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 check...

Security Scan

The skill is an instruction-only media-buying helper whose declared inputs and procedures line up with its stated purpose and it does not request extra credentials, installs, or system access.

Purpose & Capability

Name/description (media buying support across ad platforms) matches the SKILL.md content: account health, bidding, A/B test design, monitoring. There are no unrelated requirements (no cloud credentials, binaries, or config paths).

Instruction Scope

The SKILL.md contains only domain-appropriate instructions (how to evaluate snapshots, build tests, set anomaly thresholds). It does not instruct the agent to read system files, environment variables, or send data to unexpected external endpoints. Note: the declared inputs (account_structure_snapshot, recent_performance_series, etc.) are potentially sensitive ad-account data — the skill expects those data objects but does not request credentials.

Install Mechanism

No install spec and no code files are present (instruction-only). This minimizes risk because nothing is downloaded or written to the host.

Credentials

The skill declares no required environment variables, credentials, or config paths. That is proportionate for an advisory/analysis skill. As above, the user-provided inputs may contain sensitive campaign data but the skill does not ask for unrelated secrets.

Persistence & Privilege

Skill is not always-enabled and does not request persistent agent-level privileges. It does not modify other skills or system configs based on the provided materials.

Assessment

This skill appears coherent and instruction-only, but the inputs it asks for (account snapshots, performance series, bidding configs) can contain sensitive ad-account details. Before using it: (1) do not paste account credentials, tokens, or raw access keys — provide redacted or aggregated metrics instead; (2) limit shared data to the minimum required (summaries, anonymized IDs); (3) confirm you trust the agent/runtime that will receive this data; and (4) if you need the skill to act on live accounts, prefer granting scoped API tokens with least privilege rather than full credentials.

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