dvcrn Ads Bid Optimizer
Purpose
Core mission:
- bid logic, pacing guardrails, allocation optimization
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: bid logic, pacing guardrails, allocation optimization
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
- Strategy Snapshot
- Channel Role Definition
- Budget and Bidding Plan
- Test Matrix
- Scale and Kill Rules
Workflow
- Define objective hierarchy (primary and secondary KPI).
- Assign channel roles by funnel stage.
- Allocate budget by expected signal and risk.
- Design test cells and learning windows.
- 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, 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