Skill: danyangliu-sandwichlab/landing-page-auditor (version 1.0.0)

--- name: landing-page-auditor description: Audit landing pages for paid traffic quality from Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads funnels. --- # Ads Landing Page Audit ## Purpose Core mission: - reachability checks, friction diagnosis, conversion blockers 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 - this specific capability: reachability checks, friction diagnosis, conversion blockers 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: - entity_ids: account, campaign, adset, or ad identifiers - incident_or_audit_scope - time_window Optional: - logs_or_events - policy_flags - alert_thresholds - owner_contacts ## Output Contract 1. Operational Status Summary 2. Severity-ranked Findings 3. Mitigation Actions 4. Escalation Ticket Payload 5. Monitoring Checklist ## Workflow 1. Confirm operational scope and impacted entities. 2. Validate data freshness and event completeness. 3. Detect anomalies, policy flags, or setup gaps. 4. Rank fixes by spend risk and recovery speed. 5. Emit owner-ready action and ticket payload. ## Decision Rules - If data is stale, block final judgement and request refresh timestamp. - If high-severity risk is found, recommend containment action first. - If root cause is uncertain, provide top hypotheses with validation order. ## Platform Notes Primary scope: - Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads Platform behavior guidance: - Keep recommendations channel-aware; do not collapse all channels into one generic plan. - For Meta and TikTok Ads, prioritize creative testing cadence. - For Google Ads and Amazon Ads, prioritize demand-capture and query/listing intent. - For DSP/programmatic, prioritize audience control and frequency governance. ## Constraints And Guardrails - Never fabricate metrics or policy outcomes. - Separate observed facts from assumptions. - Use measurable language for each proposed action. - Include at least one rollback or stop-loss condition when spend risk exists. ## Failure Handling And Escalation - If critical inputs are missing, ask for only the minimum required fields. - If platform constraints conflict, show trade-offs and a safe default. - If confidence is low, mark it explicitly and provide a validation checklist. - If high-risk issues appear (policy, billing, tracking breakage), escalate with a structured handoff payload. ## Code Examples ### Incident Ticket Example ticket_id: INC-ads-001 severity: high impact: spend_waste_risk owner: media-ops next_check_at: 2026-03-03T10:00:00Z ### Health Rule Example rule: delivery_drop condition: impressions_down_pct > 40 action: alert_and_pause_review ## Examples ### Example 1: Pixel breakage before launch Input: - Purchase event missing - Campaign start in 24h Output focus: - severity ranking - immediate mitigation - relaunch checklist ### Example 2: Account health audit Input: - Multiple platform accounts - Unknown policy or billing status Output focus: - readiness scorecard - blocking risks - owner-level actions ### Example 3: Live anomaly handling Input: - Spend spikes with no conversion lift - Multiple campaigns impacted Output focus: - anomaly triage path - containment actions - incident ticket payload ## 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)