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-material-classifiernpx skills add zjunlp/SkillNet --skill scienceworld-material-classifiergit 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-material-classifier)<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-material-classifier"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-material-classifier.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 | $0.00069 | $0.00336 |
| Opus 5 | $0.00034 | $0.00168 |
| Sonnet 5 | $0.00014 | $0.00067 |
| Haiku 4.5 | $0.00007 | $0.00034 |
Grade A, and why
scienceworld-material-classifier 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.
What it actually says
Material Classification Skill
When to Use
Activate when direct experimental testing of a material property (conductivity, magnetism, etc.) fails or equipment is unavailable, and you need to classify the material by inference to complete a sorting task.
Procedure
focus on <OBJECT>— identify the target and note its material composition.- Attempt direct testing if equipment exists (e.g.,
connect <OBJECT> terminal 1 to <WIRE> terminal 2). - If testing fails, infer the property from the object's material. Consult
references/material_properties.mdfor lookup. move <OBJECT> to <CONTAINER>— place in the appropriate classification container.look at <CONTAINER>— verify the object was placed correctly.
Example
Task: Classify a glass jar for electrical conductivity when the circuit test is unavailable.
focus on glass jarconnect glass jar terminal 1 to yellow wire terminal 2— action fails (invalid connection).- Inference: glass is an electrical insulator → non-conductive.
move glass jar to orange boxlook at orange box— observation: "containing a glass jar" — classification complete.
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.
- 2d ago First seen · 24 lines · 69 tokens per session scan A f404b0ce03fe
scienceworld-material-classifier is a skill published in the GitHub repository zjunlp/SkillNet (1,254 stars, last pushed 2d ago), licensed MIT. It adds 69 tokens to every session and 336 once invoked, about $0.0003 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.
Other skills, from other repositories
ddr-globem-analysis
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones, generating QA pairs about behavioral/psychological changes…
mimic-iv-patient-analysis
Comprehensive strategy for analyzing individual patient records in MIMIC-IV EHR database and generating high-quality, diverse QA pairs. Use this skill whenever the task involves analyzing a specific patient's clinical data from MIMIC-IV (or similar EHR databases), querying across hospital and ICU tables, and…
globem-user-analysis
Comprehensive individual-user analysis on the GLOBEM dataset — a longitudinal passive-sensing + mental-health study of college students. Use this skill whenever a task involves analyzing a specific participant (e.g. "Analyze user INS-W002") from the GLOBEM dataset, exploring behavioral patterns from smartphone…
mimic-patient-analysis
Comprehensive patient analysis using the MIMIC-IV clinical database. Use this skill whenever asked to analyze, summarize, or investigate a patient's medical history, hospital admissions, diagnoses, medications, procedures, or clinical course from a MIMIC-IV SQLite database. Triggers on prompts like "Analyze patient…
auto-experiment
Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENTPLAN.md, routes mechanism family inline (Phase 1.5), implements experiment code, deploys to GPU, and collects initial results. Use when user says "implement experiments", "experiment", "deploy the plan", or has an experiment plan ready to…
hypothesis-batch
Automated pipeline for generating and refining multiple research hypotheses.