Meta PyTorch
Official@meta-pytorch
Offers specialized engineering capabilities for tensor-based library maintenance, release documentation, and rigorous code quality assurance.
Agent Skills by Meta PyTorch
Showing 20 vetted skills indexed across 2 GitHub repositories.
meta-pytorchhf-cli
Manages Hugging Face Hub repositories, models, datasets, and Spaces via the hf command-line interface.
meta-pytorchopenenv-cli
Scaffold, validate, build, and push OpenEnv agentic execution environments via the openenv CLI.
meta-pytorchwatch-pr
Monitor GitHub PR CI checks and Greptile reviews, auto-fixing failures until green.
meta-pytorchupdate-docs
Detects stale documentation references after API changes and propagates fixes across the repository.
meta-pytorchrelease
Deploys OpenEnv environments to Hugging Face Spaces and syncs canonical collections.
meta-pytorchhf-space-recovery
Diagnose and recover failing Hugging Face Space deployments for OpenEnv environments.
meta-pytorchsprint
Coordinates parallel GitHub issue resolution using agent teams, worktrees, TDD, and stacked PRs.
meta-pytorchgenerate-openenv-env
Generate OpenEnv reinforcement learning environments from a use-case description.
meta-pytorchfeature-submission-reminder
Generate a Markdown dev-discuss post merging PyTorch release timelines with AI-identified features.
meta-pytorchrelease-blog-features
Diff two release branches to generate a structured PyTorch blog feature draft.
meta-pytorchcategorize-miscategorized
Move miscategorized PR entries to correct release notes worksheets using headers and labels.
meta-pytorchgen-release-notes
Generate and organize PyTorch release notes for a version and area.
meta-pytorchprocess-cherry-picks
Extract cherry-pick entries from tracker issues into release notes worksheets and cherrypicks.md.
meta-pytorchwork-on-issue
Parse GitHub issues into goals, acceptance criteria, and constraints.
meta-pytorchalignment-review
Assess code changes for alignment with OpenEnv principles and RFCs.
meta-pytorchrfc-check
Determine if proposed changes require an RFC by applying criteria and checking conflicts.
meta-pytorchwrite-tests
Generate failing test scaffolds from todos or requirements for TDD workflows.
meta-pytorchpre-submit-pr
Automate pre-submit PR validation with linting, tests, and RFC analysis.
meta-pytorchimplement
Generate minimal code changes to fix failing tests from /write-tests runs.
meta-pytorchsimplify
Refactor Python code after tests pass to improve readability and maintainability.
Frequently Asked Questions About Meta PyTorch
FAQPage SchemaWhat specific tasks can be performed using these capabilities?▼
These capabilities enable the parsing of GitHub issues into acceptance criteria, the generation of failing test scaffolds for development, the validation of pull requests against RFC standards, and the systematic organization of release notes and cherry-pick entries for versioned software distributions.
Which personas benefit most from these technical capabilities?▼
Core maintainers, release managers, and software engineers working on large-scale tensor-based frameworks benefit from these capabilities. They are designed for contributors managing complex repository health, ensuring RFC compliance, and streamlining the documentation process for frequent software releases.
What are the prerequisites for implementing these maintenance functions?▼
Implementation requires an existing repository environment with established GitHub issue tracking, defined RFC documentation, and a structured release branch strategy. Users must have appropriate repository permissions to execute linting, test generation, and release note worksheet updates within their development environment.