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-task-interpreternpx skills add zjunlp/SkillNet --skill scienceworld-task-interpretergit 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-task-interpreter)<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-task-interpreter"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-task-interpreter.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 | $0.00106 | $0.00416 |
| Opus 5 | $0.00053 | $0.00208 |
| Sonnet 5 | $0.00021 | $0.00083 |
| Haiku 4.5 | $0.00011 | $0.00042 |
Grade A, and why
scienceworld-task-interpreter 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.
What it actually says
Instructions
Activate this skill when the user provides a new task instruction for the ScienceWorld environment. Your primary function is to interpret the instruction and generate a clear, executable plan.
1. Parse the Task
Read the user's instruction carefully. Extract the following core components:
- Primary Objective: What is the ultimate goal? (e.g., "find", "compare", "manipulate").
- Target Object/Subject: What is the main focus of the task? (e.g., "animal with the shortest life span").
- Specified Location: Is a location explicitly mentioned? (e.g., "animals are in the 'outside' location").
2. Formulate the Plan
Based on the parsed components, construct a plan. The standard plan structure is:
- Navigate: If a target location is specified and you are not there, teleport to it immediately.
- Observe: Upon arriving at the correct location, use
look aroundto survey the environment and identify relevant objects. - Analyze & Execute: Apply domain knowledge or comparative reasoning to the observed objects to fulfill the primary objective. Then, take the final required action (e.g.,
focus on [OBJECT],pick up [OBJECT]).
3. Output the Interpretation
Before taking the first action, articulate your interpretation and plan in a Thought step. This confirms the skill has correctly parsed the task. Output Format:
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 · 24 lines · 106 tokens per session scan A 7d92518df5f0
scienceworld-task-interpreter is a skill published in the GitHub repository zjunlp/SkillNet (1,253 stars, last pushed 15d ago), licensed MIT. It adds 106 tokens to every session and 416 once invoked, about $0.0005 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.