Ads Audience Targeting

Build audience segmentation and targeting plans for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, and DSP/programmatic campaigns.

Security Scan

The skill is an instruction-only audience-segmentation advisor whose requested inputs and behavior match its stated purpose and it does not request credentials, installs, or system access.

Purpose & Capability

Name/description (audience segmentation and targeting for ad platforms) aligns with the SKILL.md instructions: it asks for campaign goals, scope, context, KPIs and produces segmentation, action plans, and handoff payloads. No unrelated capabilities or external services are requested.

Instruction Scope

Runtime instructions are limited to marketing inputs (business_goal, scope, context, metrics) and generating recommendations, platform notes, and handoff fields. The SKILL.md does not instruct the agent to read system files, environment variables, credentials, or transmit data to unexpected endpoints.

Install Mechanism

This is an instruction-only skill with no install spec and no code files, so nothing is written to disk or installed. That is the lowest-risk install pattern and appropriate for a guidance/analysis skill.

Credentials

The skill declares no required environment variables, credentials, or config paths. The inputs it requests (account context, historical performance) are appropriate for an ad-targeting planner, but they may include sensitive account data provided by the user — the skill itself does not demand secrets or keys.

Persistence & Privilege

always is false and model invocation is allowed (default). The skill does not request persistent system presence, nor does it modify other skills or system settings.

Assessment

This skill appears coherent and low-risk: it only needs marketing/account context to produce audience segmentation recommendations and does not request credentials or install software. Before using it, ensure you: (1) do not paste sensitive account credentials or access tokens into prompts — provide high-level performance metrics or anonymized examples instead; (2) verify the skill's source or owner if you need to share real account IDs or proprietary customer data; (3) test with non-sensitive sample data to confirm outputs meet your compliance/privacy needs; and (4) if handing off actions to execution systems, ensure those downstream integrations ask separately for needed API credentials under your control.

Purpose

Define ICP segments, audience labels, exclusions, and targeting hypotheses that are ready for ad setup.

When To Trigger

Use this skill when the user asks to:

• run ads or execute advertising campaigns with clear operational next steps
• grow revenue or profit, improve roas, reduce cpa, or optimize budget and bidding
• analyze market, traffic, conversion funnel, and campaign performance signals
• apply this specific capability: icp segmentation, audience labels, exclusion strategy

Typical trigger keywords:

• ads, advertising, campaign, growth, strategy
• revenue, profit, roi, roas, cpa
• budget, bidding, traffic, conversion, funnel
• meta, googleads, tiktokads, youtubeads, amazonads, shopifyads, dsp

Input Contract

Required:

• business_goal: primary objective (sales, leads, traffic, awareness, retention)
• scope: campaign range, market, timeline, and platform scope
• context: URL, account context, historical performance, or request text

Optional:

• kpi_targets: target cpa, roas, revenue, roi, ltv, cvr
• constraints: budget, policy, brand rules, timeline, resource limits
• platform_preference: preferred channels and priority
• baseline_metrics: existing benchmark metrics

Output Contract

Return an execution-ready result with:

1. Intent Summary (goal, KPI, scope)
2. Findings (key observations and assumptions)
3. Action Plan (prioritized next steps)
4. Risks and Guardrails (what can break and what to monitor)
5. Handoff Payload (structured fields for downstream skills)

Workflow

1. Normalize request and confirm objective.
2. Validate available inputs and list missing critical data.
3. Analyze according to this skill focus: icp segmentation, audience labels, exclusion strategy.
4. Generate prioritized actions tied to KPI impact.
5. Add platform-specific notes and constraints.
6. Emit a compact handoff payload for execution.

Decision Rules

• If KPI is missing, infer likely primary KPI from goal and mark assumption explicitly.
• If data quality is low, return conservative recommendations and required follow-up checks.
• If platform context is unclear, provide platform-agnostic baseline plus channel variants.
• If policy or account risk appears high, require compliance or account checks before scale.
• If urgency is high and uncertainty is high, prioritize reversible low-risk actions first.

Platform Notes

Primary platform scope:

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

Guidance:

• Use platform-specific recommendations only when evidence supports them.
• Keep naming explicit: Meta, Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP.
• If request is cross-channel, provide channel order and budget split rationale.

Constraints And Guardrails

• Do not fabricate data, performance outcomes, or policy approvals.
• Separate facts from assumptions in every recommendation.
• Keep recommendations measurable and tied to explicit KPIs.
• Avoid irreversible changes without validation checkpoints.

Failure Handling And Escalation

• If required inputs are missing, request concise follow-up fields before final recommendation.
• If data sources conflict, report conflict and provide a safe default path.
• If request implies unsupported account actions, escalate with an exact handoff checklist.
• If compliance risk is detected, route to Ads Compliance Review before launch.

Examples

Example 1: Meta ecommerce optimization

Input:

• Goal: sales growth with lower cpa
• Platform: Meta (Facebook/Instagram)

Output focus:

• top blockers
• prioritized fixes
• week-1 actions and expected KPI movement

Example 2: Google Ads lead generation

Input:

• Goal: improve lead quality and stabilize cpl
• Platform: Google Ads

Output focus:

• search intent structure
• budget and bidding adjustments
• lead-routing handoff fields

Example 3: TikTok plus YouTube scale test

Input:

• Goal: scale traffic while protecting roas
• Platforms: TikTok Ads and YouTube Ads

Output focus:

• test matrix
• risk guardrails
• monitoring and rollback triggers

Quality Checklist

• All required sections are present
• At least 3 registry keywords appear in When To Trigger
• Input and output contracts are explicit and actionable
• Workflow is step-based and execution ready
• Platform references are concrete when applicable
• At least 3 examples are included