OpenClaw

roas-forecast-attribution-modeler

Build ROAS forecasting and attribution-model assumptions for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic planning.

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/roas-forecast-attribution-modeler

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

Ads ROAS Forecast

Purpose

Core mission:

forecast scenario modeling, attribution sensitivity, budget recommendation

This skill is specialized for advertising workflows and should output actionable plans rather than generic advice.

When To Trigger

Use this skill when the user asks for:

ad execution guidance tied to business outcomes
growth decisions involving revenue, roas, cpa, or budget efficiency
platform-level actions for: Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic
this specific capability: forecast scenario modeling, attribution sensitivity, budget recommendation

High-signal keywords:

ads, advertising, campaign, growth, revenue, profit
roas, cpa, roi, budget, bidding, traffic, conversion, funnel
meta, googleads, tiktokads, youtubeads, amazonads, shopifyads, dsp

Input Contract

Required:

forecast_target: roas, cpa, or revenue
planning_horizon
base_assumptions

Optional:

attribution_window_options
budget_scenarios
seasonality_factors
risk_tolerance

Output Contract

Model Inputs
Scenario Outputs
Sensitivity Analysis
Attribution Impact Notes
Budget Recommendation

Workflow

Normalize baseline metrics and assumptions.
Build base, upside, and downside scenarios.
Run sensitivity on conversion rate and CPC assumptions.
Compare attribution windows and expected deltas.
Recommend budget path with confidence bounds.

Decision Rules

If assumptions are uncertain, widen forecast intervals and reduce aggressiveness.
If scenario spread is large, recommend phased budget release.
If attribution window drives major variance, present dual-plan decisions.

Platform Notes

Primary scope:

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

Platform behavior guidance:

Keep recommendations channel-aware; do not collapse all channels into one generic plan.
For Meta and TikTok Ad...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/danyangliu-sandwichlab/roas-forecast-attribution-modeler
Version: —
Commit: 878b2bd4a47a65db4172c8aeea0d348b79015109
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

# Ads ROAS Forecast

## Purpose
Core mission:
- forecast scenario modeling, attribution sensitivity, budget recommendation

This skill is specialized for advertising workflows and should output actionable plans rather than generic advice.

## When To Trigger
Use this skill when the user asks for:
- ad execution guidance tied to business outcomes
- growth decisions involving revenue, roas, cpa, or budget efficiency
- platform-level actions for: Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic
- this specific capability: forecast scenario modeling, attribution sensitivity, budget recommendation

High-signal keywords:
- ads, advertising, campaign, growth, revenue, profit
- roas, cpa, roi, budget, bidding, traffic, conversion, funnel
- meta, googleads, tiktokads, youtubeads, amazonads, shopifyads, dsp

## Input Contract
Required:
- forecast_target: roas, cpa, or revenue
- planning_horizon
- base_assumptions

Optional:
- attribution_window_options
- budget_scenarios
- seasonality_factors
- risk_tolerance

## Output Contract
1. Model Inputs
2. Scenario Outputs
3. Sensitivity Analysis
4. Attribution Impact Notes
5. Budget Recommendation

## Workflow
1. Normalize baseline metrics and assumptions.
2. Build base, upside, and downside scenarios.
3. Run sensitivity on conversion rate and CPC assumptions.
4. Compare attribution windows and expected deltas.
5. Recommend budget path with confidence bounds.

## Decision Rules
- If assumptions are uncertain, widen forecast intervals and reduce aggressiveness.
- If scenario spread is large, recommend phased budget release.
- If attribution window drives major variance, present dual-plan decisions.

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

Platform behavior guidance:
- Keep recommendations channel-aware; do not collapse all channels into one generic plan.
- For Meta and TikTok Ad...

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