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.
npx skills add K-Dense-AI/scientific-agent-skills --skill pufferlib --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 siz
- Safe defaults
- First local checks
- Installation and provenance
- Published 3.0.0
- Current 4.0 source
- Environment workflow
- 1. Validate the contract
- 2. Adapt only after review
- 3. Native environments
- Vectorization workflow
- Policy workflow
- Training and evaluation
- Logging
- Checkpoint workflow
python3 scripts/env_template.py --help python3 scripts/env_contract_validator.py python3 scripts/benchmark_vectorization.py --backend serial python3 scripts/train_template.py python3 scripts/validate_plan.py python3 scripts/repro_plan.py uv venv --python 3.11 uv add --exact --no-sync "pufferlib==3.0.0" uv lock uv sync --frozen
What does the pufferlib skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pufferlib --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.