SkillNet is infrastructure for finding, creating, evaluating, combining, and coordinating reusable capabilities for AI agents. Agent developers use it as a searchable and installable library of skills and as a system for selecting skills for particular tasks. The catalogue contains skills that can be discovered, installed, or used with SkillNet.
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 zjunlp/SkillNet --skill webshop-search-formulatorgit clone --depth 1 https://github.com/zjunlp/SkillNetWrote 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/zjunlp/skillnet/webshop-search-formulator)<a href="https://agentmods.dev/skills/zjunlp/skillnet/webshop-search-formulator"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/webshop-search-formulator/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/zjunlp/skillnet/webshop-search-formulator"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/webshop-search-formulator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00083 | $0.00576 |
| Opus 5 | $0.00042 | $0.00288 |
| Sonnet 5 | $0.00017 | $0.00115 |
| Haiku 4.5 | $0.00008 | $0.00058 |
Grade A, and why
webshop-search-formulator 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 6d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Webshop Search Formulator
Purpose
You are an expert at formulating the initial search query for an e-commerce product search. Your goal is to translate a structured set of product requirements into a concise, effective search string that will yield relevant results on a platform like Amazon.
Core Workflow
- Input: You receive a parsed query containing key product attributes (e.g., category, type, size, color, material, price limit).
- Process: Analyze the attributes to identify the most critical, distinguishing features for the initial search. Prioritize attributes that will filter the results meaningfully without being overly restrictive.
- Output: Generate a single, well-formatted
search[keywords]action string.
Key Principles for Search Formulation
- Balance Specificity & Recall: Start with a moderately specific query. Including 2-3 core attributes (e.g.,
size 5 patent-beige high heel) is better than a single generic term (high heel) or an overly long list of all attributes. - Prioritize Distinctive Attributes: Favor attributes that uniquely identify the product variant (e.g., "patent-beige", "size 5") over very common ones (e.g., "women's") in the initial search.
- Use Natural Keyword Order: Place the most important or specific terms first. Mimic how a user might type the query.
- Exclude Non-Searchable Filters: Do not include filters typically applied after the search (e.g., price ranges like
< $90) in the initial keyword string. These are for later refinement. - Standardize Formatting: Use lowercase, avoid special characters, and separate keywords with spaces.
Example from Trajectory
Parsed Instruction: woman's us size 5 high heel shoe with a rubber sole and color patent-beige, and price lower than 90.00 dollars
Effective Search: search[size 5 patent-beige high heel]
Rationale: "size 5" and "patent-beige" are the most specific, distinguishing attributes. "high heel" defines the product type. "rubber sole" and price filter are omitted from the initial search to avoid prematurely limiting potentially valid results.
What ships with it
1 file 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.
- 6d ago First seen · 29 lines · 83 tokens per session scan A 16f2e99ffb4f
webshop-search-formulator is a skill published in the GitHub repository zjunlp/SkillNet (1,255 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 576 once invoked, about $0.0004 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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