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 agentmods add skills/zjunlp/skillnet/scienceworld-room-explorernpx skills add zjunlp/SkillNet --skill scienceworld-room-explorergit 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/scienceworld-room-explorer)<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-room-explorer"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-room-explorer.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.00556 |
| Opus 5 | $0.00040 | $0.00278 |
| Sonnet 5 | $0.00016 | $0.00111 |
| Haiku 4.5 | $0.00008 | $0.00056 |
Grade A, and why
scienceworld-room-explorer 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 2d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
Purpose
Use this skill to establish a foundational understanding of your immediate environment. It is the critical first step for any task requiring object interaction, inventory assessment, or spatial reasoning.
When to Use
- Upon entering a new room.
- When the task requires identifying available resources.
- Before planning a complex sequence of actions (e.g., building a circuit, mixing chemicals).
- If the state of the environment may have changed (e.g., after using an object, or if a long time has passed).
Core Action
Execute the look around action. This is the only action required for this skill.
Output Processing
- The observation returned by
look aroundis your primary output. - Parse this observation to create a mental inventory. Key elements to note:
- Room Name: The identified location.
- Objects: List all items, containers, and devices.
- Object States: Note if devices are on/off, containers are open/closed, or if objects contain other items.
- Connections: Note any pre-existing wire connections or object placements.
- Exits: Identify doors to other rooms and their state (open/closed).
Example
Scenario: Agent teleports to the kitchen and needs to identify available resources.
teleport to kitchenlook around- Observation: "This room is called the kitchen. In it, you see: a fridge (containing chocolate, milk), a metal pot, a thermometer, a counter, a stove (which is turned off). You also see: a door to the hallway (which is open)."
- Parsed inventory: fridge (open, contains chocolate and milk), metal pot, thermometer, counter, stove (off), exit to hallway.
Integration with Other Skills
The observation generated by this skill is essential context for downstream tasks. Before performing actions like pick up, connect, or use, always ensure your understanding of object locations and states is current by running this skill if needed.
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.
- 2d ago First seen · 40 lines · 80 tokens per session scan A d632b678fdb8
scienceworld-room-explorer is a skill published in the GitHub repository zjunlp/SkillNet (1,253 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 556 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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