Dev & Engineering ✓ NVIDIA · Official pull-request-reviewcodeownersgitgithub-clibranch-managementcode-review

CODEOWNERS PR Splitter

Split large pull requests by CODEOWNERS groups to reduce review burden while keeping each PR independently mergeable.

FollowSkills review · FSRS-2.0
Not recommended
57/ 100 5-point scale 2.9 / 5
Trust18 / 25 · 3.6/5

The skill requires user approval before execution, creates draft PRs, pushes to the user's fork rather than upstream, and exposes the main git/GitHub operations, reducing unauthorized and irreversible-action risk. It does not explain credential handling, sensitive-code treatment, rollback, or remote-branch cleanup, so full marks are not justified.

Reliability8 / 20 · 2.0/5

The workflow and key commands are broadly self-consistent and cover CODEOWNERS analysis, dependency identification, and split execution. However, it depends on gh, git, upstream/main, and repository configuration, with little guidance for invalid input, command failure, conflicts, recovery, or diagnosable error feedback; static calibration also caps this dimension at 10.

Adaptability10 / 15 · 3.3/5

Trigger phrases, input parameter, audience, and primary scenario are reasonably clear for developers splitting Megatron-LM PRs by CODEOWNERS groups. Boundaries for unsuitable cases, complex dependencies, or non-GitHub environments are underdeveloped, and Chinese-language support and mainland-China network reachability are not addressed.

Convention10 / 15 · 3.3/5

SKILL.md provides front matter, layered workflow guidance, constraints, and operational safeguards; Apache-2.0 licensing, author, version, and benchmark information are present. However, metadata.tags, Instructions, Examples, FAQs, detailed limitations, changelog, and explicit maintenance responsibility are missing, matching the benchmark findings.

Effectiveness7 / 15 · 2.3/5

The skill defines a useful path from PR inspection and CODEOWNERS clustering through draft-PR creation, including test co-location and dependency handling. There is no representative directly usable output or execution reproduction for this skill path, and results still require user review and repository-specific handling; static calibration therefore limits the score.

Verifiability4 / 10 · 2.0/5

A committed static benchmark reports nine Tier 1 checks with ten findings and states that Tier 3 was unavailable. evals.json is empty, and there are no dedicated key-path tests or independent reproduction materials, so only limited evidence is supportable.

Evidence confidence:Low Reviewed Jul 20, 2026 Reviewed revision 55f18499943e
Before you use it
  • Execution reads PR data, CODEOWNERS, and the current GitHub user, and may create branches, commits, pushes, and draft PRs; confirm the repository, bases, file scope, and fork target before execution.
  • There is no defined recovery procedure for gh authentication failure, git apply conflicts, push failure, partially created PRs, or rollback.
  • BENCHMARK.md states that Tier 3 was unavailable and evals.json is empty; it also records missing examples, Instructions, metadata.tags, and an explicit WHEN-to-use description.
  • The skill depends on GitHub CLI, Git, upstream/main, and NVIDIA Megatron-LM repository conventions; operation on other hosting platforms or through mainland-China networks is not established.
See the full review method →

What does this skill do, and when should you use it?

This skill analyzes large NVIDIA Megatron-LM pull requests and proposes a split based on CODEOWNERS reviewer groups. It keeps tests with the production code they validate, identifies dependencies between resulting PRs, and requires user approval before execution. After approval, it creates branches from appropriate bases, applies file-scoped diffs, pushes to the user’s fork, and opens draft PRs.

Fetches PR title, body, source branch, author, diff statistics, and the current GitHub user; parses .github/CODEOWNERS and maps changed files to reviewer groups; clusters files by ownership and counts the PR’s distinct groups; produces a split plan with files, required groups, and dependencies; after approval, uses git diff and git apply to extract file-scoped changes, commits and pushes them to the user’s fork, and creates draft PRs.

  1. A Megatron-LM contributor needs to divide a large PR spanning several ownership areas into smaller reviewable PRs.
  2. A PR author wants to reduce the number of CODEOWNERS reviewer groups involved in each PR.
  3. A maintainer needs tests to remain with the production code they validate instead of becoming a separate test-only PR.
  4. A change includes renamed symbols or cross-PR dependencies that require compatibility aliases, re-exports, or shims.

What are this skill's strengths and limitations?

Pros
  • Optimizes around CODEOWNERS groups rather than file count alone.
  • Explicitly preserves production-test relationships and checks independent mergeability.
  • Requires pushes to the user’s fork and draft PR creation during execution.
Limitations
  • Its scope is specifically NVIDIA/Megatron-LM pull requests and their CODEOWNERS file.
  • Execution requires GitHub CLI, Git, network access, and permission to use the user’s fork.
  • The SKILL.md provides no test suite, platform matrix, or failure-recovery procedure.
  • The user must approve the proposed split before execution.

How do you install this skill?

Install the specific skill with the default skills CLI:

npx skills add nvidia/skills --skill mcore-split-pr --yes

The README does not specify a client-specific local folder; the CLI prompts for the installation destination.

How do you use this skill?

After installation, ask the agent for example: “Split PR https://github.com/NVIDIA/Megatron-LM/pull/123 to reduce CODEOWNERS reviewers” or “Split PR 123 into independently mergeable draft PRs.” The agent should present the constraints and proposed split first, then wait for approval before executing.

FAQ

Will it push directly to NVIDIA/Megatron-LM?
No. The instructions require pushing to the user’s fork and creating draft PRs.
Can tests be placed in their own PR?
No. Tests should travel with the production code they validate.
What happens when split PRs depend on renamed symbols?
The dependency is called out, and the earlier PR may need backward-compatible aliases, re-exports, or shims.
Will installation modify an existing PR automatically?
No. The skill must first propose a split and wait for user approval.

More skills from this repository

All from NVIDIA/skills

Automation & Ops ✓ NVIDIA · Official

Megatron-Core CI Failure to Issue

Investigate failed GitHub Actions jobs and create structured bug issues for NVIDIA Megatron-LM.

Dev & Engineering ✓ NVIDIA · Official

NeMo-RL Auto Research

Turns NeMo-RL research goals into reproducible, iterative experiments.

Dev & Engineering ✓ NVIDIA · Official

NeMo Relay Migration Assistant

Safely migrate NeMo Flow projects to NeMo Relay.

Dev & Engineering ✓ NVIDIA · Official

NeMo Relay Installation Guide

Choose and verify the right NeMo Relay path for CLIs, language packages, and maintained frameworks.

Dev & Engineering ✓ NVIDIA · Official

NeMo Relay Quick Start

Helps first-time NeMo Relay users prove observable execution value through the smallest suitable trial.

Automation & Ops ✓ NVIDIA · Official

TAO Workflow Launch Gate

Preflight TAO jobs across platforms, credentials, data, containers, and monitoring.

Dev & Engineering ✓ NVIDIA · Official

Jetson BSP Source Workspace Initializer

Prepares a tracked Jetson BSP source workspace and cross-compiler for downstream customization and builds.

Dev & Engineering ✓ NVIDIA · Official

NeMo Evaluator Plugin Skill

Run and manage NeMo Platform model evaluations through the Evaluator CLI and Python SDK.

Automation & Ops ✓ NVIDIA · Official

DOCA Telemetry Utils

Discover telemetry counters, translate Data IDs, and verify BlueField support before committing exporter configuration.

Dev & Engineering ✓ NVIDIA · Official

DOCA Verbs Raw RDMA Control

Guides DOCA developers who genuinely need low-level verbs control, porting, and diagnosis.

Dev & Engineering ✓ NVIDIA · Official

Earth2Studio Installation Guide

Helps users install Earth2Studio and configure model extras and environment settings safely.

Dev & Engineering ✓ NVIDIA · Official

Jetson Clock Customizer

Lock or cap Jetson CPU, GPU, and EMC clock behavior before flashing a BSP image.

Dev & Engineering ✓ NVIDIA · Official

DOCA Arg Parser CLI

Guides developers in building and debugging standard CLIs for DOCA applications.

Automation & Ops ✓ NVIDIA · Official

DOCA Upgrade Control

Safely gate DOCA upgrades and rollbacks with explicit confirmation.

Automation & Ops ✓ NVIDIA · Official

Jetson Headless Memory Optimizer

Reclaim memory on GUI-free Jetson devices through safe, reversible system-service changes.

Dev & Engineering ✓ NVIDIA · Official

cuOpt Numerical Optimization API

Guide agents through GPU-accelerated LP, MILP, and QP modeling with cuOpt

Dev & Engineering ✓ NVIDIA · Official

Jetson Fan Curve Customizer

Safely maintain nvfancontrol fan profiles and boot defaults in a Jetson BSP.

Automation & Ops ✓ NVIDIA · Official

Jetson nvpmodel Power Mode Customizer

Safely customize Jetson BSP power modes, frequency caps, and the boot-default profile.

Data & Analysis ✓ NVIDIA · Official

TAO DAFT Dataset Converter

Guides AI agents through tao-daft conversion between supported NVIDIA TAO DAFT dataset formats.

Automation & Ops ✓ NVIDIA · Official

RTVI-CV 2D Detection & Tracking Operations

Deploy and operate the RTVI-CV 2D video detection and tracking microservice.

Related skills