Skill: danyangliu-sandwichlab/funnel-ads-helper (version 1.0.0)

--- 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)