Agent skill · research science · k-dense-ai
pufferlib
Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Use when adapting Gymnasium/PettingZoo environments to published PufferLib 3.0.0 or working with the redesigned native 4.0 source line.
Why this skill is useful
Provides version-aware guidance and safety protocols for using PufferLib in reinforcement learning environments, which the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
PufferLib Use PufferLib with an explicit version profile. Upstream currently has two incompatible surfaces: Profile Status on 2026-07-23 Main use --- --- --- pufferlib==3.0.0 Latest stable PyPI release, published 2025-06-23 Python/Gymnasium/PettingZoo emulation, pufferlib.vector, Torch PuffeRL source 4.0 Upstream default branch; not the latest stable PyPI artifact Native C Ocean environments, native CUDA trainer, optional Torch fallback Do not combine 3.0 imports with 4.0 config/CLI examples. The 4.0 redesign removed the 3.0 emulation, vector, and pytorch modules from the current package tree. Safe defaults 1. Start with bundled synthetic, CPU-only, network-free tools. 2. Do not import an arbitrary environment by dotted path. Bundled tools accept only allowlisted built-ins and slug identifiers. 3. Do not install or execute an unreviewed environment package, native extension, ROM, map, checkpoint, or pickle file. 4. Verify official source, immutable revision, licenses, checksums or attestations, and build hooks. Sandbox native builds and first execution. 5. Cap steps, environments, agents, workers, threads, buffers, memory, disk, render size, and wall time. 6. Keep training and evaluation environments/seeds separate. 7. Default logging to local/none. External logging requires explicit opt-in, disclosure acknowledgment, and separate artifact-upload approval. 8. Never pass W&B or Neptune credentials via CLI, INI, JSON, tags, run names, or logger configuration. Never print them. 9. Never dump all environment variables or recursively search for .env. 10. Hash checkpoint bytes before trusted, sandboxed loading; metadata inspection is not proof of safety. First local checks All bundled CLIs are dependency-free and emit strict JSON: Defaults are synthetic, deterministic, bounded, local, CPU-only, no-network, and dry-run where training would otherwise occur. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/pufferlib