E-Commerce Product Ranking

E-Commerce Product Ranking is an AI Engineering skill for Claude Code, published by asgard-ai-platform in skills.

228 stars28 forkson asgard-ai-platform/skillsAdded 2026/09/03Repository updated 2026/06/06

ai-agentanthropicclaudeclaude-agent-skillsclaude-codecoding-agentknowledge-basemcpmethodologyopen-sourceprompt-engineeringskillstaiwan

Install in seconds

Install E-Commerce Product Ranking

Copy E-Commerce Product Ranking into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-ranking ~/.claude/skills/algo-ecom-ranking

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/asgard-ai-platform/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: algo-ecom-ranking/SKILL.md in asgard-ai-platform/skills
Installs to: ~/.claude/skills/algo-ecom-ranking
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2793 skills

What E-Commerce Product Ranking does

E-Commerce Product Ranking combines relevance, CTR, conversion, ratings, and business metrics to sort products. Use it for product search or browse ranking when pure text relevance is not enough.

E-Commerce Product Ranking is cataloged under AI Engineering on DirSkills. E-Commerce Product Ranking comes from a repository tagged ai-agent, anthropic, claude, claude-agent-skills and claude-code.

Documentation

README

Excerpt

E-commerce ranking combines text relevance (BM25) with commercial signals (CTR, conversion rate, revenue, margin) into a unified ranking score. Uses learning-to-rank (LTR) models trained on click and conversion data to optimize for business-relevant outcomes.

When to Use

Trigger conditions:

Building a product search/browse ranking beyond pure text relevance
Incorporating business metrics (margin, inventory) into ranking
Implementing a learning-to-rank pipeline

When NOT to use:

For pure text search relevance only (use BM25)
When no click/conversion data exists (start with rule-based ranking)

Algorithm

This is the opening of the README. Read the full README on GitHub.

Frequently asked about E-Commerce Product Ranking

What else does asgard-ai-platform publish alongside E-Commerce Product Ranking?
E-Commerce Product Ranking is one of 25 skills that DirSkills catalogs from asgard-ai-platform/skills, the repository it ships in. Its siblings there include ARIMA Time Series Model, Ad Bidding Strategies and Ad Budget Allocation. Each one is a separate skill with its own page in this directory, installs the same way E-Commerce Product Ranking does, and is maintained by asgard-ai-platform in that same repository. The rest of the collection is listed on the asgard-ai-platform/skills page.
How does E-Commerce Product Ranking compare to other AI Engineering skills?
E-Commerce Product Ranking ranks #2500 by stars among the 2793 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of E-Commerce Product Ranking against them. Open each page to compare what they document and how they install.