Vectorized multi-agent reinforcement learning simulator. An agent skill from wentorai/research-plugins.
$ npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wentorai/research-plugins vmas-simulator-guide --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/domains/ai-ml/vmas-simulator-guide .claude/skills/vmas-simulator-guide && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "vmas-simulator-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/ai-ml/vmas-simulator-guide into .claude/skills/vmas-simulator-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vmas-simulator-guide", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/wentorai/research-plugins/tree/main/skills/domains/ai-ml/vmas-simulator-guideType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wentorai/research-plugins vmas-simulator-guide --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/domains/ai-ml/vmas-simulator-guide .agents/skills/vmas-simulator-guide && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "vmas-simulator-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/ai-ml/vmas-simulator-guide into .agents/skills/vmas-simulator-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vmas-simulator-guide", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wentorai/research-plugins vmas-simulator-guide --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/domains/ai-ml/vmas-simulator-guide .cursor/skills/vmas-simulator-guide && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "vmas-simulator-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/ai-ml/vmas-simulator-guide into .cursor/skills/vmas-simulator-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vmas-simulator-guide", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/wentorai/research-plugins.git --path skills/domains/ai-ml/vmas-simulator-guide--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wentorai/research-plugins vmas-simulator-guide --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/domains/ai-ml/vmas-simulator-guide .gemini/skills/vmas-simulator-guide && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "vmas-simulator-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/ai-ml/vmas-simulator-guide into .gemini/skills/vmas-simulator-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vmas-simulator-guide", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install wentorai/research-plugins vmas-simulator-guideInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/domains/ai-ml/vmas-simulator-guide .github/skills/vmas-simulator-guide && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "vmas-simulator-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/ai-ml/vmas-simulator-guide into .github/skills/vmas-simulator-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vmas-simulator-guide", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install wentorai/research-plugins vmas-simulator-guide --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/domains/ai-ml/vmas-simulator-guide .opencode/skills/vmas-simulator-guide && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "vmas-simulator-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/ai-ml/vmas-simulator-guide into .opencode/skills/vmas-simulator-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vmas-simulator-guide", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
vmas-simulator-guideVectorized multi-agent reinforcement learning simulator. An agent skill from wentorai/research-plugins.
Vmas Simulator Guide is an agent skill from wentorai/research-plugins. Vectorized multi-agent reinforcement learning simulator
Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Reinforcement learning and Multi-agent orchestration. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
github.comarxiv.orgFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Vmas Simulator Guide loads about 960 tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 149 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 149 words, ~960 tokens.
.claude/skills/vmas-simulator-guide/SKILL.md (or your agent's skills folder).VMAS is a vectorized simulator for multi-agent reinforcement learning (MARL) that runs thousands of parallel environments on GPU via PyTorch. It provides a diverse set of 2D cooperative, competitive, and mixed scenarios for benchmarking multi-agent algorithms. Orders of magnitude faster than CPU-based simulators, enabling rapid research iteration on multi-agent coordination problems.
pip install vmasimport vmas
# Create vectorized environment
env = vmas.make_env(
scenario="simple_spread",
num_envs=1024, # Parallel environments
num_agents=3,
device="cuda", # GPU acceleration
continuous_actions=True,
)
# Environment loop
obs = env.reset()
for step in range(100):
# Random actions for demonstration
actions = [env.action_space[i].sample()
for i in range(env.n_agents)]
obs, rewards, dones, infos = env.step(actions)
# obs: list of [num_envs, obs_dim] tensors
# rewards: list of [num_envs] tensors| Scenario | Type | Agents | Description |
|---|---|---|---|
| simple_spread | Cooperative | 3 | Cover N landmarks |
| simple_tag | Competitive | 4 | Predator-prey |
| transport | Cooperative | 4 | Move package to goal |
| wheel | Cooperative | 4 | Coordination on wheel |
| flocking | Cooperative | 5+ | Reynolds flocking |
| discovery | Cooperative | 3 | Explore and discover |
| navigation | Mixed | N | Multi-agent navigation |
# With TorchRL
from torchrl.envs import VmasEnv
env = VmasEnv(
scenario="simple_spread",
num_envs=512,
device="cuda",
)
# With RLlib
from ray.rllib.env import MultiAgentEnv
# VMAS provides RLlib-compatible wrapper
# With CleanRL / custom training
import torch
env = vmas.make_env("transport", num_envs=2048, device="cuda")
obs = env.reset()
# All tensors on GPU — train directly without CPU transfer
policy_output = policy_network(obs[0]) # Agent 0 observationsfrom vmas import Scenario, Agent, World, Landmark
class MyScenario(Scenario):
def make_world(self, batch_dim, device):
world = World(batch_dim=batch_dim, device=device)
world.add_agent(Agent(name="agent_0"))
world.add_agent(Agent(name="agent_1"))
world.add_landmark(Landmark(name="goal"))
return world
def reset_world(self, env, world):
# Randomize positions
for agent in world.agents:
agent.set_pos(torch.rand(env.batch_dim, 2) * 2 - 1)
def reward(self, agent, world):
# Distance to goal
goal = world.landmarks[0]
return -torch.linalg.norm(agent.state.pos - goal.state.pos,
dim=-1)
# Register and use
env = vmas.make_env(MyScenario(), num_envs=512)© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/domains/ai-ml/vmas-simulator-guide of wentorai/research-plugins.
Open the folder on GitHubat commit bf44b3c
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.
Vmas Simulator Guide next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Vmas Simulator Guide this skillwentorai/research-plugins | 298 | 1 repos | ~960 | Automated safety check: Pass | MIT | |
| Oracle Agent Team OrchestratorBald0Wang/DeepSeek-Oracle | 187 | — | ~551 | Automated safety check: Pass | None | |
| Langgraph Agent Patternssoba-labs/langchain-agent-skills | 107 | — | ~3.6k | Automated safety check: Pass | MIT | |
| AI Agents Architectdavila7/claude-code-templates | 32k | 3 repos | ~641 | Automated safety check: Pass | MIT | |
| Databricks Agent Bricksdatabricks/databricks-agent-skills | 345 | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| AI Super Intelligencecoco-research/coco | 503 | — | ~3.7k | Automated safety check: Pass | Custom licence |
Bald0Wang/DeepSeek-Oracle
Orchestrate DeepSeek Oracle multi-agent workflows by routing user intents to specialist oracle skills, enforcing safety gates, and composing one unified answer with follow-up questions and action…
soba-labs/langchain-agent-skills
Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications.
davila7/claude-code-templates
Expert in designing and building autonomous AI agents. An agent skill from davila7/claude-code-templates.
databricks/databricks-agent-skills
Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
coco-research/coco
Your AI research and engineering brain trust. An agent skill from coco-research/coco.
magnus919/agent-skills
Build LLM applications with the LlamaIndex framework. An agent skill from magnus919/agent-skills.
wentorai/research-plugins
Craft structured research abstracts that maximize clarity and journal acceptance
wentorai/research-plugins
Manage academic citations across BibTeX, APA, MLA, and Chicago formats
wentorai/research-plugins
Summarize academic papers with structured extraction of key elements
wentorai/research-plugins
Evidence-based study techniques for academic learning and retention
wentorai/research-plugins
Adjust writing tone and register for academic audiences and venues
wentorai/research-plugins
Academic translation, post-editing, and Chinglish correction guide
Categories
Vectorized multi-agent reinforcement learning simulator. An agent skill from wentorai/research-plugins. Vmas Simulator Guide is an agent skill from wentorai/research-plugins.
Vmas Simulator Guide fits situations like: tasks that involve Reinforcement learning; tasks that involve Multi-agent orchestration.
Run `npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a claude-code`. Or copy the skill folder (skills/domains/ai-ml/vmas-simulator-guide in wentorai/research-plugins) into .claude/skills/vmas-simulator-guide in your project. Claude Code loads it when a task matches its description.
Run `npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a codex`. Or copy the skill folder (skills/domains/ai-ml/vmas-simulator-guide in wentorai/research-plugins) into .agents/skills/vmas-simulator-guide in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill vmas-simulator-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vmas-simulator-guide, .gemini/skills/vmas-simulator-guide, .github/skills/vmas-simulator-guide and .opencode/skills/vmas-simulator-guide in your project.
Going by SKILL.md and its folder, Vmas Simulator Guide needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
SKILL.md names 2 domains. As links in the text: github.com and arxiv.org. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Vmas Simulator Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 960 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Vmas Simulator Guide: Oracle Agent Team Orchestrator (Bald0Wang/DeepSeek-Oracle, 187 stars), Langgraph Agent Patterns (soba-labs/langchain-agent-skills, 107 stars), AI Agents Architect (davila7/claude-code-templates, 32k stars) and Databricks Agent Bricks (databricks/databricks-agent-skills, 345 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.
Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
