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PythonReactSEOPDF
OpenClaw

funnel-ads-helper

Diagnose and optimize full conversion funnels for paid traffic from Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads campaigns.

danyangliu-sandwichlab0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install danyangliu-sandwichlab/funnel-ads-helper

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Funnel Helper

Purpose

Core mission:

  • Analyze conversion funnel drop-off by stage.
  • Identify bottlenecks from ad click to checkout or lead submit.
  • Recommend stage-specific optimization actions.
  • Define funnel experiment roadmap and expected impact.

When To Trigger

Use this skill when the user asks for:

  • conversion funnel diagnosis
  • CVR optimization planning
  • landing page and checkout improvement sequence
  • funnel experiment design tied to ROAS/CPA goals

High-signal keywords:

  • conversion, funnel, checkout, cvr
  • cpa, roas, traffic, landing page
  • campaign, optimize, retarget

Input Contract

Required:

  • funnel_stage_metrics
  • traffic_source_breakdown
  • conversion_goal
  • observation_window

Optional:

  • session_replay_notes
  • form_or_checkout_logs
  • segment_breakdowns
  • experiment_history

Output Contract

  1. Funnel Stage Health Scorecard
  2. Bottleneck Priority Ranking
  3. Optimization Actions by Stage
  4. Experiment Roadmap with KPI impact
  5. Monitoring and Iteration Rules

Workflow

  1. Normalize funnel definitions and stage metrics.
  2. Rank drop-off severity and opportunity size.
  3. Map root causes (message mismatch, UX friction, trust gap, etc.).
  4. Recommend stage-specific actions and experiments.
  5. Define monitoring thresholds and iteration cadence.

Decision Rules

  • If top-funnel CTR is strong but CVR is weak, prioritize LP and checkout fixes.
  • If add-to-cart is strong but purchase is weak, prioritize trust/payment friction fixes.
  • If retargeting conversion is low, review audience freshness and offer relevance.
  • If funnel data is sparse, run diagnostic experiments before major redesign.

Platform Notes

Primary scope:

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

Platform behavior guidance:

  • Keep funnel interpretation tied to traffic intent by channel.
  • Distinguish ad-side and on-site bottlenecks before action.

Constraints And Guardrails

  • Do not infer funnel causes without stage-l...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/danyangliu-sandwichlab/funnel-ads-helper
Version
—
Commit
3edce7e5d0144cda5f062d2726a153a6a6d8a73f
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Funnel Helper

## Purpose
Core mission:
- Analyze conversion funnel drop-off by stage.
- Identify bottlenecks from ad click to checkout or lead submit.
- Recommend stage-specific optimization actions.
- Define funnel experiment roadmap and expected impact.

## When To Trigger
Use this skill when the user asks for:
- conversion funnel diagnosis
- CVR optimization planning
- landing page and checkout improvement sequence
- funnel experiment design tied to ROAS/CPA goals

High-signal keywords:
- conversion, funnel, checkout, cvr
- cpa, roas, traffic, landing page
- campaign, optimize, retarget

## Input Contract
Required:
- funnel_stage_metrics
- traffic_source_breakdown
- conversion_goal
- observation_window

Optional:
- session_replay_notes
- form_or_checkout_logs
- segment_breakdowns
- experiment_history

## Output Contract
1. Funnel Stage Health Scorecard
2. Bottleneck Priority Ranking
3. Optimization Actions by Stage
4. Experiment Roadmap with KPI impact
5. Monitoring and Iteration Rules

## Workflow
1. Normalize funnel definitions and stage metrics.
2. Rank drop-off severity and opportunity size.
3. Map root causes (message mismatch, UX friction, trust gap, etc.).
4. Recommend stage-specific actions and experiments.
5. Define monitoring thresholds and iteration cadence.

## Decision Rules
- If top-funnel CTR is strong but CVR is weak, prioritize LP and checkout fixes.
- If add-to-cart is strong but purchase is weak, prioritize trust/payment friction fixes.
- If retargeting conversion is low, review audience freshness and offer relevance.
- If funnel data is sparse, run diagnostic experiments before major redesign.

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

Platform behavior guidance:
- Keep funnel interpretation tied to traffic intent by channel.
- Distinguish ad-side and on-site bottlenecks before action.

## Constraints And Guardrails
- Do not infer funnel causes without stage-l...

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