Agent skill · marketing growth · sickn33
ad-campaign-analyzer
Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.
Why this skill is useful
Provides domain-specific knowledge on analyzing and optimizing ad campaign performance across multiple platforms, which the AI wouldn't reliably generate on its own.
What it needs
About 10k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Ad Campaign Analyzer Overview Take raw campaign performance data and turn it into testable decisions. Normalize the inputs, distinguish descriptive results from causal evidence, quantify uncertainty when the data supports it, and propose bounded budget experiments. Core principle: Most startup founders check their ad dashboard, see a ROAS number, and either panic or celebrate. This skill gives you the nuanced analysis a paid media specialist would: what's actually significant, what's noise, and where your next dollar should go. It also solves the allocation problem — most startups either spread budget too thin across channels (no channel gets enough to learn) or dump everything into one channel (missing cheaper opportunities elsewhere). When to Use This Skill "Analyze my Google Ads performance" "Which ads should I kill?" "Is this campaign working?" "Where am I wasting ad spend?" "Optimize my Meta Ads" "How should I split my ad budget?" "Should I spend more on Google or Meta?" "Reallocate my ad spend across channels" "Where am I getting the best return?" "I have $X/month for ads — how should I distribute it?" Phase 0: Intake 1. Campaign data — One of: CSV export from Google Ads / Meta Ads Manager / LinkedIn Campaign Manager Pasted performance table Screenshots of dashboard (we'll extract the data) 2. Platform(s) — Google / Meta / LinkedIn / All 3. Time period — What date range does this cover? 4. Monthly budget — Total ad spend in this period 5. Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads) 6. Target metrics — Do you have target CPA or ROAS? If not, ask for an approved, dated benchmark source; never invent one. 7. Any known changes? — Did you change creative, budget, or targeting during this period? 8. Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other 9. Funnel data (if available): Lead → MQL rate MQL → SQL rate SQL → Close rate Average deal size 10. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/ad-campaign-analyzer