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 lifecycle-marketing-automationgit 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/lifecycle-marketing-automation)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/lifecycle-marketing-automation"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/lifecycle-marketing-automation/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/lifecycle-marketing-automation"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/lifecycle-marketing-automation.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 | $0.02267 |
| Opus 5 | $0.00015 | $0.01133 |
| Sonnet 5 | $0.00006 | $0.00453 |
| Haiku 4.5 | $0.00003 | $0.00227 |
Grade B, and why
lifecycle-marketing-automation scanned grade B with 1 finding 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 9d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
await fetch('https://a.klaviyo.com/api/profile-import/', { method: 'POST', How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lifecycle Marketing Automation
Overview
Lifecycle marketing treats each customer as being at a defined stage in their relationship with your brand — from anonymous visitor to loyal advocate — and delivers stage-appropriate messaging automatically. Unlike broadcast campaigns, lifecycle automation is triggered by behavior and stage transitions, ensuring every message is relevant. Klaviyo's predictive analytics and flow builder cover most lifecycle automation needs without custom code.
When to Use This Skill
- When moving from batch-and-blast campaigns to behavior-triggered messaging
- When different customer segments are receiving identical generic emails
- When onboarding new customers and needing a structured first-30-day nurture plan
- When LTV and repeat purchase rate are flat despite healthy acquisition numbers
- When building a holistic view of the customer journey across email, SMS, and push
Core Instructions
Step 1: Define your lifecycle stages
| Stage | Definition | Primary Goal |
|---|---|---|
| Subscriber | Email captured, no purchase | Convert to first purchase |
| First-time buyer | 1 order, placed < 60 days ago | Onboard, reduce returns, build habit |
| Active | 2+ orders, purchased within repurchase window | Grow AOV and purchase frequency |
| Loyal | 4+ orders OR > $500 LTV | Maintain, protect from churn, reward |
| At-risk | Approaching 1.5x their normal repurchase window | Proactive re-engagement |
| Lapsed | Beyond 2x their normal repurchase window | Win-back campaign |
| Advocate | Has reviewed, referred, or engaged heavily | Amplify via referral program |
Step 2: Set up lifecycle automation per stage
Shopify with Klaviyo
Klaviyo computes expected repurchase dates and churn risk automatically based on your order history — you do not need to calculate lifecycle stages manually.
Subscriber stage — Welcome Series:
- Go to Klaviyo → Flows → Create Flow → Welcome Series (use template)
- The flow fires when someone joins your email list
- Configure 3 emails over 7 days:
- Email 1 (immediate): Welcome + brand story + first-order discount
- Email 2 (day 2): Bestsellers or "Start here" product guide
- Email 3 (day 7): Social proof (reviews, customer photos, media mentions)
- Add a flow filter: "Has NOT placed an order" — exits the flow if they purchase before completing it
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/campaign-config-analytics-and-real-time-/criteria.json 3.0 KB
- evals/campaign-config-analytics-and-real-time-/task.md 2.5 KB
- evals/lifecycle-stage-classification-logic/criteria.json 2.8 KB
- evals/lifecycle-stage-classification-logic/task.md 1.9 KB
- evals/stage-transition-workflow-automation/criteria.json 3.0 KB
- evals/stage-transition-workflow-automation/task.md 2.2 KB
- tile.json 332 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.
- 9d ago First seen · 200 lines · 29 tokens per session scan B 1fb11ffb83c9
lifecycle-marketing-automation 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 2,267 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (sends data to an external url). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
ebay-sold-listings-search
All process output to user (progress updates, process notifications) follows the user's language.
1688-product-detail
Extracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688…
amazon-listing-competitor-analysis-skill
This skill helps users analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked…
ebay-search-listing
Extracts product listings from any eBay search or category page URL, returning per-item cards (itemNumber, url, title, subtitle, caption, price, priceWithCurrency, currency, wasPrice, bids, shipping, seller, sellerFeedbackCount, sellerPositiveRating, reviewsCount, starRating, image) plus pagination state (currentPage…
amazon-bestseller-listing
Amazon Best Sellers listing scraper: extract product cards from any Amazon Best Sellers (zgbs) or /gp/bestsellers/ category page — returns rank (position on chart), asin, title, url, image, imageAlt, price, stars, reviewCount, ratingRaw per item, plus category metadata (categoryName, categoryFullName, categoryUrl) and…
amazon-search-listing
Amazon search and category listing scraper: extract product listings from any Amazon search results page, keyword search URL, or category browse page and return per-item cards (asin, title, url, image, price, listPrice, stars, reviewCount, badges, isAmazonChoice, isBestSeller, isSponsored, delivery, boughtInPast…