OpenClaw

attribution-ads-helper

Build cross-channel attribution analysis and decision guidance for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic campaigns.

danyangliu-sandwichlab0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install danyangliu-sandwichlab/attribution-ads-helper

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Attribution Helper

Purpose

Core mission:

Diagnose attribution discrepancies across channels.
Compare attribution window assumptions and their budget impact.
Build practical attribution decision framework for optimization.
Produce actionable attribution-aligned allocation guidance.

When To Trigger

Use this skill when the user asks for:

attribution model comparison
conflicting ROAS/CAC by channel
budget decisions under attribution uncertainty
tracking and model interpretation support

High-signal keywords:

attribution, tracking, model, predict
roas, cpa, revenue, allocation, budget
meta, googleads, tiktokads, youtubeads, dsp

Input Contract

Required:

channel_metrics_by_window
attribution_windows
conversion_event_definitions
decision_context

Optional:

offline_conversion_data
holdout_or_incrementality_data
MMM_or_ltv_inputs
confidence_threshold

Output Contract

Attribution Mismatch Map
Window Sensitivity Analysis
Decision-safe KPI View
Budget Reallocation Recommendation
Validation Experiment Plan

Workflow

Normalize event and conversion definitions.
Compare performance under each attribution window.
Quantify decision deltas from model differences.
Propose allocation with confidence labeling.
Output validation experiments for unresolved gaps.

Decision Rules

If attribution views diverge materially, use blended guardrail plan.
If one channel is highly view-through sensitive, reduce reliance on last-touch only.
If incremental evidence exists, prioritize it over proxy metrics.
If uncertainty remains high, allocate budget in capped test tranches.

Platform Notes

Primary scope:

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

Platform behavior guidance:

Keep window comparisons explicit per channel.
Separate platform-reported and unified-attribution decisions.

Constraints And Guardrails

Never mix inconsisten...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/danyangliu-sandwichlab/attribution-ads-helper
Version: —
Commit: 244bd71c29acd066716a9c30fb1584ef8ea996be
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Attribution Helper

## Purpose
Core mission:
- Diagnose attribution discrepancies across channels.
- Compare attribution window assumptions and their budget impact.
- Build practical attribution decision framework for optimization.
- Produce actionable attribution-aligned allocation guidance.

## When To Trigger
Use this skill when the user asks for:
- attribution model comparison
- conflicting ROAS/CAC by channel
- budget decisions under attribution uncertainty
- tracking and model interpretation support

High-signal keywords:
- attribution, tracking, model, predict
- roas, cpa, revenue, allocation, budget
- meta, googleads, tiktokads, youtubeads, dsp

## Input Contract
Required:
- channel_metrics_by_window
- attribution_windows
- conversion_event_definitions
- decision_context

Optional:
- offline_conversion_data
- holdout_or_incrementality_data
- MMM_or_ltv_inputs
- confidence_threshold

## Output Contract
1. Attribution Mismatch Map
2. Window Sensitivity Analysis
3. Decision-safe KPI View
4. Budget Reallocation Recommendation
5. Validation Experiment Plan

## Workflow
1. Normalize event and conversion definitions.
2. Compare performance under each attribution window.
3. Quantify decision deltas from model differences.
4. Propose allocation with confidence labeling.
5. Output validation experiments for unresolved gaps.

## Decision Rules
- If attribution views diverge materially, use blended guardrail plan.
- If one channel is highly view-through sensitive, reduce reliance on last-touch only.
- If incremental evidence exists, prioritize it over proxy metrics.
- If uncertainty remains high, allocate budget in capped test tranches.

## Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic

Platform behavior guidance:
- Keep window comparisons explicit per channel.
- Separate platform-reported and unified-attribution decisions.

## Constraints And Guardrails
- Never mix inconsisten...

搜索