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// Skill profile

Funnel Helper

name: funnel-ads-helper

by danyangliu-sandwichlab · published 2026-03-22

数据处理自动化任务加密货币
Total installs
0
Stars
★ 0
Last updated
2026-03
// Install command
$ claw add gh:danyangliu-sandwichlab/danyangliu-sandwichlab-funnel-ads-helper
View on GitHub
// Full documentation

---

name: funnel-ads-helper

description: 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.

---

# 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-level evidence.
  • Keep test queue prioritized by expected impact and effort.
  • Avoid simultaneous high-impact changes that break attribution clarity.

    • Failure Handling And Escalation

  • If stage definitions are inconsistent, output a canonical funnel mapping first.
  • If missing checkout data blocks diagnosis, request minimum event payload.
  • If conversion drops sharply during active changes, trigger rollback review.

    • Code Examples

    Funnel Health Schema

    stages:

    - impression_to_click

    - click_to_viewcontent

    - viewcontent_to_addtocart

    - addtocart_to_checkout

    - checkout_to_purchase

    primary_metric: stage_cvr

    Bottleneck Prioritization Rule

    impact_score = dropoff_pct * traffic_volume * margin_weight

    sort_by: impact_score_desc

    Examples

    Example 1: CVR collapse

    Input:

  • Click volume stable, purchases down

    • Output focus:

  • stage bottleneck map
  • immediate fixes
  • monitor plan

    • Example 2: Checkout friction

    Input:

  • Add-to-cart high, checkout completion low

    • Output focus:

  • checkout friction hypotheses
  • test sequence
  • expected lift range

    • Example 3: Funnel rebuild plan

    Input:

  • Multi-channel traffic with inconsistent landing paths

    • Output focus:

  • canonical funnel design
  • stage KPI definitions
  • experiment roadmap

    • Quality Checklist

  • [ ] Required sections are complete and non-empty
  • [ ] Trigger keywords include at least 3 registry terms
  • [ ] Input and output contracts are operationally testable
  • [ ] Workflow and decision rules are capability-specific
  • [ ] Platform references are explicit and concrete
  • [ ] At least 3 practical examples are included
    • // Comments
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