media-buyer-ads-helper
v1.0.0Support media buying execution for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and DSP/programmatic with account health checks, bidding efficiency analysis, AB test design, and real-time anomaly monitoring.
Installation
Media Buyer Helper
Purpose
Core mission: - Evaluate account health and structure quality. - Analyze bid logic and budget allocation efficiency. - Design AB test architecture and scale model. - Monitor campaigns in real time and detect anomalies.
When To Trigger
Use this skill when the user asks for: - media buyer execution support - bid and budget efficiency diagnostics - AB testing structure design - live campaign watch and anomaly alerts
High-signal keywords: - media, bidding, budget, auction, allocation - abtest, campaign, performance, optimize - cpa, roas, scale, monitor
Input Contract
Required: - account_structure_snapshot - bidding_config - budget_allocation_snapshot - recent_performance_series
Optional: - test_history - alert_thresholds - creative_breakdowns - seasonality_notes
Output Contract
- Account Health and Structure Score
- Bid and Budget Efficiency Findings
- AB Test Structure Blueprint
- Scale Model with Trigger Conditions
- Monitoring and Alert Rules
Workflow
- Check account hierarchy and naming hygiene.
- Evaluate bid strategy vs KPI objective.
- Diagnose budget fragmentation and overlap.
- Build AB test matrix with clear success metrics.
- Define anomaly thresholds and response playbook.
Decision Rules
- If structure complexity is high and spend is low, simplify before adding tests.
- If CPA variance is high, reduce concurrent experiments.
- If winning cells are statistically weak, extend learning window.
- If anomaly severity is high, prioritize containment over optimization.
Platform Notes
Primary scope: - Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic
Platform behavior guidance: - Map bid logic to channel auction mechanics. - Keep test isolation strict to avoid cross-cell contamination.
Constraints And Guardrails
- Do not claim statistical significance without threshold checks.
- Avoid broad budget jumps without gate conditions.
- Keep alert rules tied to action ownership.
Failure Handling And Escalation
- If data granularity is insufficient, request minimum breakdowns.
- If live anomaly cannot be diagnosed, escalate with incident payload.
- If policy rejects disrupt test integrity, pause affected cells and reroute budget.
Code Examples
AB Test Matrix
test_id: AB-2026-07
variable: bid_strategy
cells:
- control: target_cpa
- challenger: max_conversion_value
success_metric: blended_roas
Anomaly Rule
if spend_spike_pct > 35 and conversions_drop_pct > 25:
severity: high
action: notify_and_limit_budget
Examples
Example 1: Bid efficiency issue
Input: - CPC up, CVR flat
Output focus: - bid logic fix - budget reallocation - test plan
Example 2: AB test setup
Input: - Need test for broad vs layered audience
Output focus: - clean test architecture - significance rule - rollout timeline
Example 3: Real-time anomaly
Input: - Sudden spend spike in one channel
Output focus: - anomaly diagnosis - immediate actions - escalation path
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