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/alfworld-clean-objectnpx skills add zjunlp/SkillNet --skill alfworld-clean-objectgit 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-clean-object)<a href="https://agentmods.dev/skills/zjunlp/skillnet/alfworld-clean-object"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/alfworld-clean-object.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 | $0.00072 | $0.00428 |
| Opus 5 | $0.00036 | $0.00214 |
| Sonnet 5 | $0.00014 | $0.00086 |
| Haiku 4.5 | $0.00007 | $0.00043 |
Grade A, and why
alfworld-clean-object 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 4d 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.
What it actually says
Instructions
Clean an object you are holding using a sinkbasin. The object must be in your inventory before cleaning.
Workflow
- Navigate:
go to sinkbasin 1(or the appropriate sinkbasin in the environment) - Clean:
clean {object} with sinkbasin 1-- verify observation confirms "You clean the {object}" - Proceed: The object is now clean. Continue with the next task step
Action Format
clean {obj} with {recep}(e.g.,clean potato 1 with sinkbasin 1)
Error Recovery
- "Nothing happened": Check (1) you are holding the object, (2) you are at the sinkbasin, (3) object and receptacle names are correct
- Not at sinkbasin: execute
go to sinkbasin 1first
Example
Scenario: You are holding potato 1 and need to clean it.
Thought: I need to clean this potato. I should go to the sinkbasin.
Action: go to sinkbasin 1
Observation: On the sinkbasin 1, you see nothing.
Action: clean potato 1 with sinkbasin 1
Observation: You clean the potato 1 using the sinkbasin 1.
Result: The potato is now in a clean state and ready for the next task step.
Post-Condition
After successful execution, the object will be in a clean state. You may proceed with the next step of your task (e.g., placing the clean object on a shelf or in a microwave).
What ships with it
2 files 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.
- 4d ago First seen · 37 lines · 72 tokens per session scan A 6cdc4f117182
alfworld-clean-object is a skill published in the GitHub repository zjunlp/SkillNet (1,253 stars, last pushed 14d ago), licensed MIT. It adds 72 tokens to every session and 428 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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