3. Cross-platform comparison
  4. Observed change or “insufficient trend history”
  5. Risk / anomaly note
  6. Recommended action (watch / act / gather more data)

For Mode B — category-level

  1. Executive summary (max 5 lines)
  2. Current category price-band snapshot
  3. Platform comparison
  4. Observed band shift or “insufficient trend history”
  5. Noise vs real movement note
  6. Recommended action (watch / act / gather more data)

Quality and safety rules

  • Never recommend below the stated margin floor unless explicitly allowed.
  • Avoid reacting to one-off noisy listing anomalies.
  • Label uncertainty honestly.
  • If browser results are thin or ambiguous, say so directly.
  • Do not backfill missing marketplace data with guesses.

Creatop handoff

If the result is strong enough to act on, pass forward:

  • accepted pricing actions
  • watchlist items
  • promo timing notes
  • category price anchors
  • cross-platform spread observations

License

Copyright (c) 2026 Razestar.

This skill is provided under CC BY-NC-SA 4.0 for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license.

Commercial use requires a separate paid commercial license from Razestar. No trademark rights are granted.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

88.67%
按下载量换算2,694

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills