growth-autopilot-ads（增长自动驾驶广告）

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

1. Autopilot Strategy Blueprint
2. Budget and Structure Policy
3. Dynamic Bid/Scale Rules
4. Safety Guardrails and Kill-switches
5. Monitoring and Escalation Workflow

Workflow

1. Convert business goal to machine-actionable policy set.
2. Initialize budget and structure by channel role.
3. Apply adaptive bid and scale rules by KPI trend.
4. Enforce guardrails and automatic rollback logic.
5. 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