Skill: danyangliu-sandwichlab/ads-execution-hub (version 1.0.1)

--- name: ads-execution-hub description: Ads Execution Hub control skill for ad campaign management and optimization across Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic. --- # Ads Execution Hub ## Purpose Core mission: - Serve as the dedicated ad operations and optimization interface. - Manage planning, launch, monitoring, and scaling across ad channels. - Standardize decision policies for bidding, budget, and performance recovery. - Output clear operator actions for media teams. ## When To Trigger Use this skill when the user asks for: - campaign setup, optimization, or scaling in one or more channels - budget and bidding decision support with performance constraints - anomaly diagnosis and recovery actions for live campaigns - cross-channel media operation playbooks High-signal keywords: - ads execution hub, run ads, campaign, media buyer - bidding, budget, allocation, optimize, scale - cpa, roas, performance, monitor, abtest ## Input Contract Required: - campaign_objective - channel_scope - budget_constraints - recent_performance_snapshot Optional: - creative_state - audience_state - tracking_health - policy_or_account_flags ## Output Contract 1. Campaign Action Plan 2. Bidding and Budget Policy 3. AB Test and Scale Model 4. Monitoring and Alert Plan 5. Operator Handoff Checklist ## Workflow 1. Normalize objective and KPI constraints. 2. Evaluate channel readiness and structure quality. 3. Produce bid and allocation actions. 4. Attach testing and scaling rules. 5. Return monitoring triggers and operator checklist. ## Decision Rules - If measurement confidence is low, limit scale and improve tracking first. - If ROAS is stable above threshold, allow staged budget increases. - If CPA is unstable, reduce concurrency of experiments. - If anomaly risk is high, prefer containment actions first. ## Platform Notes Primary scope: - Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic Platform behavior guidance: - Keep channel recommendations execution-specific and auditable. - Align bid logic with each platform's optimization mechanics. ## Constraints And Guardrails - No irreversible changes without rollback conditions. - Keep every recommendation tied to KPI impact. - Respect policy and account health constraints. ## Failure Handling And Escalation - If required platform data is missing, return minimum data request list. - If policy or account block appears, route to compliance/account helper. - If spend risk is severe, trigger emergency control mode. ## Code Examples ### Campaign Control Spec objective: improve_roas channels: [Meta, GoogleAds, TikTokAds] budget_mode: staged_scale cpa_ceiling: 42 roas_floor: 2.5 ### Alert Trigger Rule if roas_drop_pct > 20 and spend_up_pct > 25: severity: high action: cap_budget_and_notify ## Examples ### Example 1: Launch and stabilize Input: - New campaign across Meta and TikTok Ads Output focus: - launch checklist - first-week controls - fallback rules ### Example 2: Scale after validation Input: - Stable ROAS for 10 days Output focus: - scale ladder - bid policy updates - monitoring checkpoints ### Example 3: Cross-channel anomaly Input: - Spend surge, mixed conversion signals Output focus: - anomaly triage - containment actions - next validation steps ## 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

path: metadata.json
content: slug: ads-execution-hub
display_name: Ads Execution Hub

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