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 finsilabs/awesome-ecommerce-skills --skill marketing-spend-analysisgit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-skillsWrote 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/finsilabs/awesome-ecommerce-skills/marketing-spend-analysis)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/marketing-spend-analysis"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/marketing-spend-analysis/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/finsilabs/awesome-ecommerce-skills/marketing-spend-analysis"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/marketing-spend-analysis.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 | $0.00029 | $0.02945 |
| Opus 5 | $0.00015 | $0.01473 |
| Sonnet 5 | $0.00006 | $0.00589 |
| Haiku 4.5 | $0.00003 | $0.00295 |
Grade A, and why
marketing-spend-analysis 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Spend Analysis
Overview
Marketing spend is typically the largest variable cost in a DTC ecommerce business — often 15–40% of revenue. Unlike most costs, marketing spend is directly controllable in near-real-time: you can increase or decrease budgets on paid channels within minutes. This creates both opportunity (scale what works) and risk (waste capital on what does not).
The core goal is to maximize total contribution profit from your marketing investment — not just revenue. A channel with high ROAS but thin margins, high return rates, or low AOV may generate less actual profit than a channel with lower ROAS and stronger unit economics.
This skill guides you through building a unified view of marketing spend and performance across all channels, using tools designed specifically for ecommerce merchants.
When to Use This Skill
- When managing marketing budgets across multiple platforms (Meta, Google, TikTok, Amazon Ads)
- When wanting to identify which channels generate the most profitable customers
- When needing a unified marketing performance dashboard fed by multiple ad platforms
- When hitting diminishing returns on a key channel and deciding how to reallocate spend
- When comparing platform-reported ROAS against first-party attributed ROAS
- When building a marketing efficiency report for a board or investor update
Core Instructions
Step 1: Choose a unified marketing analytics tool
The biggest problem in marketing spend analysis is that each platform (Meta, Google, TikTok) reports its own ROAS using its own attribution window — and they all claim 100% credit. You need a tool that pulls data from all platforms into one view and compares against your actual order data.
| Platform | Recommended Tool | What It Does |
|---|---|---|
| Shopify | Triple Whale or Polar Analytics | Connects Shopify orders + all ad platforms; shows blended ROAS, MER, and channel-level true ROAS side by side |
| Shopify (budget option) | Shopify Analytics + Google Analytics 4 | Free; last-click attribution only; no cross-platform comparison |
| WooCommerce | Metorik + GA4 | Metorik adds UTM attribution to WooCommerce orders; GA4 provides channel-level conversion reporting |
| BigCommerce | Glew.io or Rockerbox | Both connect BigCommerce orders to ad platform spend data |
| All platforms | Northbeam or Rockerbox | Platform-agnostic; provide first-party multi-touch attribution across all channels with spend pacing |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/diminishing-returns-curve-fitting-and-op/criteria.json 2.4 KB
- evals/diminishing-returns-curve-fitting-and-op/task.md 2.3 KB
- evals/roas-metrics-computation-and-profitabili/criteria.json 2.8 KB
- evals/roas-metrics-computation-and-profitabili/task.md 1.5 KB
- evals/unified-spend-schema-attribution-windows/criteria.json 3.3 KB
- evals/unified-spend-schema-attribution-windows/task.md 3.5 KB
- tile.json 328 B
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.
- 12d ago First seen · 202 lines · 29 tokens per session scan A 31d74b727011
marketing-spend-analysis is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 29 tokens to every session and 2,945 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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