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// Skill profile

Ads Bid Optimizer

name: budget-bidding-optimizer

by danyangliu-sandwichlab · published 2026-03-22

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-budget-bidding-optimizer
View on GitHub
// Full documentation

---

name: budget-bidding-optimizer

description: Optimize budget pacing and bid strategy for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and DSP/programmatic auctions.

---

# 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

    1. Strategy Snapshot

    2. Channel Role Definition

    3. Budget and Bidding Plan

    4. Test Matrix

    5. Scale and Kill Rules

    Workflow

    1. Define objective hierarchy (primary and secondary KPI).

    2. Assign channel roles by funnel stage.

    3. Allocate budget by expected signal and risk.

    4. Design test cells and learning windows.

    5. 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
    • // Comments
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