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// Skill profile

Lanbow Ads

name: lanbow-ads

by danyangliu-sandwichlab · published 2026-03-22

数据处理自动化任务加密货币
Total installs
0
Stars
★ 0
Last updated
2026-03
// Install command
$ claw add gh:danyangliu-sandwichlab/danyangliu-sandwichlab-lanbow-ads
View on GitHub
// Full documentation

---

name: lanbow-ads

description: Lanbow Ads control skill for ad campaign management and optimization across Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic.

---

# Lanbow Ads

Purpose

Core mission:

  • Serve as Lanbow's dedicated ad operations and optimization interface.
  • Manage planning, launch, monitoring, and scaling across ad channels.
  • Standardize decision policies for bidding, budget, and performance recovery.
  • Output clear operator actions for media teams.
  • When To Trigger

    Use this skill when the user asks for:

  • campaign setup, optimization, or scaling in one or more channels
  • budget and bidding decision support with performance constraints
  • anomaly diagnosis and recovery actions for live campaigns
  • cross-channel media operation playbooks
  • High-signal keywords:

  • lanbow ads, run ads, campaign, media buyer
  • bidding, budget, allocation, optimize, scale
  • cpa, roas, performance, monitor, abtest
  • Input Contract

    Required:

  • campaign_objective
  • channel_scope
  • budget_constraints
  • recent_performance_snapshot
  • Optional:

  • creative_state
  • audience_state
  • tracking_health
  • policy_or_account_flags
  • Output Contract

    1. Campaign Action Plan

    2. Bidding and Budget Policy

    3. AB Test and Scale Model

    4. Monitoring and Alert Plan

    5. Operator Handoff Checklist

    Workflow

    1. Normalize objective and KPI constraints.

    2. Evaluate channel readiness and structure quality.

    3. Produce bid and allocation actions.

    4. Attach testing and scaling rules.

    5. Return monitoring triggers and operator checklist.

    Decision Rules

  • If measurement confidence is low, limit scale and improve tracking first.
  • If ROAS is stable above threshold, allow staged budget increases.
  • If CPA is unstable, reduce concurrency of experiments.
  • If anomaly risk is high, prefer containment actions first.
  • Platform Notes

    Primary scope:

  • Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic
  • Platform behavior guidance:

  • Keep channel recommendations execution-specific and auditable.
  • Align bid logic with each platform's optimization mechanics.
  • Constraints And Guardrails

  • No irreversible changes without rollback conditions.
  • Keep every recommendation tied to KPI impact.
  • Respect policy and account health constraints.
  • Failure Handling And Escalation

  • If required platform data is missing, return minimum data request list.
  • If policy or account block appears, route to compliance/account helper.
  • If spend risk is severe, trigger emergency control mode.
  • Code Examples

    Campaign Control Spec

    objective: improve_roas

    channels: [Meta, GoogleAds, TikTokAds]

    budget_mode: staged_scale

    cpa_ceiling: 42

    roas_floor: 2.5

    Alert Trigger Rule

    if roas_drop_pct > 20 and spend_up_pct > 25:

    severity: high

    action: cap_budget_and_notify

    Examples

    Example 1: Launch and stabilize

    Input:

  • New campaign across Meta and TikTok Ads
  • Output focus:

  • launch checklist
  • first-week controls
  • fallback rules
  • Example 2: Scale after validation

    Input:

  • Stable ROAS for 10 days
  • Output focus:

  • scale ladder
  • bid policy updates
  • monitoring checkpoints
  • Example 3: Cross-channel anomaly

    Input:

  • Spend surge, mixed conversion signals
  • Output focus:

  • anomaly triage
  • containment actions
  • next validation steps
  • 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
  • // Comments
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