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-object-selectornpx skills add zjunlp/SkillNet --skill scienceworld-object-selectorgit 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-object-selector)<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-object-selector"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-object-selector.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 | $0.00079 | $0.00653 |
| Opus 5 | $0.00039 | $0.00327 |
| Sonnet 5 | $0.00016 | $0.00131 |
| Haiku 4.5 | $0.00008 | $0.00065 |
Grade A, and why
scienceworld-object-selector 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 yesterday.
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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: scienceworld-object-selector
Purpose
Identify and select the correct object from visible candidates in the ScienceWorld environment based on task-defined criteria (e.g., "non-living thing", "electrical component", "container"), then signal intent with a focus on action.
When to Use
- When a task requires choosing a specific type of object from multiple visible options
- When the agent must classify objects by property (living/non-living, material type, function) to select one
- When the task instructs finding and interacting with a particular category of object
Workflow
- Survey the environment -- Use
look aroundto list all visible objects in the current location. - Evaluate candidates -- For each visible object, determine if it matches the task criteria. Exclude target containers (e.g., destination boxes) and fixed furniture from candidates.
- Select the best match -- Choose the first suitable, portable object unless the task specifies a preference (e.g., "largest", "closest").
- Signal intent -- Execute
focus on [OBJECT]to confirm selection. - Execute the task action -- Proceed with the required action (e.g.,
pick up,move,use).
Examples
Example 1: Find a non-living thing and move it
Task: "Find a non-living thing. First, focus on the thing. Then, move it to the purple box."
> look around
You see: purple box, table, battery, black wire, blue light bulb, red light bulb, red wire, switch, violet light bulb, yellow wire, ultra low temperature freezer.
Evaluation: Purple box is the destination. Table and freezer are fixed furniture. Remaining items (battery, wires, bulbs, switch) are all non-living and portable.
> focus on black wire
You focus on the black wire.
> move black wire to purple box
You move the black wire to the purple box.
Example 2: Find an electrical component
Task: "Find an electrical component and place it on the workbench."
> look around
You see: workbench, chair, battery, glass cup, red wire, notebook.
> focus on battery
You focus on the battery.
> move battery to workbench
You move the battery to the workbench.
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.
- yesterday First seen · 66 lines · 79 tokens per session scan A 7a7abdb5fd1f
scienceworld-object-selector is a skill published in the GitHub repository zjunlp/SkillNet (1,253 stars, last pushed today), licensed MIT. It adds 79 tokens to every session and 653 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.
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.