Amazon Buy Box Monitor
browser-act/skills
Pulls Amazon product details, competing seller prices and seller ratings for a given ASIN through the BrowserAct API, without browser automation.
Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics.
Install Algo Ecom Ranking
$ npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-ranking --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-ecom-ranking .claude/skills/algo-ecom-ranking && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "algo-ecom-ranking" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-ranking into .claude/skills/algo-ecom-ranking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-ranking", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-rankingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-ranking --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-ecom-ranking .agents/skills/algo-ecom-ranking && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-ecom-ranking" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-ranking into .agents/skills/algo-ecom-ranking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-ranking", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-ranking --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-ecom-ranking .cursor/skills/algo-ecom-ranking && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "algo-ecom-ranking" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-ranking into .cursor/skills/algo-ecom-ranking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-ranking", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-ecom-ranking--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-ranking --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-ecom-ranking .gemini/skills/algo-ecom-ranking && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "algo-ecom-ranking" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-ranking into .gemini/skills/algo-ecom-ranking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-ranking", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills algo-ecom-rankingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-ecom-ranking .github/skills/algo-ecom-ranking && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "algo-ecom-ranking" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-ranking into .github/skills/algo-ecom-ranking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-ranking", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-ranking --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-ecom-ranking .opencode/skills/algo-ecom-ranking && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "algo-ecom-ranking" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-ranking into .opencode/skills/algo-ecom-ranking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-ranking", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
algo-ecom-ranking- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 395 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to build a product ranking system beyond text relevance
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Ecom Ranking is an agent skill from asgard-ai-platform/skills. Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Use this skill when the user needs to build a product ranking system beyond text relevance, balance relevance with commercial objectives, or implement learning-to-rank — even if they say 'product sorting', 'search result ranking', or 'how to rank products'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/lambdamart.md` and `references/position-debiasing.md`).
It sits in Sales & Support, covering E-commerce operations. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to build a product ranking system beyond text relevance
- Balance relevance with commercial objectives
- Implement learning-to-rank — even if they say product sorting
- Search result ranking
Example prompts
- “product sorting”
- “search result ranking”
- “how to rank products”
- “/algo-ecom-ranking”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Algo Ecom Ranking loads about 1.1k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 395 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 395 words, ~1,089 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-ecom-ranking/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-ecom-ranking
- description
- Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Use this skill when the user needs to build a product ranking system beyond text relevance, balance relevance with commercial objectives, or implement learning-to-rank — even if they say 'product sorting', 'search result ranking', or 'how to rank products'.
- metadata.category
- WP-43 電商搜尋演算法
- metadata.tags
- ecommerce, ranking, learning-to-rank, multi-objective
E-Commerce Product Ranking
Overview
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
IRON LAW: Relevance Is Necessary But NOT Sufficient for E-Commerce Ranking
A result that is textually relevant but has zero sales history, no
reviews, and is out of stock serves no one. E-commerce ranking must
balance: relevance (does it match the query?), quality (is it a good
product?), and commercial value (does it generate revenue?).Phase 1: Input Validation
Collect features per product-query pair: text relevance score (BM25), historical CTR, conversion rate, average rating, review count, price competitiveness, inventory level, margin. Gate: Minimum features available, click data from 30+ days.
Phase 2: Core Algorithm
Rule-based baseline: Score = w₁×relevance + w₂×popularity + w₃×rating + w₄×recency. Manually tune weights.
LTR approach:
- Generate training data from click logs (clicked = positive, skipped = negative, with position debiasing)
- Features: text match, behavioral (CTR, add-to-cart rate), product quality (rating, reviews), freshness, price
- Train: LambdaMART or gradient-boosted ranking model optimizing NDCG
- Blend: final_score = α × LTR_score + (1-α) × business_boost
Phase 3: Verification
Evaluate offline: NDCG@10, MRR. A/B test online: revenue per search, click-through rate, conversion rate. Gate: NDCG improves over baseline, A/B test positive on primary metric.
Phase 4: Output
Return ranked product list with score decomposition.
Output Format
{
"results": [{"product_id": "P123", "rank": 1, "final_score": 0.92, "components": {"relevance": 0.85, "popularity": 0.95, "quality": 0.90}}],
"metadata": {"query": "wireless earbuds", "model": "lambdamart", "ndcg_at_10": 0.72}
}Examples
Sample I/O
Input: Query "laptop", 500 matching products Expected: Top results balance text match + high conversion + good ratings, not just keyword relevance.
Show full SKILL.md (152 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| New product, no history | Rely on text relevance + category avg | Cold start — no behavioral signal |
| Out of stock item | Demote or remove | Showing unavailable products frustrates users |
| Sponsored product | Blend ad rank with organic | Separate sponsored from organic clearly |
Gotchas
- Position bias in training data: Higher-ranked items get more clicks regardless of quality. Debias training data using inverse propensity weighting or randomization experiments.
- Popularity bias: Without diversity controls, popular items dominate rankings. New or niche products get no exposure. Add exploration bonus.
- Revenue optimization ≠ user satisfaction: Ranking by margin pushes expensive products up. Users lose trust if results feel commercially manipulated.
- Feature freshness: Click signals change daily. Retrain or update features frequently. Stale features degrade ranking quality.
- Category-specific models: A single ranking model may not work across all categories. Electronics ranking differs from fashion ranking.
References
- For LambdaMART implementation, see
references/lambdamart.md - For position debiasing techniques, see
references/position-debiasing.md
© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in algo-ecom-ranking of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/lambdamart.md
- references/position-debiasing.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Ecom Ranking next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Algo Ecom Ranking this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Amazon Buy Box Monitorbrowser-act/skills | 6.1k | 1 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Tourmind Bookingtourmind-com/Tourmind-Booking-Skills | 1.8k | — | ~13k | Automated safety check: Pass | MIT | |
| Ecommerce Image Suitewzj177/ecommerce-image-suite | 449 | — | ~10k | Automated safety check: Pass | Apache-2.0 | |
| Zach Feature Demand Validatorzach22-1999/amazon-skills | 209 | 1 repos | ~2.3k | Automated safety check: Notes | MIT | |
| Caramel CouponsDevinoSolutions/caramel | 141 | — | ~1.1k | Automated safety check: Pass | AGPL-3.0 |
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Related
Categories
Questions about Algo Ecom Ranking
What does Algo Ecom Ranking do?
Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Algo Ecom Ranking is an agent skill from asgard-ai-platform/skills. Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics.
When should I use Algo Ecom Ranking?
Algo Ecom Ranking fits situations like: the user needs to build a product ranking system beyond text relevance; balance relevance with commercial objectives; implement learning-to-rank — even if they say product sorting; search result ranking.
How do I install Algo Ecom Ranking in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a claude-code`. Or copy the skill folder (algo-ecom-ranking in asgard-ai-platform/skills) into .claude/skills/algo-ecom-ranking in your project. Claude Code loads it when a task matches its description.
How do I install Algo Ecom Ranking in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a codex`. Or copy the skill folder (algo-ecom-ranking in asgard-ai-platform/skills) into .agents/skills/algo-ecom-ranking in your project. Codex loads it when a task matches its description.
Can I use Algo Ecom Ranking in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-ecom-ranking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-ecom-ranking, .gemini/skills/algo-ecom-ranking, .github/skills/algo-ecom-ranking and .opencode/skills/algo-ecom-ranking in your project.
What does Algo Ecom Ranking need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Ecom Ranking is instructions for the agent only.
Does Algo Ecom Ranking access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Algo Ecom Ranking safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Algo Ecom Ranking use?
Algo Ecom Ranking is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Algo Ecom Ranking use?
About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.1k tokens, read only when the agent opens those files.
What are the alternatives to Algo Ecom Ranking?
Skills that share tags, products or a category with Algo Ecom Ranking: Amazon Buy Box Monitor (browser-act/skills, 6.1k stars), Tourmind Booking (tourmind-com/Tourmind-Booking-Skills, 1.8k stars), Ecommerce Image Suite (wzj177/ecommerce-image-suite, 449 stars) and Zach Feature Demand Validator (zach22-1999/amazon-skills, 209 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Ecom Ranking?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.