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 predictive-personalizationgit 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/predictive-personalization)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/predictive-personalization"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/predictive-personalization/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/predictive-personalization"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/predictive-personalization.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 | $0.02152 |
| Opus 5 | $0.00014 | $0.01076 |
| Sonnet 5 | $0.00006 | $0.00430 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade A, and why
predictive-personalization 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Predictive Personalization
Overview
Predictive personalization tailors the shopping experience to each visitor — showing relevant product recommendations, personalized content, and targeted offers based on behavior, purchase history, and patterns from similar customers. For most merchants, dedicated personalization apps deliver this without any custom ML code. Building a custom recommendation engine only makes sense for headless stores with significant traffic (100k+ monthly visitors) where app costs or data control requirements justify the complexity.
When to Use This Skill
- When your store shows the same products to every visitor regardless of their behavior
- When you want to add "Recommended for You" sections to your homepage, PDP, or cart
- When email campaigns send the same products to your entire list
- When conversion rates are plateauing and you need a lift from relevance
- When ready to move beyond rule-based merchandising to data-driven personalization
Core Instructions
Step 1: Choose the right personalization tool
| Platform | Best For | Shopify | WooCommerce | BigCommerce | Price |
|---|---|---|---|---|---|
| Rebuy | Product recommendations, cross-sell/upsell widgets | App Store | Limited | Limited | $99+/mo |
| LimeSpot | Personalization + merchandising | App Store | Plugin | App Marketplace | $18+/mo |
| Nosto | Mid-market, full homepage + email personalization | App Store | Plugin | App Marketplace | Revenue-share |
| Dynamic Yield | Enterprise, full A/B testing + personalization | Via JS tag | Via JS tag | Via JS tag | $1,000+/mo |
| Klaviyo (email) | Personalized product blocks in email flows | App Store | Plugin | App Marketplace | Included in Klaviyo |
| Custom | Headless stores, 100k+ visitors/mo | API | API | API | Dev cost |
Recommendation by store size:
- Under $1M revenue: Rebuy or LimeSpot for recommendation widgets; Klaviyo for personalized email
- $1M–$10M revenue: Nosto for full-site + email personalization
- $10M+: Dynamic Yield for enterprise personalization + experimentation
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/behavioral-event-tracking-and-user-profi/criteria.json 2.9 KB
- evals/behavioral-event-tracking-and-user-profi/task.md 1.8 KB
- evals/collaborative-filtering-model-with-jacca/criteria.json 2.6 KB
- evals/collaborative-filtering-model-with-jacca/task.md 1.9 KB
- evals/recommendation-scoring-diversity-a-b-tes/criteria.json 3.4 KB
- evals/recommendation-scoring-diversity-a-b-tes/task.md 2.3 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 · 186 lines · 28 tokens per session scan A e01b01533ed1
predictive-personalization is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 2,152 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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