dvcrn 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:
- Intent Summary (goal, KPI, scope)
- Findings (key observations and assumptions)
- Action Plan (prioritized next steps)
- Risks and Guardrails (what can break and what to monitor)
- Handoff Payload (structured fields for downstream skills)
Workflow
- Normalize request and confirm objective.
- Validate available inputs and list missing critical data.
- Analyze according to this skill focus: query translation, metric extraction, decision summary.
- Generate prioritized actions tied to KPI impact.
- Add platform-specific notes and constraints.
- 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