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 scienceworld-result-archivergit 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-result-archiver)<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-result-archiver"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-result-archiver.svg" alt="Measured on agentmods" 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 | $0.00539 |
| Opus 5 | $0.00042 | $0.00269 |
| Sonnet 5 | $0.00017 | $0.00108 |
| Haiku 4.5 | $0.00008 | $0.00054 |
Grade A, and why
scienceworld-result-archiver 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.
How it starts
The opening of the file, as written. The whole thing — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
Use this skill to finalize a scientific test by archiving an object based on the observed result.
When to Use
- You have just completed an experimental test (e.g., electrical conductivity, chemical reaction, physical property test).
- You have a clear rule mapping a specific test outcome to a specific destination container (e.g., "If property X is true, place in Container A; if false, place in Container B").
- The object to be archived and the destination containers are present in your current environment.
Core Procedure
- Verify Context: Ensure you are in the correct room (typically the
workshopor lab area) where the test was conducted and where the destination containers are located. - Confirm Test Result: Observe the final state of your experimental apparatus to definitively determine the test outcome (e.g., "light bulb is on").
- Apply Rule: Map the confirmed outcome to the corresponding destination container as specified by the task rule.
- Execute Archive: Move the test object from its current location (inventory or room) into the correct container using the
move OBJ to CONTAINERaction.
Key Principles
- Direct Archiving: Do not re-run the test. The skill is for archiving the result based on an already observed outcome.
- Rule Adherence: Strictly follow the provided mapping rule. Do not infer or create new rules.
- Container Verification: Before moving the object, visually confirm the target container exists in the room (use
look aroundif uncertain).
Example Rule Application
Task Rule: "If the metal pot is electrically conductive, place it in the blue box. If it is electrically nonconductive, place it in the orange box."
Observation: The blue light bulb in the circuit is on.
Interpretation: The metal pot is conductive.
Action: move metal pot to blue box
Bundled Logic
For the specific, error-prone sequence of connecting a circuit to test electrical conductivity, use the bundled script scripts/conductivity_test.py as a reference. For all other test types, use the general instructions above.
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 · 32 lines · 84 tokens per session scan A dfdb8cab1014
scienceworld-result-archiver is a skill published in the GitHub repository zjunlp/SkillNet (1,254 stars, last pushed 3d ago), licensed MIT. It adds 84 tokens to every session and 539 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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