Ads Data Query

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name: ads-data-query-assistant description: Run natural-language data query workflows for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads reports.

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Ads Data Query

Purpose

Translate questions into precise metric pulls and decision-ready summaries.

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: query translation, metric extraction, decision summary

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: query translation, metric extraction, decision summary. 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, Amazon Ads, Shopify Ads

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

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