本文へ移動
cccskills
無料GitHub で公開

mcore-split-pr

Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.

インストール方法を見る

含まれるファイル(5)

  • SKILL.md4.4 KB
  • BENCHMARK.md2.7 KB
  • evals/evals.json3 B
  • skill-card.md2.4 KB
  • skill.oms.sig4.5 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Split PR by CODEOWNERS Groups

Split a large pull request into multiple smaller PRs, where each PR touches the fewest possible CODEOWNERS reviewer groups. The goal is to reduce review burden: a PR that only touches megatron/core/ needs only the core reviewers, while a PR that also touches examples/, tools/, and megatron/training/ pulls in many additional groups.

Answer-First Constraints

For split-planning questions, lead with these constraints before the full workflow:

  • Minimize CODEOWNERS reviewer groups per PR, but each resulting PR must still be independently mergeable and reviewable.
  • Tests travel with the production code they validate; do not split tests into a separate PR just to reduce reviewer groups.
  • If PR B depends on symbols renamed in PR A, call out the dependency and put backward-compatible aliases, re-exports, or shims in PR A when needed.
  • Wait for user approval before execution.
  • Execution creates draft PRs from the right base, applies file-scoped diffs with git diff upstream/main..<source-branch> -- <paths> | git apply, pushes to the user's fork, and never pushes directly to upstream.

Workflow

1. Analyze the PR

  1. Fetch the PR details: gh pr view <number> --repo NVIDIA/Megatron-LM --json title,body,headRefName,author and gh pr diff <number> --repo NVIDIA/Megatron-LM --stat. Also determine the current GitHub user with gh api user --jq .login.
  2. Parse .github/CODEOWNERS to build a mapping from file path patterns to owner groups.
  3. For each changed file in the PR, determine which CODEOWNERS groups would be required to review it.
  4. Build a summary table grouped by CODEOWNERS group, showing which files pull in which groups.
  5. Count the total number of distinct reviewer groups the PR currently requires.

2. Propose a split that minimizes reviewer groups per PR

The primary optimization goal: minimize the number of CODEOWNERS reviewer groups required for each resulting PR.

Strategy:

  1. Cluster files by their CODEOWNERS groups. Files owned by the same set of groups naturally belong together.
  2. Identify the largest cluster — this becomes the first (and usually largest) PR.
  3. Remaining files form one or more additional PRs, each ideally requiring only one or two reviewer groups.
  4. If a split creates a dependency (e.g., PR B uses symbols renamed in PR A), the dependent PR must be merged after the first. Note this explicitly.
  5. Each PR must be independently mergeable to main — no broken imports, no missing symbols. Backward-compatible aliases and re-export stubs in the first PR can make this possible.

Present the proposed split as a table:

  • PR name/description
  • Files included
  • CODEOWNERS groups required
  • Dependencies on other PRs (if any)

Wait for user approval before proceeding.

3. Execute the split (after user approval)

For each new PR:

  1. Create a new branch from the appropriate base (main, or a dependency PR's branch).
  2. Extract the relevant changes: git diff upstream/main..<source-branch> -- <file paths> | git apply.
  3. Stage, commit with a clear message, and push to the user's fork.
  4. Create the PR as a draft (per repo contributing guidelines).
  5. If the original PR needs to be narrowed in scope, confirm with the user before force-pushing.
  6. Report all PR URLs when done.

Important guidelines

  • Always create PRs as drafts and push to the user's fork, never directly to upstream.
  • Backward-compatible changes (aliases, re-exports, deprecation shims) should go in the first PR so subsequent PRs can depend on them.
  • Test files should go with the production code they test, not in a separate PR.
  • Prefer a single clean commit per split PR over replaying the original commit history.
  • If a file is hard to categorize (e.g., it touches two groups), ask the user which PR it should go in.
  • If the current GitHub user is not the author of the original PR, each new PR's description must explicitly credit the original author (e.g., "Original changes by @<author> in #<number>").

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5582026年10月10日 更新

Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5582026年10月10日 更新

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5582026年10月10日 更新

Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5582026年10月10日 更新

Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5582026年10月10日 更新

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5582026年10月10日 更新

NVIDIA のスキルをすべて見る

このスキルの問題を報告する