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 alfworld-receptacle-preparergit 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/alfworld-receptacle-preparer)<a href="https://agentmods.dev/skills/zjunlp/skillnet/alfworld-receptacle-preparer"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/alfworld-receptacle-preparer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zjunlp/skillnet/alfworld-receptacle-preparer"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/alfworld-receptacle-preparer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00093 | $0.00595 |
| Opus 5 | $0.00046 | $0.00298 |
| Sonnet 5 | $0.00019 | $0.00119 |
| Haiku 4.5 | $0.00009 | $0.00060 |
Grade A, and why
alfworld-receptacle-preparer 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 10d 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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
Trigger: This skill is invoked when the agent's goal requires placing an object into a target receptacle (e.g., "put X in/on Y").
Core Procedure
- Navigate: Use
go to {target_receptacle}to move to the target receptacle's location. - Observe: Upon arrival, carefully read the environment's observation. It will describe the receptacle's state and any contents.
- Analyze & Prepare: Based on the observation, determine if the receptacle is ready.
- Ready State: The receptacle is described as present and accessible (e.g., "On the garbagecan 1, you see a cd 1."). No further action is needed. Proceed to place the object.
- Blocked/Closed State: If the receptacle is closed, latched, or obstructed, use the appropriate action (
open {recep},toggle {obj} {recep}) to prepare it. - Invalid Target: If the observation indicates the receptacle does not exist or cannot be interacted with ("Nothing happened"), you must abort this skill and re-plan your task strategy.
- Confirm: After any preparatory action, observe the environment's feedback to confirm the receptacle is now ready.
Key Principles
- Efficiency: This skill is a preparatory step. Do not spend turns searching for the object to be placed here. That is a separate "search" skill.
- Context: The observation text is your only source of truth about the receptacle's state. Interpret it literally.
- Idempotency: If the receptacle is already ready, this skill completes immediately with no action required beyond observation.
Example from Trajectory
Goal Context: "find two pen and put them in garbagecan." Skill Execution:
- Thought: "I'll check the garbage can first to ensure it's open and ready to receive items."
- Action:
go to garbagecan 1 - Observation: "On the garbagecan 1, you see a cd 1."
- Analysis: The garbage can is present and accessible (a CD inside does not block new items). It is ready.
- Outcome: Skill completes. The agent proceeds to
put pen 3 in/on garbagecan 1.
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.
- 10d ago First seen · 31 lines · 93 tokens per session scan A 04afc73e8e81
alfworld-receptacle-preparer is a skill published in the GitHub repository zjunlp/SkillNet (1,256 stars, last pushed yesterday), licensed MIT. It adds 93 tokens to every session and 595 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 2026-08-30.
Other skills, from other repositories
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
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…
applicable-fee-ids
Solve questions about which fee IDs apply to a payment merchant, transaction characteristics, or time period in the dabstep dataset. Use this skill for any question asking "which fee IDs apply to X", "what are the applicable fee IDs for merchant Y", "which merchants are affected by fee Z", or any query involving…
Fee_Delta_and_Impact_Simulation
Solve dabstep FeeDeltaandImpactSimulation questions: computing fee deltas when a fee's rate changes, and identifying which merchants are affected by fee rule changes. Use when asked about fee impact, delta payments, rate changes, or which merchants would be affected by modifying a fee rule.
Total_Fees_Calculation
Skill for computing total payment processing fees for a merchant over a specific day, date range, or month in the dabstep dataset. Use this skill whenever the question asks for "total fees", "fees paid", or "fees charged" for a merchant over some time period. The computation requires matching each transaction to a fee…
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…