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audience-builder观众建设者

Agent Skill

audience-builder 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

3,721

周安装

152

GitHub Stars

公开资料未说明

下载量

1,204
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:audience-builder(观众建设者)
来源仓库:https://github.com/leooooooow/audience-builder
安装命令:
openclaw skills install audience-builder
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 audience-builder

简介

通过结合购买行为、兴趣信号、相似建模等,为 Meta、TikTok 和 Google 上的电子商务活动设计目标广告受众。

SKILL.md

name
audience-builder
description
Design targeted ad audiences for ecommerce campaigns across Meta, TikTok, and Google by combining purchase behavior, interest signals, lookalike modeling, and retargeting segments with budget allocation recommendations.

Audience Builder

Most ecommerce advertisers stack generic interest audiences on Meta, broad targeting on TikTok, and one branded search campaign on Google and call it a media plan. Audience Builder helps you design a layered audience architecture across all three platforms so your first-party purchase data, browsing behavior, and lookalike seeds are deployed where each platform actually rewards them.

Use when

  • A brand says "I'm burning ad budget on Meta interest stacks that don't convert — can you rebuild my audience structure from first-party data?"
  • A TikTok Shop seller asks "what audiences should I turn on for Video Shopping Ads versus Product Shopping Ads, and how do I keep them from overlapping?"
  • A DTC founder is launching on Google Ads for the first time and wants to know which customer match lists, in-market segments, and YouTube remarketing audiences to build before spending a dollar.
  • A team is preparing a Q4 media plan and needs a complete audience map: cold prospecting, warm retargeting, and customer re-engagement — with budget splits that reflect funnel stage and platform strengths.

What this skill does

Builds a three-column audience architecture for Meta, TikTok, and Google from your customer file and pixel data. For each platform it lays out prospecting audiences (interest stacks, broad with optimization, in-market segments), seeded lookalikes at multiple similarity percentages, engagement-based warm audiences (video viewers, profile visitors, page engagers), and bottom-of-funnel retargeting segments (viewed product, added to cart, initiated checkout, purchaser exclusions). It then recommends a budget split across funnel stages, flags overlap risks, and specifies exclusion rules so platforms don't compete for the same user.

Inputs required

  • Customer file export (required): at minimum email and purchase history, ideally with AOV, order count, first order date, and product categories.
  • Pixel / conversion API event volume (required): monthly counts for view content, add to cart, initiate checkout, and purchase on each platform you advertise on.
  • Product catalog structure (required): main categories, hero SKUs, bundle SKUs, and which products are allowed to be advertised (platform policy or margin reasons).
  • Current ad accounts and spend (required): monthly spend per platform, current ROAS benchmarks, and any audience saturation issues you've already noticed.
  • Target markets (optional): countries or regions to include and exclude; affects audience size and lookalike source selection.
  • Brand restrictions (optional): any competitor or sensitive-interest exclusions, blocked placements, or creator-content policies.
  • Seasonality goals (optional): upcoming launches, sales events, or inventory clearance windows that should shift audience weighting.

Output format

A platform-by-platform audience map organized by funnel stage, each audience entry including exact setup instructions (what to name it, which source data to upload, which inclusion and exclusion logic to apply, which optimization event to pair it with, and the expected audience size range). Below the audience map, a budget allocation table shows monthly spend split across prospecting, warm, and retargeting on each platform, with reasoning. A final section lists the top three overlap and cannibalization risks with specific exclusion rules to prevent them.

Scope

  • Designed for: ecommerce operators, performance marketers, and agency account managers running multi-platform paid media.
  • Platform context: Meta Ads, TikTok Ads Manager, and Google Ads. Other platforms (Pinterest, Snap) mentioned only as adjacent references.
  • Language: English.

Limitations

  • Does not connect to ad platform APIs; all audience setup must be executed manually in each platform's UI.
  • Lookalike performance depends on platform-side data availability and cannot be predicted with certainty.
  • Does not replace creative strategy — audience architecture sets the stage, but creatives still determine conversion rate.

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

81.69%
按下载量换算984

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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来源信息

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