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.
Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation.
Install Algo Ecom Search
$ npx skills add asgard-ai-platform/skills --skill algo-ecom-search -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-search --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-search .claude/skills/algo-ecom-search && 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-search" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-search into .claude/skills/algo-ecom-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-search", 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-searchType 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-search -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-search --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-search .agents/skills/algo-ecom-search && 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-search" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-search into .agents/skills/algo-ecom-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-search", 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-search -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-search --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-search .cursor/skills/algo-ecom-search && 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-search" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-search into .cursor/skills/algo-ecom-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-search", 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-search--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-search -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-ecom-search --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-search .gemini/skills/algo-ecom-search && 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-search" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-search into .gemini/skills/algo-ecom-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-search", 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-searchInstalls 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-search -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-search .github/skills/algo-ecom-search && 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-search" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-search into .github/skills/algo-ecom-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-search", 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-search -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-search --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-search .opencode/skills/algo-ecom-search && 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-search" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-ecom-search into .opencode/skills/algo-ecom-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-ecom-search", 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-search- GitHub stars
- 242
- Token cost
- ~1.2k tokens
- SKILL.md length
- 426 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to improve search quality
- 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 Search is an agent skill from asgard-ai-platform/skills. Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.
Its SKILL.md is about 1.2k 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/query-pipeline.md` and `references/relevance-evaluation.md`).
It sits in Sales & Support, covering Search implementation and 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 improve search quality
- Implement query processing features
- Diagnose search relevance issues — even if they say search results are bad
- Improve product search
Example prompts
- “search results are bad”
- “improve product search”
- “search relevance optimization”
- “/algo-ecom-search”
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 Search loads about 1.2k tokens when it runs, and up to ~6.9k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 426 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). 426 words, ~1,184 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-ecom-search/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-ecom-search
- description
- Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.
- metadata.category
- WP-43 電商搜尋演算法
- metadata.tags
- ecommerce, search, relevance, query-understanding
E-Commerce Search Relevance
Overview
E-commerce search is a pipeline: query understanding → retrieval → ranking → presentation. Each stage affects relevance. Optimization requires diagnosing WHICH stage fails, not just tuning one component. Zero-result rate, click-through rate, and add-to-cart rate are key metrics.
When to Use
Trigger conditions:
- Diagnosing why search results don't meet user expectations
- Implementing query processing features (spell check, synonyms, intent detection)
- Reducing zero-result searches and improving conversion
When NOT to use:
- For ranking algorithm design only (use e-commerce ranking skill)
- For text relevance scoring only (use BM25)
Algorithm
IRON LAW: Search Quality Is Determined by the WEAKEST Pipeline Stage
Query understanding, retrieval, ranking, and presentation are sequential.
Perfect ranking cannot fix bad retrieval (missing products). Perfect
retrieval cannot fix bad query understanding (wrong intent). Diagnose
which stage fails FIRST before optimizing.Phase 1: Input Validation
Audit current search: sample 100 queries by volume. For each, evaluate: query understanding (correct intent?), retrieval (relevant products in candidate set?), ranking (best products at top?), presentation (useful display?). Gate: Weakness localized to specific pipeline stage(s).
Phase 2: Core Algorithm
Query understanding: 1. Spell correction (edit distance, n-gram). 2. Synonym expansion (earbuds↔earphones). 3. Intent classification (product search vs brand search vs category browse). 4. Query rewriting (attribute extraction: "red shoes size 10" → color:red, category:shoes, size:10).
Retrieval optimization: 1. Multi-field search (title, description, brand, category, SKU). 2. Boosting strategies (title match > description match). 3. Filter vs boost (hard constraints: category, availability vs soft signals: popularity).
Result quality: 1. Zero-result fallback (relax query, suggest alternatives). 2. Faceted navigation (filters by price, brand, rating). 3. Did-you-mean suggestions.
Phase 3: Verification
Measure: zero-result rate (<5% target), CTR on first page (>30% target), NDCG on judged queries. Gate: Key metrics improve over baseline.
Phase 4: Output
Return search audit with prioritized improvements.
Output Format
{
"audit": {"zero_result_rate": 0.08, "avg_ctr": 0.25, "top_failing_queries": ["earbuds wireless", "gift ideas"]},
"recommendations": [{"stage": "query_understanding", "issue": "no_synonym_expansion", "impact": "high", "fix": "Add earbuds↔earphones synonym"}],
"metadata": {"queries_sampled": 100, "period": "2025-Q1"}
}Examples
Show full SKILL.md (174 more words)Show less
Sample I/O
Input: "wireles earbud" (misspelled) returns 0 results Expected: Spell correction → "wireless earbuds" → relevant products displayed. Recommendation: implement spell correction.
Edge Cases
| Input | Expected | Why |
|---|---|---|
| Category-only query ("shoes") | Browse intent, show popular | Not a specific product search |
| Brand misspelling | Fuzzy brand matching | "Nikee" → "Nike" |
| Long-tail query ("blue cotton v-neck t-shirt men XL") | Attribute parsing needed | Multiple structured attributes in free text |
Gotchas
- Synonym maintenance: Synonym lists need ongoing curation. "AirPods" is a brand, not a synonym for "earbuds." Wrong synonyms hurt precision.
- Over-recall: Aggressive synonym expansion and fuzzy matching return too many irrelevant results. Balance recall (find everything) with precision (only relevant).
- Language-specific challenges: Chinese search needs word segmentation. "皮鞋" (leather shoes) should not match "拖鞋" (slippers) despite shared "鞋".
- Search analytics are essential: Without tracking query-level CTR, zero-result queries, and conversion rates, you're optimizing blind.
- A/B testing search is hard: Search changes affect all queries. Some improve, some regress. Measure aggregate metrics AND stratify by query type.
References
- For query understanding pipeline architecture, see
references/query-pipeline.md - For search relevance evaluation methodology, see
references/relevance-evaluation.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-search of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/query-pipeline.md
- references/relevance-evaluation.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Ecom Search 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 Search this skillasgard-ai-platform/skills | 242 | — | ~1.2k | 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 Search
What does Algo Ecom Search do?
Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Algo Ecom Search is an agent skill from asgard-ai-platform/skills. Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation.
When should I use Algo Ecom Search?
Algo Ecom Search fits situations like: the user needs to improve search quality; implement query processing features; diagnose search relevance issues — even if they say search results are bad; improve product search.
How do I install Algo Ecom Search in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-ecom-search -a claude-code`. Or copy the skill folder (algo-ecom-search in asgard-ai-platform/skills) into .claude/skills/algo-ecom-search in your project. Claude Code loads it when a task matches its description.
How do I install Algo Ecom Search in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-ecom-search -a codex`. Or copy the skill folder (algo-ecom-search in asgard-ai-platform/skills) into .agents/skills/algo-ecom-search in your project. Codex loads it when a task matches its description.
Can I use Algo Ecom Search 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-search -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-search, .gemini/skills/algo-ecom-search, .github/skills/algo-ecom-search and .opencode/skills/algo-ecom-search in your project.
What does Algo Ecom Search need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Ecom Search is instructions for the agent only.
Does Algo Ecom Search 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 Search 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 Search use?
Algo Ecom Search 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 Search use?
About 1.2k tokens (SKILL.md is roughly 4.7k 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 5.7k tokens, read only when the agent opens those files.
What are the alternatives to Algo Ecom Search?
Skills that share tags, products or a category with Algo Ecom Search: 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 Search?
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.