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 win-back-reactivationgit 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/win-back-reactivation)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/win-back-reactivation"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/win-back-reactivation/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/win-back-reactivation"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/win-back-reactivation.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 | $0.00027 | $0.02510 |
| Opus 5 | $0.00014 | $0.01255 |
| Sonnet 5 | $0.00005 | $0.00502 |
| Haiku 4.5 | $0.00003 | $0.00251 |
Grade A, and why
win-back-reactivation 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 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.
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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win-Back Reactivation
Overview
Lapsed customers — those who have not purchased within 2× their typical repurchase cycle — represent a high-ROI recovery opportunity because they already know your brand. Win-back campaigns targeting these customers typically yield 5–15% reactivation rates, compared to 1–3% for cold prospecting. For Shopify, Klaviyo's predictive churn risk segment automates lapsed customer identification without manual RFM scoring. For WooCommerce, AutomateWoo provides a dedicated "Win Back" automation trigger. The strategic work is structuring a three-step email sequence, personalizing offers by customer LTV, and sunsetting contacts who remain unresponsive.
When to Use This Skill
Note: For proactive churn prevention before customers lapse, see @customer-retention-engine. This skill focuses on re-engaging customers who have already become inactive.
- When a large portion of your customer base has not purchased in 90–180 days
- When overall repeat purchase rate is declining year-over-year
- When you have never systematically targeted lapsed customers before
- When lifecycle marketing is in place but there is no specific win-back workflow
- When wanting to identify which lapsed customers are worth discounting vs. sunsetting
Core Instructions
Step 1: Choose your win-back automation tool
| Platform | Recommended Tool | Why | Price |
|---|---|---|---|
| Shopify | Klaviyo | Predictive churn risk segment built-in; syncs Shopify orders natively | Free up to 500 contacts; $20+/mo |
| WooCommerce | AutomateWoo | Native "Win Back" trigger based on days since last order | $99/yr |
| BigCommerce | Klaviyo | Same as Shopify — Klaviyo has a native BigCommerce integration | Free up to 500 contacts; $20+/mo |
| Any platform | Drip | Ecommerce-focused automation with built-in win-back workflow templates | $39+/mo |
Recommendation: If you already use Klaviyo for cart abandonment or post-purchase flows, add win-back there — no additional tool needed. If you are on WooCommerce without Klaviyo, AutomateWoo is the most cost-effective option.
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/lapsed-customer-segmentation-logic/criteria.json 2.4 KB
- evals/lapsed-customer-segmentation-logic/task.md 1.6 KB
- evals/purchase-handler-and-email-sunset-workfl/criteria.json 2.8 KB
- evals/purchase-handler-and-email-sunset-workfl/task.md 1.8 KB
- evals/win-back-sequence-and-offer-personalizat/criteria.json 2.7 KB
- evals/win-back-sequence-and-offer-personalizat/task.md 1.8 KB
- tile.json 308 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 · 182 lines · 27 tokens per session scan A a03a8147707f
win-back-reactivation is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 2,510 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 2026-09-03.
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churn-prevention
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holiday-shopper-reactivation
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cart-abandonment-value-segmentation
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