stable-baselines3
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.
- Package
- 8 files, 86 KB
- SKILL.md
- 1586 words, 12+ sections
- Code
- Python (3)
- Allowed tools
ReadWriteEditBash- License
- MIT license
- Compatibility
- Requires Python 3.10+, PyTorch >= 2.8, and stable-baselines3 2.9.0. Gymnasium environments; optional extras for TensorBoard and Atari (ale-py).
SKILL.md covers: Stable Baselines3 · Overview · Installation · Basic installation · With extra dependencies (TensorBoard, ale-py for Atari, etc.) · Related Projects · Core Capabilities · 1. Training RL Agents · Create environment · Initialize agent (device="cpu" is often faster for MlpPolicy on small envs) · Train the agent · Save the model
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 15 KB |
references/algorithms.md | doc | 13 KB |
references/callbacks.md | doc | 6.9 KB |
references/custom_environments.md | doc | 15 KB |
references/vectorized_envs.md | doc | 18 KB |
scripts/custom_env_template.py | script | 10.0 KB |
scripts/evaluate_agent.py | script | 4.2 KB |
scripts/train_rl_agent.py | script | 4.6 KB |
External hosts referenced
stable-baselines3.readthedocs.io github.com arxiv.org sb3-contrib.readthedocs.io docs.python.org doi.org export.arxiv.org gymnasium.farama.org
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-stable-baselines3-scan.json
- Badge for your README:
- Request a re-scan