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shopify-ads-helperShopify ADS 助手

Agent Skill

shopify-ads-helper 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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

30,820

周安装

1,259

GitHub Stars

公开资料未说明

下载量

9,871
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:shopify-ads-helper(Shopify ADS 助手)
来源仓库:https://github.com/danyangliu-sandwichlab/shopify-ads-helper
安装命令:
openclaw skills install shopify-ads-helper
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 shopify-ads-helper

简介

该技能优化 Meta、Google Ads、TikTok 等平台的 Shopify 专属广告投放流程。

  • 通过事件完整性校验与 ROAS 监控提升付费增长效率。
  • 可自动生成投放建议报告指导素材创意与出价策略调整。
  • 需接入各广告平台 API 并配置 OAuth 认证保障数据安全传输。
  • 避免频繁修改账户结构以免触发反作弊机制导致限流处罚。

SKILL.md

name
shopify-ads-helper
description
Optimize Shopify-specific paid growth workflows for Meta (Facebook/Instagram), Google Ads, TikTok Ads, and YouTube Ads with Shopify event integrity, ROAS diagnostics, scale planning, and funnel optimization.

Shopify Ads Helper

Purpose

Core mission:

  • Verify Shopify pixel and attribution status for conversion optimization readiness.
  • Diagnose account structure and creative performance affecting ROAS.
  • Recommend scaling route and budget increments.
  • Improve landing page and conversion funnel outcomes.

When To Trigger

Use this skill when the user asks for:

  • Shopify ads troubleshooting
  • OCPX readiness in Shopify stack
  • ROAS fluctuation diagnosis in store campaigns
  • checkout and funnel optimization

High-signal keywords:

  • shopifyads, shop, ecommerce, checkout
  • pixel, attribution, tracking, campaign
  • roas, cpa, budget, scale, funnel

Input Contract

Required:

  • shopify_store_url
  • tracking_stack_summary
  • channel_performance_snapshot
  • checkout_metrics

Optional:

  • product_margin_data
  • collection_level_performance
  • creative_breakdown
  • shipping_policy_context

Output Contract

  1. Shopify Tracking Health Summary
  2. Channel and Structure Diagnosis
  3. ROAS Volatility Interpretation
  4. Scale Path with Budget Gates
  5. Checkout and Funnel Optimization Plan

Workflow

  1. Validate Shopify event mapping consistency.
  2. Confirm attribution alignment across channels.
  3. Audit campaign architecture per product/collection.
  4. Isolate ROAS drivers by audience/creative/offer.
  5. Propose staged budget lift and funnel fixes.

Decision Rules

  • If checkout completion is weak, prioritize on-site fixes over spend expansion.
  • If collection-level margins vary, apply differential bid and budget controls.
  • If attribution mismatch is high, rely on blended and platform views in parallel.
  • If shipping/offer changes happened recently, separate pre/post effects before action.

Platform Notes

Primary scope:

  • Shopify Ads ecosystem with Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads

Platform behavior guidance:

  • Keep Shopify conversion events normalized before cross-platform optimization.
  • Pair channel decisions with collection-level economics when available.

Constraints And Guardrails

  • Do not optimize on ROAS alone when margin is uneven across SKUs.
  • Flag data-lag effects for short lookback windows.
  • Keep budget changes incremental under attribution uncertainty.

Failure Handling And Escalation

  • If event schema is broken, return patch checklist and freeze scale actions.
  • If Shopify app conflicts affect tracking, escalate with app inventory and event log.
  • If billing or policy lock appears, route to platform owner with urgency level.

Code Examples

Shopify Event Mapping Check

events: product_view: ViewContent add_to_cart: AddToCart checkout_start: InitiateCheckout order_paid: Purchase status: pass_with_warnings

Budget Lift Gate

if blended_roas >= 2.8 and checkout_cvr >= 2.2: increase_budget_pct: 15 else: hold_budget: true

Examples

Example 1: Shopify ROAS drift

Input:

  • ROAS unstable after theme update

Output focus:

  • tracking validation
  • cause isolation
  • corrective actions

Example 2: Collection scaling

Input:

  • One collection outperforming others

Output focus:

  • collection-level budget logic
  • structure recommendations
  • risk guardrails

Example 3: Checkout drop

Input:

  • Add-to-cart steady, purchase down

Output focus:

  • checkout funnel fixes
  • retargeting adjustment
  • measurement checks

Quality Checklist

  • [ ] Required sections are complete and non-empty
  • [ ] Trigger keywords include at least 3 registry terms
  • [ ] Input and output contracts are operationally testable
  • [ ] Workflow and decision rules are capability-specific
  • [ ] Platform references are explicit and concrete
  • [ ] At least 3 practical examples are included

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

77.25%
按下载量换算7,625

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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

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