ads-budget
agricidaniel/claude-ads
Plan and optimize paid-media budgets, bidding, and allocation across platforms with ROAS, CPA, and marginal-return analysis.
What is ads-budget?
Structures budget planning for paid advertising by establishing objectives, normalizing spend data, calculating break-even boundaries, and comparing allocation options. Use this when deciding how much to spend on ads, where to allocate budget across platforms, whether to scale campaigns, or how to set bidding strategies.
- Calculate break-even boundaries and show formulas, inputs, uncertainty, and sensitivity cases
- Distinguish committed baseline, controlled experiments, and reserve capacity
- Compare hold, reallocate, scale, reduce, or experiment options using marginal evidence
- Analyze metrics: CPA, ROAS, MER, LTV:CAC, spend pacing, and forecasts
- Return decision-complete plans with platform/campaign amounts, timing, owners, guardrails, and rollback triggers
- Normalize spend and outcomes across comparable windows and platform definitions
How to install ads-budget
npx skills add https://github.com/agricidaniel/claude-ads --skill ads-budgetHow to use ads-budget
- 1.Define your objective, target conversion value, gross margin, cash constraints, and sales capacity
- 2.Gather historical spend and outcome data (conversions, revenue) from each platform
- 3.Normalize data to comparable time windows and conversion definitions across platforms
- 4.Calculate break-even CPA and ROAS thresholds using the provided formulas
- 5.Identify your committed baseline, experimental budget, and reserve capacity
- 6.Compare allocation options (hold, reallocate, scale, reduce, experiment) using marginal returns, not blended averages
- 7.Document the final plan with platform amounts, timing, owner, guardrails, success measures, and rollback triggers
Use cases
- Allocate quarterly ad budget across Google Ads, Meta, and TikTok based on historical ROAS and margin constraints
- Decide whether to scale a high-performing campaign or hold spend pending more conversion data
- Set bidding strategy and daily spend pacing to hit a target CPA while respecting cash flow limits
- Evaluate tradeoffs between baseline spend, controlled experiments, and reserve capacity for new channels
- Forecast ad spend and revenue under different seasonality and sales-capacity scenarios
- Performance marketers and growth managers
- E-commerce and SaaS finance teams
- Product managers evaluating marketing ROI
- Agencies managing multi-platform campaigns
ads-budget FAQ
No. The skill treats these as optional heuristics, never universal authorization. Always base decisions on your specific margins, constraints, attribution uncertainty, and marginal evidence.
Establish it upfront as a constraint in step 1. Normalize outcomes to comparable definitions, calculate sensitivity cases, and reserve capacity for experiments to reduce uncertainty over time.
Blended returns average all spend; marginal returns measure the incremental outcome of the next dollar spent. Use marginal evidence to decide scaling, not blended averages alone.
Use controlled experiments to test new channels or strategies with reserve capacity. Scale only after you have sufficient evidence (platform minimum evidence threshold) that marginal returns exceed break-even.
Define rollback triggers upfront in your plan—e.g., CPA exceeds 1.5× target, ROAS drops below 3:1, or conversion volume falls below expected range. Monitor guardrails weekly.
Full instructions (SKILL.md)
Source of truth, from agricidaniel/claude-ads.
name: ads-budget description: "Plan and review paid-media budgets, bidding, pacing, marginal return, forecasts, CPA, ROAS, MER, LTV:CAC, constraints, and allocation across supported platforms. Use for ad budget allocation, media budget, bidding strategy, scaling, spend pacing, budget forecast, ROAS target, or investment tradeoffs."
Budget and Bidding
- Establish objective, conversion value, gross margin, cash constraints, sales capacity, attribution uncertainty, seasonality, and platform minimum evidence.
- Normalize spend and outcomes to comparable windows and definitions.
- Calculate break-even boundaries and show formulas, inputs, uncertainty, and sensitivity cases.
- Distinguish committed baseline, controlled experiments, and reserve capacity.
- Compare hold, reallocate, scale, reduce, or experiment options using marginal evidence rather than blended averages alone.
- Return a decision-complete plan with platform/campaign amount, timing, owner, guardrails, success measure, and rollback trigger.
Rules such as 70/20/10, fixed CPA multiples, fixed budget-to-CPA ratios, and fixed percentage scaling are optional heuristics, never universal authorization.
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