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 alfworld-appliance-preparergit 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/alfworld-appliance-preparer)<a href="https://agentmods.dev/skills/zjunlp/skillnet/alfworld-appliance-preparer"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/alfworld-appliance-preparer/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/alfworld-appliance-preparer"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/alfworld-appliance-preparer.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 | $0.00084 | $0.00561 |
| Opus 5 | $0.00042 | $0.00280 |
| Sonnet 5 | $0.00017 | $0.00112 |
| Haiku 4.5 | $0.00008 | $0.00056 |
Grade A, and why
alfworld-appliance-preparer 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 10d 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
Goal
Prepare a specified household appliance for immediate use by ensuring it is in the correct open or closed state. This is a prerequisite step before performing actions like heat, cool, or toggle with the appliance.
Input
- appliance_identifier: The name of the appliance to prepare (e.g.,
microwave 1,toaster 1,fridge 1).
Core Logic
- Navigate to the Appliance: First, go to the location of the target appliance.
- Check State & Prepare: Determine if the appliance needs to be opened or closed based on the intended subsequent action. The standard rule is:
- For appliances used to contain items for processing (e.g., microwave, oven, fridge), they typically need to be open to receive the item.
- Use the
open {appliance}orclose {appliance}action as needed.
- Confirm Readiness: The skill is complete when the appliance is in the correct state, confirmed by an observation from the environment (e.g., "The microwave 1 is open.").
Important Considerations
- State Awareness: Always observe the environment's feedback after each action (e.g., "The microwave 1 is closed."). Do not assume the state.
- Error Handling: If the action fails (environment outputs "Nothing happened"), the appliance may already be in the desired state. Re-check the observation and proceed.
- Trajectory Insight: Refer to the example in
references/trajectory_example.mdto see a practical application of this skill in the context of a larger task.
Example
Input: appliance_identifier: microwave 1
Sequence:
go to microwave 1→ Observation: "You are at microwave 1. The microwave 1 is closed."open microwave 1→ Observation: "You open the microwave 1. The microwave 1 is open."
Output: "The microwave 1 is open and ready for use."
Output
A confirmation that the appliance is ready, typically in the form of the agent's Thought summarizing the prepared state and the environment's observation.
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.
- 10d ago First seen · 37 lines · 84 tokens per session scan A a67b15480cad
alfworld-appliance-preparer is a skill published in the GitHub repository zjunlp/SkillNet (1,256 stars, last pushed yesterday), licensed MIT. It adds 84 tokens to every session and 561 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 2026-08-30.
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