Claude Cursor Skill

paid-ads-optimize

Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions.

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Download nowork-studio-notfair-plugin-paid-ads_paid-ads-optimize-f984768.zip · 1 KB

nowork-studio/notfair-plugin 3849 495 forks MIT Updated 1d ago

Part of nowork-studio/notfair-plugin — 88 skills

Install

skills CLI npx skills add https://github.com/nowork-studio/notfair-plugin/tree/main/paid-ads/paid-ads-optimize

Claude Code

claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install nowork-studio-notfair-plugin@llmmart

Git git clone https://github.com/nowork-studio/notfair-plugin.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole nowork-studio/notfair-plugin collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Paid Ads Optimization

Read ../shared/operating-contract.md and ../shared/measurement-framework.md. Review before changing anything.

Diagnose before cutting

Verify the conversion signal, period completeness, spend volume, attribution model, and recent account changes. Spend with no recorded conversion can indicate broken tracking or immature data; treat it as a hypothesis until the signal and volume support an intervention. Check landing-page or operational failures before blaming targeting.

Classify the bottleneck as query/audience quality, creative fatigue, delivery/rank, budget constraint, landing-page mismatch, tracking, or economics. Use the specialized Google, Meta, X, LinkedIn, Reddit, or TikTok skill for live diagnosis. For other platforms, analyze only the supplied or verified data.

Rank reversible moves

Prefer this order: exclude an irrelevant query, placement, or audience; pause the narrowest losing unit; adjust budget or bid in a measured step; then consider structural change. For a reallocation, show the current and proposed allocations, the same total budget unless the user approves an increase, and the observable hypothesis.

Do not declare a loser from a few clicks. Set a threshold appropriate to the named target CPA, conversion lag, and channel role. Preserve upper-funnel and assisted-conversion context rather than judging all campaigns on last-click CPA alone.

Approval and follow-up

Present each exact mutation with scope, current value, proposed value, currency exposure, rationale, and review date. After approval, execute only through the verified platform skill or connector, read back the result, and record the intervention's expected effect and guardrail. Revisit after the declared observation window instead of promising a generic ongoing watch.

Files (notfair-plugin)

agents

openai.yaml 220 B

interface:
  display_name: "Paid Ads Optimization"
  short_description: "Find waste and propose evidence-backed changes"
  default_prompt: "Use $paid-ads-optimize to identify wasted spend and review safe optimizations."

SKILL.md 2.1 KB

---
name: paid-ads-optimize
description: Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions.
argument-hint: "<campaign, target CPA/ROAS, or 'find waste'>"
---

# Paid Ads Optimization

Read `../shared/operating-contract.md` and `../shared/measurement-framework.md`. Review before changing anything.

## Diagnose before cutting

Verify the conversion signal, period completeness, spend volume, attribution model, and recent account changes. Spend with no recorded conversion can indicate broken tracking or immature data; treat it as a hypothesis until the signal and volume support an intervention. Check landing-page or operational failures before blaming targeting.

Classify the bottleneck as query/audience quality, creative fatigue, delivery/rank, budget constraint, landing-page mismatch, tracking, or economics. Use the specialized Google, Meta, X, LinkedIn, Reddit, or TikTok skill for live diagnosis. For other platforms, analyze only the supplied or verified data.

## Rank reversible moves

Prefer this order: exclude an irrelevant query, placement, or audience; pause the narrowest losing unit; adjust budget or bid in a measured step; then consider structural change. For a reallocation, show the current and proposed allocations, the same total budget unless the user approves an increase, and the observable hypothesis.

Do not declare a loser from a few clicks. Set a threshold appropriate to the named target CPA, conversion lag, and channel role. Preserve upper-funnel and assisted-conversion context rather than judging all campaigns on last-click CPA alone.

## Approval and follow-up

Present each exact mutation with scope, current value, proposed value, currency exposure, rationale, and review date. After approval, execute only through the verified platform skill or connector, read back the result, and record the intervention's expected effect and guardrail. Revisit after the declared observation window instead of promising a generic ongoing watch.