Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lingxling/awesome-skills-cn --skill ad-campaign-analyzergit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lingxling/awesome-skills-cn/ad-campaign-analyzer)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/ad-campaign-analyzer"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/ad-campaign-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/ad-campaign-analyzer"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/ad-campaign-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.04145 |
| Opus 5.5 | $0.00011 | $0.01658 |
| Sonnet 5.5 | $0.00006 | $0.00829 |
| Haiku 4.5 | $0.00003 | $0.00415 |
Grade A, and why
ad-campaign-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ad-campaign-analyzer — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 383 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- 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)
- Platform(s) — Google / Meta / LinkedIn / All
- Time period — What date range does this cover?
- Monthly budget — Total ad spend in this period
- Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads)
- Target metrics — Do you have target CPA or ROAS? If not, ask for an approved, dated benchmark source; never invent one.
- Any known changes? — Did you change creative, budget, or targeting during this period?
- Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other
- Funnel data (if available):
- Lead → MQL rate
- MQL → SQL rate
- SQL → Close rate
- Average deal size
- Channels you're considering but haven't tried — Want to test new channels?
- Constraints — Minimum spend on any channel? Platform you must stay on?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 19d ago First seen · 383 lines · 28 tokens per session scan A 7071ea538133
ad-campaign-analyzer is a skill published in the GitHub repository lingxling/awesome-skills-cn (321 stars, last pushed 19d ago), licensed MIT. It adds 28 tokens to every session and 4,145 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ad-campaign-analyzer, differing in 28 lines, and is treated as a copy.
Other skills, from other repositories
ad-campaign-analyzer
Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.
ad-campaign-analyzer
Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.
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