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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-stable-baselines3git clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-stable-baselines3)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-stable-baselines3"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-stable-baselines3/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/alterlab-ieu/alterlab-academic-skills/alterlab-stable-baselines3"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-stable-baselines3.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00109 | $0.02855 |
| Opus 5.5 | $0.00044 | $0.01142 |
| Sonnet 5.5 | $0.00022 | $0.00571 |
| Haiku 4.5 | $0.00011 | $0.00285 |
Grade A, and why
alterlab-stable-baselines3 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 1mo 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.
This is a copy
89% identical to stable-baselines3 — 189 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stable Baselines3
Overview
Stable Baselines3 (SB3) is a PyTorch-based library providing reliable implementations of reinforcement learning algorithms. This skill provides comprehensive guidance for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows using SB3's unified API.
When to Use This Skill
Use this skill when the user wants to:
- Train and evaluate a single-agent RL policy (PPO, A2C, SAC, TD3, DDPG, DQN, HER) on a Gymnasium environment.
- Wrap a research simulator as a custom
gymnasium.Envand validate it withcheck_env. - Add evaluation, checkpointing, early stopping, or custom logging through callbacks.
- Speed up training with vectorized environments, or save, load, and report trained agents reproducibly.
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| Millions of steps per second, native multi-agent environments, or PufferEnv/Atari-scale throughput | alterlab-pufferlib |
| Supervised deep-learning training loops (LightningModule, multi-GPU strategies) | alterlab-pytorch-lightning |
| Black-box or multi-objective optimization of parameters without a sequential decision process | alterlab-pymoo |
| Simulating queues or resource contention without learning a policy | alterlab-simpy |
Core Capabilities
1. Training RL Agents
Basic Training Pattern:
import gymnasium as gym
from stable_baselines3 import PPO
# Create environment
env = gym.make("CartPole-v1")
# Initialize agent
model = PPO("MlpPolicy", env, verbose=1)
# Train the agent
model.learn(total_timesteps=10000)
# Save the model
model.save("ppo_cartpole")
# Load the model (without prior instantiation)
model = PPO.load("ppo_cartpole", env=env)
Important Notes:
total_timestepsis a lower bound; actual training may exceed this due to batch collection- Use
model.load()as a static method, not on an existing instance - The replay buffer is NOT saved with the model to save space
What ships with it
8 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.
- 1mo ago Changed · +25 lines e474803deaf6
- 1mo ago First seen · 305 lines · 109 tokens per session scan A e5002df9e1c1
alterlab-stable-baselines3 is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (78 stars, last pushed 4d ago), licensed MIT. It adds 109 tokens to every session and 2,855 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to stable-baselines3, differing in 189 lines, and is treated as a copy.
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