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-product-evaluatorgit 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-product-evaluator)<a href="https://agentmods.dev/skills/zjunlp/skillnet/webshop-product-evaluator"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/webshop-product-evaluator/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-product-evaluator"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/webshop-product-evaluator.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 | $0.00068 | $0.00649 |
| Opus 5 | $0.00034 | $0.00324 |
| Sonnet 5 | $0.00014 | $0.00130 |
| Haiku 4.5 | $0.00007 | $0.00065 |
Grade A, and why
webshop-product-evaluator 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
When to Use
Activate this skill when you are on a search results page in a web shopping environment and need to evaluate multiple products against specific user requirements (e.g., price limits, feature keywords).
Core Process
- Parse the Observation: Identify the user's instruction, the list of available products, and their associated details (Title, Price, ASIN/Product ID).
- Extract Requirements: From the user instruction, identify:
- Price Limit: The maximum acceptable price (e.g., "lower than 40.00 dollars").
- Feature Keywords: Key product attributes (e.g., "teeth whitening", "freshens breath").
- Evaluate Products: For each product listing on the current page:
- Check if the price is below the user's limit.
- Check if the product title contains the required feature keywords.
- Prioritize products that meet all criteria. If multiple products qualify, choose the one that appears most relevant or cost-effective.
- Take Action:
- If a suitable product is found, click on its product ID (e.g.,
click[B09NYFDNVX]) to view its details. - If no suitable product is found on the current page, consider using the
searchaction with refined keywords or clickingNext >to browse more results.
- If a suitable product is found, click on its product ID (e.g.,
Thought Process Format
Always structure your internal reasoning and final action using this format:
Thought: [Summarize the user's constraints, evaluate each product against them, and justify your selection.]
Action: click[<product_id>]
Example
Instruction: "i need a teeth whitening toothpaste that also freshens breath, and price lower than 15.00 dollars"
Observation (Search Results Page):
- B09NYFDNVX: "Whitening Toothpaste with Breath Freshener" — $12.99
- B08KZPTL3M: "Professional Teeth Whitening Kit" — $24.99
- B07HQRJ4WP: "Mint Fresh Whitening Toothpaste" — $9.49
Thought: The user needs a teeth whitening toothpaste that freshens breath, under $15. Product B09NYFDNVX at $12.99 mentions both "whitening" and "breath freshener" in the title — meets all criteria. B08KZPTL3M exceeds the budget at $24.99. B07HQRJ4WP is within budget and mentions "whitening" but does not explicitly mention breath freshening. Selecting B09NYFDNVX as the best match.
Action: click[B09NYFDNVX]
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 · 45 lines · 68 tokens per session scan A e35f0e7d44d2
webshop-product-evaluator is a skill published in the GitHub repository zjunlp/SkillNet (1,256 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 649 once invoked, about $0.0003 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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