Ads Media Strategy

Plan channel mix and media strategy for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic.

Security Scan

This is an instruction-only ad media planning skill whose inputs, outputs, and runtime instructions are coherent with its stated purpose and do not request credentials, installs, or unusual system access.

Purpose & Capability

The skill name and description (media/channel mix, budget allocation for ad platforms) match the SKILL.md content. No binaries, environment variables, or config paths are requested, which is proportionate for an advisory/strategy skill.

Instruction Scope

Runtime instructions are focused on strategy, test matrices, budget splits, and guardrails. One mildly ambiguous item: 'escalate with a structured handoff payload' — the skill doesn't specify an external endpoint or destination for such payloads, so reviewers should confirm downstream handling of any handoff data to avoid accidental disclosure of sensitive info. Otherwise the instructions stay within advertising strategy scope and do not direct reading of system files or credentials.

Install Mechanism

No install spec and no code files — the skill is instruction-only, which minimizes disk footprint and execution risk.

Credentials

No required environment variables, credentials, or config paths are declared. The SKILL.md does not reference accessing secrets or unrelated environment state.

Persistence & Privilege

always is false and the skill is user-invocable with normal autonomous invocation allowed. It does not request permanent presence or modifications to other skills or system-wide settings.

Assessment

This skill is an instruction-only planner and appears internally consistent for creating ad channel mixes and budgets. Before installing: confirm that any 'handoff payload' generated by the agent will not be automatically sent to external services (no endpoints are specified), and avoid supplying real account credentials or billing tokens in free-text inputs. If you plan to use the agent to perform live actions (execute orders, change campaign billing), require an explicit, separate integration that you control rather than pasting credentials into chat. Otherwise it is safe to install from an access/privilege perspective.

Purpose

Core mission:

• objective mapping, channel mix, budget allocation

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, DSP/programmatic
• this specific capability: objective mapping, channel mix, budget allocation

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:

• objective: growth target and KPI priority
• budget_frame: test budget and scale budget
• channel_scope: channels to include

Optional:

• audience_segments
• creative_inventory
• seasonality_window
• policy_constraints

Output Contract

1. Strategy Snapshot
2. Channel Role Definition
3. Budget and Bidding Plan
4. Test Matrix
5. Scale and Kill Rules

Workflow

1. Define objective hierarchy (primary and secondary KPI).
2. Assign channel roles by funnel stage.
3. Allocate budget by expected signal and risk.
4. Design test cells and learning windows.
5. Set scale, hold, and stop rules.

Decision Rules

• If KPI conflict exists, prioritize revenue efficiency over volume.
• If channel evidence is weak, allocate minimum test budget first.
• If audience is broad, start with modular creatives and layered targeting.

Platform Notes

Primary scope:

• Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic

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

Strategy Matrix (YAML)

objective: improve_roas
channels:
  - name: Meta
    role: demand_creation
  - name: Google Ads
    role: demand_capture
budget_split:
  Meta: 0.55
  Google Ads: 0.45

Test Cell Example

cell_id: T1
variable: audience_segment
success_metric: cpa

Examples

Example 1: Channel mix reset

Input:

• Budget fixed at 50k
• ROAS dropped for two weeks

Output focus:

• reallocation plan
• test matrix
• stop-loss conditions

Example 2: Creator-led expansion strategy

Input:

• Goal: scale traffic without ROAS collapse
• Channels: TikTok Ads + YouTube Ads

Output focus:

• funnel role split
• budget pacing logic
• creative cadence

Example 3: Retargeting-heavy recovery

Input:

• Prospecting unstable
• Strong existing customer base

Output focus:

• retargeting architecture
• audience exclusion design
• two-phase launch plan

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