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media-buyer-ads-helper媒体买家广告助手

Agent Skill

media-buyer-ads-helper 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

10,593

周安装

437

GitHub Stars

公开资料未说明

下载量

3,461
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:media-buyer-ads-helper(媒体买家广告助手)
来源仓库:https://github.com/danyangliu-sandwichlab/media-buyer-ads-helper
安装命令:
openclaw skills install media-buyer-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 media-buyer-ads-helper

简介

统一管理 Meta、Google、TikTok 等多平台广告投放与账户健康检查。

  • 适用于广告代理、电商运营或品牌推广团队的跨平台投放场景。
  • 支持预算分配、受众定向与 ROI 监控等核心功能。
  • 使用前需授权各平台 API 访问权限,确保数据安全合规。
  • 建议定期检查账户异常活动,防范恶意扣费风险。media-buyer-ads-helper 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
media-buyer-ads-helper
description
Support media buying execution for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and DSP/programmatic with account health checks, bidding efficiency analysis, AB test design, and real-time anomaly monitoring.

Media Buyer Helper

Purpose

Core mission:

  • Evaluate account health and structure quality.
  • Analyze bid logic and budget allocation efficiency.
  • Design AB test architecture and scale model.
  • Monitor campaigns in real time and detect anomalies.

When To Trigger

Use this skill when the user asks for:

  • media buyer execution support
  • bid and budget efficiency diagnostics
  • AB testing structure design
  • live campaign watch and anomaly alerts

High-signal keywords:

  • media, bidding, budget, auction, allocation
  • abtest, campaign, performance, optimize
  • cpa, roas, scale, monitor

Input Contract

Required:

  • account_structure_snapshot
  • bidding_config
  • budget_allocation_snapshot
  • recent_performance_series

Optional:

  • test_history
  • alert_thresholds
  • creative_breakdowns
  • seasonality_notes

Output Contract

  1. Account Health and Structure Score
  2. Bid and Budget Efficiency Findings
  3. AB Test Structure Blueprint
  4. Scale Model with Trigger Conditions
  5. Monitoring and Alert Rules

Workflow

  1. Check account hierarchy and naming hygiene.
  2. Evaluate bid strategy vs KPI objective.
  3. Diagnose budget fragmentation and overlap.
  4. Build AB test matrix with clear success metrics.
  5. Define anomaly thresholds and response playbook.

Decision Rules

  • If structure complexity is high and spend is low, simplify before adding tests.
  • If CPA variance is high, reduce concurrent experiments.
  • If winning cells are statistically weak, extend learning window.
  • If anomaly severity is high, prioritize containment over optimization.

Platform Notes

Primary scope:

  • Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic

Platform behavior guidance:

  • Map bid logic to channel auction mechanics.
  • Keep test isolation strict to avoid cross-cell contamination.

Constraints And Guardrails

  • Do not claim statistical significance without threshold checks.
  • Avoid broad budget jumps without gate conditions.
  • Keep alert rules tied to action ownership.

Failure Handling And Escalation

  • If data granularity is insufficient, request minimum breakdowns.
  • If live anomaly cannot be diagnosed, escalate with incident payload.
  • If policy rejects disrupt test integrity, pause affected cells and reroute budget.

Code Examples

AB Test Matrix

test_id: AB-2026-07 variable: bid_strategy cells: - control: target_cpa - challenger: max_conversion_value success_metric: blended_roas

Anomaly Rule

if spend_spike_pct > 35 and conversions_drop_pct > 25: severity: high action: notify_and_limit_budget

Examples

Example 1: Bid efficiency issue

Input:

  • CPC up, CVR flat

Output focus:

  • bid logic fix
  • budget reallocation
  • test plan

Example 2: AB test setup

Input:

  • Need test for broad vs layered audience

Output focus:

  • clean test architecture
  • significance rule
  • rollout timeline

Example 3: Real-time anomaly

Input:

  • Sudden spend spike in one channel

Output focus:

  • anomaly diagnosis
  • immediate actions
  • escalation path

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

93.72%
按下载量换算3,244

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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