---

name: "pfllib" description: "Routes PFLlib tasks for dataset preparation, federated-learning experiments, and library extension." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PFLlib

Use this skill for PFLlib dataset generation, experiment execution, and code extension tasks.

Start here

1. Create or verify a Python 3.11 environment with the PFLlib runtime stack: PyTorch, torchvision, torchtext, cvxpy, NumPy < 2, and the scientific stack used by the repository.
2. Run scripts/check_install.py --repo-root <checkout> to confirm imports and CUDA availability.
3. Run scripts/scan_registry.py --repo-root <checkout> when you need a quick view of the supported dataset, model, and algorithm surface.
4. Pick the sub-skill that matches the task family below.

Routes

data-preparation

Use this route to generate or validate client-split datasets such as MNIST, AG News, Amazon Review, HAR, and the other built-in scenario generators. Read sub-skills/data-preparation/SKILL.md first.

experiments

Use this route to launch FL runs, tune CLI flags, inspect results, or check privacy/system-condition settings. Read sub-skills/experiments/SKILL.md first.

extension

Use this route when you need to add a new algorithm, dataset, model, or optimizer to the repo. Read sub-skills/extension/SKILL.md first.

Shared helpers

• scripts/check_install.py — run this first when the environment or CUDA stack is in doubt.
• scripts/scan_registry.py — use this to inspect the supported registry before editing or launching.

Read these references

• references/repo-provenance.md when you need to compare the skill against a checkout or decide whether it should be refreshed.
• references/troubleshooting.md for cross-cutting install, import, CUDA, cvxpy, and working-directory problems.
• references/repo-routing-metadata.json for the router placement data used by the live repo-skills router.

Notes

• This repository is script-driven rather than a packaged library; use the bundled check and launch helpers against a local checkout.
• If a dataset tree is missing, route to data-preparation before launching an experiment.
• If the task is to change the codebase, not just run it, route to extension.