
Find and remove tech debt (redundant/duplicated code), run linters, and ensure code quality in recent changes
Interview user in-depth to create a detailed spec with strict implementation details and tradeoff analysis
# TorchRec Release Cut Instructions ## Step 1 — Switch to a Local Machine These instructions should be run from a **local machine** (e.g., MacBook Pro), not from a devgpu or dev-server, since you'll need direct access to GitHub for pushing branches and triggering workflows. ## Step 2 — Clone / Navigate to the Repo Navigate to the TorchRec repo at `~/local/torchrec`. If the directory doesn't exist, clone it first: ```bash git clone https://github.com/meta-pytorch/torchrec/ ~/local/torchrec c
Investigate and explain TorchRec planner sharding statistics output, especially how HBM storage is computed per table and per rank. Use when the user asks about sharding stats, storage breakdown, or memory estimation.
Generate tests for TorchRec source files with correct patterns (unit, distributed, hypothesis), proper BUCK targets, and test utilities. Use when asked to generate tests, add test coverage, or write tests for a module.
Write docstrings for TorchRec functions and methods following PyTorch conventions. Use when writing or updating docstrings in TorchRec code.
Review TorchRec pull requests and diffs for distributed correctness, sharding safety, backward compatibility, and test coverage. Use when reviewing PRs, diffs, or when asked to review code changes.
Guide users through creating Agent Skills for Claude Code. Use when the user wants to create, write, author, or design a new Skill for TorchRec, or needs help with SKILL.md files.
Review code changes for bugs and alignment with OpenEnv principles and RFCs. Use when reviewing PRs, checking code before commit, or when asked to review changes. Implements two-tier review model.