- name
- growth-autopilot-ads
- description
- Automate full-funnel strategy generation, budget structure design, and dynamic bid/scale adjustments for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic campaigns.
Growth Autopilot
Purpose
Core mission:
- Auto-generate full paid growth strategy from goals.
- Auto-design budget and account structure.
- Dynamically adjust bids and scale pace by performance signals.
- Keep growth stable with guardrails and anomaly recovery rules.
When To Trigger
Use this skill when the user asks for:
- automated growth strategy orchestration
- auto budget split and dynamic optimization
- autopilot decision loops for bidding and scaling
- continuous monitoring and adjustment policies
High-signal keywords:
- autopilot, automation, growth ai, growthbot
- budget, bidding, allocation, optimize, scale
- roas, cpa, revenue, performance, campaign
Input Contract
Required:
- north_star_goal
- budget_constraints
- platform_scope
- control_limits (max drawdown, min roas, etc.)
Optional:
- warm_start_data
- creative_inventory_state
- seasonality_rules
- escalation_contacts
Output Contract
- Autopilot Strategy Blueprint
- Budget and Structure Policy
- Dynamic Bid/Scale Rules
- Safety Guardrails and Kill-switches
- Monitoring and Escalation Workflow
Workflow
- Convert business goal to machine-actionable policy set.
- Initialize budget and structure by channel role.
- Apply adaptive bid and scale rules by KPI trend.
- Enforce guardrails and automatic rollback logic.
- Emit periodic optimization reports and next actions.
Decision Rules
- If KPI drift exceeds tolerance, shift into conservative mode.
- If confidence is low, reduce automation aggressiveness.
- If anomaly severity is high, trigger partial or full freeze.
- If recovery is confirmed, resume staged scale progression.
Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic
Platform behavior guidance:
- Autopilot rules should be channel-specific but policy-governed centrally.
- Keep bid logic aligned with platform optimization objective.
Constraints And Guardrails
- Do not auto-approve risky policy-sensitive creative changes.
- Keep manual override path always available.
- Every auto action must map to an auditable rule.
Failure Handling And Escalation
- If critical metrics are delayed, pause automated changes.
- If policy rejection rate spikes, route to human review queue.
- If data quality degrades, switch to monitoring-only mode.
Code Examples
Autopilot Policy YAML
objective: maximize_revenue_with_roas_floor roas_floor: 2.3 cpa_ceiling: 38 budget_step_pct: 12 rollback_trigger: roas_drop_pct: 18 window_days: 3
Decision Loop Pseudocode
if roas >= roas_floor and cpa <= cpa_ceiling: increase_budget(step_pct) elif roas < roas_floor: decrease_budget(step_pct) tighten_bids()
Examples
Example 1: Autopilot bootstrap
Input:
- New account with limited baseline
Output focus:
- starter policy set
- safe exploration bounds
- monitoring cadence
Example 2: Dynamic scale mode
Input:
- KPI stable for 3 weeks
Output focus:
- scale ladder
- bid adaptation rules
- rollback plan
Example 3: Emergency stabilization
Input:
- ROAS crash + spend spike
Output focus:
- freeze/rollback action
- root-cause checklist
- re-entry conditions
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