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 charlieviettq/awesome-agent-skill --skill algo-ecom-searchgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/algo-ecom-search)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-ecom-search"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ecom-search/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/charlieviettq/awesome-agent-skill/algo-ecom-search"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ecom-search.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 | $0.00069 | $0.01042 |
| Opus 5 | $0.00034 | $0.00521 |
| Sonnet 5 | $0.00014 | $0.00208 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
"algo-ecom-search" 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 13d 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.
This is a copy
95% identical to algo-ecom-search — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
3 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.
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.
- 13d ago First seen · 87 lines · 69 tokens per session scan A 965afe8b22ee
"algo-ecom-search" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 1,042 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to algo-ecom-search, differing in 8 lines, and is treated as a copy.
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