Media Buyer Helper

Support 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.

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

1. Account Health and Structure Score
2. Bid and Budget Efficiency Findings
3. AB Test Structure Blueprint
4. Scale Model with Trigger Conditions
5. Monitoring and Alert Rules

Workflow

1. Check account hierarchy and naming hygiene.
2. Evaluate bid strategy vs KPI objective.
3. Diagnose budget fragmentation and overlap.
4. Build AB test matrix with clear success metrics.
5. 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