ads-meta

仓库创建 2026年7月6日最近提交 21 天前SkillHot 收录 21 天前
▸ 精选理由

覆盖深层投放与追踪风险,适合广告运营与审计团队。

▸ 风险提示

会涉及用户事件与追踪配置,可能触及数据隐私合规问题

这个 Skill 做什么

对 Facebook/Instagram/Threads 广告账户做全面的 50 项健康与风险审计。

对 Facebook、Instagram、Threads 广告帐号做一套 50 项的深度健康与风险审计,覆盖 Pixel/CAPI、账号结构、创意多样性、Entity‑ID 聚类风险、ASC/AAC 和 Advantage+ 配置等。适合诊断帐号问题、优化 Sales/Leads/App 投放或评估受众与创意策略时用。特别在 Andromeda+GEM+Lattice 的背景下把技术与创意风险量化,还会把创意当作目标来打分。

▸ 展开 SKILL.md 英文原文

Meta Ads deep analysis covering Facebook, Instagram, and Threads advertising in the Andromeda + GEM + Lattice era. Evaluates 50 checks across Pixel/CAPI health, creative diversity and Entity-ID clustering risk, account structure, ASC/AAC defaults for Sales/Leads/App, and audience targeting. Includes Advantage+ assessment and creative-as-targeting scoring. Use when user says Meta Ads, Facebook Ads, Instagram Ads, Threads ads, Advantage+, ASC, AAC, Andromeda, GEM, Lattice, Entity-ID clustering, creative diversity, Sales optimization, Leads optimization, App optimization, or Meta campaign.

垂直行业Meta审计Pixel/CAPI创意与结构通用
0
Stars
0
Forks
15
仓库内 Skill
+0
7 日增星
安装 / 使用
给你的 Agent 一句话(通用)
帮我安装这个 skill:https://raw.githubusercontent.com/incuca/incuca-ads/main/skills/ads-meta/SKILL.md
或 curl 直取 SKILL.md
curl -fsSL "https://raw.githubusercontent.com/incuca/incuca-ads/main/skills/ads-meta/SKILL.md"
SKILL.MD 节选查看完整文件 ↗
# Meta Ads Deep Analysis

## Andromeda + GEM + Lattice (2026)

Meta's delivery stack was rebuilt across three releases:

- **Andromeda** (Oct 2025) — ad-retrieval ranking model with 10,000× more
  model capacity than the previous funnel ([Meta Engineering, Dec 2024](https://engineering.fb.com/2024/12/02/production-engineering/meta-andromeda-advantage-automation-next-gen-personalized-ads-retrieval-engine/)).
  Filters the candidate creative set before the auction layer ever sees it.
- **GEM** (Generative Embedding Model, late 2025) — replaces the feature
  pipeline. Creative *content* embeds directly into the targeting space, which
  is why "creative is the new targeting" is now mechanical tr
via SKILL·HOT · 数据来自 GitHub 公开信息 · 原文版权归作者所有