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-target-identifiernpx skills add zjunlp/SkillNet --skill scienceworld-target-identifiergit 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-target-identifier)<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-target-identifier"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-target-identifier.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 | $0.00064 | $0.00704 |
| Opus 5 | $0.00032 | $0.00352 |
| Sonnet 5 | $0.00013 | $0.00141 |
| Haiku 4.5 | $0.00006 | $0.00070 |
Grade A, and why
scienceworld-target-identifier 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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Target Object Identifier
Purpose
This skill enables you to systematically locate objects in the ScienceWorld environment that match a specific target description (e.g., "living thing", "container", "electrical device"). It transforms the raw observation text from look around into a structured list of candidate objects for your current task.
When to Use
- Trigger Condition: Immediately after executing
look aroundin any room. - Input Required: The full observation text from
look aroundAND the target description from your task. - Output: A prioritized list of matching objects with their locations and properties.
Execution Workflow
Step 1: Parse Observation
Extract all observable items from the room description. Pay special attention to:
- Objects listed after "Here you see:"
- Objects in containers (marked with "containing" or "On the X is:")
- Substances (marked as "a substance called")
- Living vs. non-living distinctions
Step 2: Apply Target Filter
Use the bundled classification script to filter objects based on the target description:
- For "living thing": Include animals, plants, eggs, and biological organisms
- For specific categories: Match against known object taxonomies
- For generic descriptions: Use semantic similarity matching
Step 3: Prioritize Candidates
Rank candidates by:
- Accessibility: Objects not in closed containers first
- Proximity: Objects in current room before other locations
- Task Relevance: Objects matching secondary task criteria (e.g., "easy to transport")
Step 4: Generate Action Plan
For each high-priority candidate:
- Note its exact name as it appears in observations
- Determine if
pick up,focus on, or other preliminary action is needed - Plan path to target location if specified in task
Key Considerations
- Exact Object Names: Use the exact phrasing from observations (e.g., "turtle egg" not "egg turtle")
- Container States: All containers are open per environment rules
- Teleportation: You can instantly move between rooms when searching
- Multiple Matches: If multiple objects match, select based on task context (e.g., choose less mobile items for transport tasks)
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 · 60 lines · 64 tokens per session scan A 321cbae0b177
scienceworld-target-identifier is a skill published in the GitHub repository zjunlp/SkillNet (1,253 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 704 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…
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…
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…
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.