dvcrn Ads Campaign Review
Purpose
Core mission:
- root-cause analysis, lesson extraction, next-cycle design
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, DSP/programmatic
- this specific capability: root-cause analysis, lesson extraction, next-cycle design
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:
- question_or_report_goal
- metric_scope: KPI, dimensions, and date range
- data_source_scope
Optional:
- attribution_window
- benchmark_reference
- dashboard_filters
- confidence_threshold
Output Contract
- Metric Definition Clarification
- Query Plan
- Result Summary
- Interpretation and Caveats
- Decision Recommendation
Workflow
- Disambiguate metric definitions and time window.
- Build query slices by platform, funnel, and audience.
- Compute trend deltas and variance drivers.
- Summarize findings with confidence level.
- Propose concrete next actions.
Decision Rules
- If metric definitions conflict, lock one canonical definition before analysis.
- If sample size is small, mark result as directional not conclusive.
- If attribution changes materially alter result, show both views.
Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon 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
Query Spec Example
metric: roas
dimensions: [platform, campaign]
date_range: last_30d
Result Schema
{
"platform": "Meta",
"spend": 12000,
"revenue": 42000,
"roas": 3.5
}
Examples
Example 1: Daily report automation
Input:
- Need 9AM daily summary for key campaigns
- KPI: spend, cpa, roas
Output focus:
- report schema
- anomaly highlights
- top next actions
Example 2: Attribution window comparison
Input:
- 1d click vs 7d click disagreement
- Decision needed for budget shift
Output focus:
- side-by-side metric table
- interpretation caveats
- decision recommendation
Example 3: Traffic structure diagnosis
Input:
- Revenue flat but traffic rising
- Suspected quality decline
Output focus:
- source mix decomposition
- quality signal changes
- corrective action 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