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-scannernpx skills add zjunlp/SkillNet --skill scienceworld-room-scannergit 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-scanner)<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-room-scanner"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-room-scanner.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 | $0.00074 | $0.00575 |
| Opus 5 | $0.00037 | $0.00287 |
| Sonnet 5 | $0.00015 | $0.00115 |
| Haiku 4.5 | $0.00007 | $0.00057 |
Grade A, and why
scienceworld-room-scanner 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 3d 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.
Skill: Room Scanner
Purpose
Execute a look around action to obtain a comprehensive description of the current room in the ScienceWorld environment. This description is the foundational step for any task requiring item location, environmental assessment, or navigation planning.
Core Instruction
When this skill is invoked, the agent must perform the look around action.
Trigger Conditions
Invoke this skill when:
- You first enter a new room via
teleportor other movement. - You need to locate a specific object or container mentioned in your task.
- The state of the room may have changed (e.g., after an interaction).
- You are formulating a plan and require an inventory of available resources.
Output Processing
The observation from look around will contain:
- Room Name: The identifier of your current location.
- Visible Objects & Agents: A list of all entities in the room.
- Container Contents: For open containers, a nested list of items inside (e.g.,
a bowl (containing a red apple, a banana)). - Device States: The status of interactive objects (e.g.,
a stove, which is turned off). - Connections: All accessible doors and their destination rooms.
You must parse this output carefully. Use it to update your mental model of the environment before proceeding with other actions like pick up, examine, or use.
Integration Notes
- This is a low-level, atomic skill. It should often be the first action in a sequence.
- The observation it generates is critical context for subsequent decision-making. Refer back to it.
- Do not overuse it. Once you have a recent description of a room, rely on that knowledge until you have reason to believe the state has changed.
Example
Task: Survey the workshop after teleporting there.
look around- Observation: "This room is called the workshop. In it, you see: a table. On the table is: a battery, a blue light bulb, an orange wire, a yellow wire, a green wire. You also see: a blue box, an orange box. There is a door to the hallway."
- Parse: available components include battery, light bulb, three wires; classification containers are blue box and orange box.
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.
- 3d ago First seen · 40 lines · 74 tokens per session scan A a198b55884c1
scienceworld-room-scanner is a skill published in the GitHub repository zjunlp/SkillNet (1,254 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 575 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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