Official agent skill

Mcore Testing

Test system for Megatron-LM. An agent skill from NVIDIA/skills.

OfficialApache-2.0Auto-check passedTesting & QA

Install Mcore Testing

skills CLI

$ npx skills add NVIDIA/skills --skill mcore-testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills mcore-testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mcore-testing .claude/skills/mcore-testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "mcore-testing" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/mcore-testing into .claude/skills/mcore-testing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-testing", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/mcore-testing

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill mcore-testing -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills mcore-testing --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mcore-testing .agents/skills/mcore-testing && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "mcore-testing" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/mcore-testing into .agents/skills/mcore-testing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-testing", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill mcore-testing -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills mcore-testing --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mcore-testing .cursor/skills/mcore-testing && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "mcore-testing" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/mcore-testing into .cursor/skills/mcore-testing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-testing", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/mcore-testing

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill mcore-testing -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills mcore-testing --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mcore-testing .gemini/skills/mcore-testing && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "mcore-testing" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/mcore-testing into .gemini/skills/mcore-testing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-testing", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills mcore-testing

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill mcore-testing -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mcore-testing .github/skills/mcore-testing && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "mcore-testing" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/mcore-testing into .github/skills/mcore-testing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-testing", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill mcore-testing -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills mcore-testing --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mcore-testing .opencode/skills/mcore-testing && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "mcore-testing" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/mcore-testing into .opencode/skills/mcore-testing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-testing", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: mcore-testing
GitHub stars: 3.5k
Token cost: ~1.8k tokens
SKILL.md length: 540 words
Files: 5
Skills in repo: 386
Repo updated: Oct 9, 2026
First seen: Oct 9, 2026
Licence: Apache-2.0

At a glance

Test system for Megatron-LM. An agent skill from NVIDIA/skills.

Works in 5 steps: Create tests/unit_tests//test_.py. → Use fixtures from… → Apply markers as needed → …
Tasks that involve Unit testing
SKILL.md covers Answer-First Testing Facts, Test Layout, How Tests Execute and Recipe YAML Structure, plus 4 more sections
Calls uv and python

What it does

Mcore Testing is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).

It sits in Testing & QA, covering Unit testing. It works with NVIDIA AI Platform, pytest and GitHub. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

Tasks that involve Unit testing

Example prompts

“/mcore-testing”

Requirements

Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

Create tests/unit_tests//test_.py.
Use fixtures from tests/unit_tests/conftest.py.
Apply markers as needed
Verify locally (see Running Unit Tests Locally above).
If the test needs a dedicated CI bucket, add an entry to

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- uv
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mcore Testing loads about 1.8k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 540 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~44
When it runs · the whole SKILL.md, loaded when a task matches: ~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 540 words, ~1,837 tokens.

Download SKILL.mdSave it as .claude/skills/mcore-testing/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.

name: mcore-testing
description: Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.
license: Apache-2.0
when_to_use: Adding or running a unit or functional test; understanding the test layout; writing a recipe YAML; downloading or updating golden values; reproducing a test…
metadata.author: Philip Petrakian <ppetrakian@nvidia.com>

Testing Guide

Answer-First Testing Facts

For questions about disabling tests without deleting them:

Functional recipe entries stay in YAML; disable by suffixing scope with -broken, for example scope: [mr-github] -> scope: [mr-github-broken].
Unit-test skips use pytest markers instead: @pytest.mark.flaky_in_dev skips in the default dev environment, and @pytest.mark.flaky skips in LTS.
Do not delete the test case or recipe entry when the goal is discoverability and easy re-enable.

Test Layout

text

tests/
├── unit_tests/          # pytest, 1 node × 8 GPUs, torch.distributed runner
├── functional_tests/    # end-to-end shell + training scripts
│   └── test_cases/
│       └── {model}/{test_case}/
│           ├── model_config.yaml          # training args
│           └── golden_values_{env}_{platform}.json
└── test_utils/
    ├── recipes/
    │   ├── h100/        # YAML recipes for H100 jobs
    │   └── gb200/       # YAML recipes for GB200 jobs
    └── python_scripts/  # helpers (recipe_parser, golden-value download, …)

How Tests Execute

The GitHub Actions runner invokes launch_nemo_run_workload.py, which uses nemo-run to launch a DockerExecutor container. The repo is bind-mounted at /opt/megatron-lm; training data is mounted at /mnt/artifacts.

Unit tests are dispatched through torch.distributed.run:

Ranks 0 and 3 are tee-d to stdout; all other ranks write only to log files.
Per-rank log files land at {assets_dir}/logs/1/ and are uploaded as a GitHub artifact after the run.

Functional tests are driven by tests/functional_tests/shell_test_utils/run_ci_test.sh. Only rank 0 runs the pytest validation step; training output from all ranks is uploaded as an artifact.

Flaky-failure auto-retry: launch_nemo_run_workload.py retries up to 3 times for known transient patterns (NCCL timeout, ECC error, segfault, HuggingFace connectivity, …) before declaring a genuine failure.

Recipe YAML Structure

Recipes live in tests/test_utils/recipes/ and are parsed by tests/test_utils/python_scripts/recipe_parser.py. Each file expands a cartesian products block into individual workload specs:

yaml

type: basic
format_version: 1
maintainers: [mcore]
loggers: [stdout]
spec:
  name: "{test_case}_{environment}_{platforms}"
  model: gpt              # maps to tests/functional_tests/test_cases/{model}/
  build: mcore-pyt-{environment}
  nodes: 1
  gpus: 8
  n_repeat: 5
  platforms: dgx_h100
  time_limit: 1800
  script_setup: |
    ...
  script: |-
    bash tests/functional_tests/shell_test_utils/run_ci_test.sh ...
products:
  - test_case: [my_test]
    products:
      - environment: [dev, lts]
        scope: [mr-github]
        platforms: [dgx_h100]

Key runtime placeholders: {assets_dir}, {artifacts_dir}, {test_case}, {environment}, {platforms}, {n_repeat}.

Disabling a Test Without Deleting It

To temporarily disable a test case in a recipe YAML, suffix its scope value with -broken — do not delete the entry:

yaml

# before (test runs in CI)
scope: [mr-github]

# after (test is skipped; entry preserved for easy re-enable)
scope: [mr-github-broken]

Running Unit Tests Locally

All unit tests initialize a torch.distributed group, so every invocation requires GPU access and must go through torch.distributed.run:

bash

# Full suite
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests

# Single file
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests/models/test_gpt_model.py

# Single test
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests/models/test_gpt_model.py::TestGPTModel::test_constructor

# Filter by name substring
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests -k optimizer

Marker filters

bash

# Exclude flaky tests during development
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests -m "not flaky and not flaky_in_dev"

# Include experimental tests
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests --experimental

CI parity

Use tests/unit_tests/run_ci_test.sh to reproduce a CI bucket failure exactly. For ad-hoc runs, prefer the direct torch.distributed.run invocations above.

Gotchas

pyproject.toml sets addopts = --durations=15 -s -rA — stdout is not captured (-s), so ranks interleave during multi-rank runs. Override with --capture=fd when debugging a specific rank.
tests/unit_tests/conftest.py looks for test data under /opt/data and attempts a download if missing. Supply it manually or skip data-dependent tests when running outside the canonical container.

Show full SKILL.md (209 more words)Show less

Adding a Unit Test

Create tests/unit_tests/<category>/test_<name>.py.
Use fixtures from tests/unit_tests/conftest.py.
Apply markers as needed:
- @pytest.mark.internal — skipped on legacy tag
- @pytest.mark.flaky_in_dev — skipped in dev environment (CI default; use this to disable a flaky test without blocking the standard pipeline)
- @pytest.mark.flaky — skipped in lts environment
- @pytest.mark.experimental — latest tag only
Verify locally (see Running Unit Tests Locally above).
If the test needs a dedicated CI bucket, add an entry to tests/test_utils/recipes/h100/unit-tests.yaml.

Adding a Functional / Integration Test

Create tests/functional_tests/test_cases/<model>/<test_name>/.
Write model_config.yaml with MODEL_ARGS, ENV_VARS, and TEST_TYPE.
Add a YAML recipe under tests/test_utils/recipes/h100/ (and gb200/ if needed). Required fields: scope, environment, platform, n_repeat, time_limit.
Push the PR, add the label "Run functional tests" to trigger a full run.

After a successful run, download golden values:

bash

python tests/test_utils/python_scripts/download_golden_values.py \
  --source github --pipeline-id <run-id>

Commit the downloaded golden values.

Common Pitfalls

Problem	Cause	Fix
Test passes locally but fails in CI	Different environment or data path	Check `DATA_PATH`, `DATA_CACHE_PATH`, and the `environment` tag (`dev` vs `lts`)
Golden value mismatch after a code change	Numerical regression	Download new golden values via `download_golden_values.py` after a clean run
`cicd-integration-tests-gb200` not triggered	GB200 jobs require maintainer status	Ask a maintainer to trigger, or add the `Run functional tests` label

Files

SKILL.md and 4 other files in skills/mcore-testing of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/evals.json
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit dfdd080

Compare with similar skills

Mcore Testing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mcore Testing compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Mcore Testing this skillNVIDIA/skills	3.5k	—	~1.8k	Automated safety check: Pass	Apache-2.0	today
Issue WriterNVIDIA/container-canary	308	—	~1.1k	Automated safety check: Pass	Apache-2.0	1 mo ago
Dependency Pinscognitivegears/ha-escpos-thermal-printer	112	—	~1.1k	Automated safety check: Pass	MIT	3 days ago
Llava Onevision2 ConsistencyEvolvingLMMs-Lab/LLaVA-OneVision-2	1.2k	—	~4.1k	Automated safety check: Pass	Apache-2.0	today
Fixing Flaky TestsPostHog/posthog	40k	—	~5.9k	Automated safety check: Pass	Custom licence	today
Adk Verify Snippetsgoogle/adk-python	22k	—	~1.4k	Automated safety check: Pass	Apache-2.0	today

Similar skills

Issue Writer
NVIDIA/container-canary
Official
Draft and revise concise, human-focused GitHub issues for pytest-kind-ng.
308 GitHub stars~1.1k tokensUpdated 1 mo ago
Testing & QAAuto-check passed
Dependency Pins
cognitivegears/ha-escpos-thermal-printer
A skill your agent uses when bumping any dependency, upgrading the HA test harness (pytest-homeassistant-custom-component) or HA floor, or handling Dependabot PRs/security alerts — per-package pin…
112 GitHub stars~1.1k tokensUpdated 3 days ago
Testing & QAAuto-check passed
Llava Onevision2 Consistency
EvolvingLMMs-Lab/LLaVA-OneVision-2
Bilingual guide for running and interpreting LLaVA-OneVision2 HF vs Megatron consistency checks across TP and PP settings
1.2k GitHub stars~4.1k tokensUpdated today
Testing & QAAuto-check passed
Fixing Flaky Tests
PostHog/posthog
Official
Guides an agent through reproducing, root-causing, fixing, and validating flaky tests in the PostHog monorepo.
40k GitHub stars~5.9k tokensUpdated today
Testing & QAAuto-check passed
Adk Verify Snippets
google/adk-python
Official
Checks that every Python code block in a Markdown file actually compiles and runs, by extracting each block to a temporary file, executing it in an isolated subprocess, and writing a pass/fail…
22k GitHub stars~1.4k tokensUpdated today
Testing & QAAuto-check passed
Hermetic Python Unit Tests
dimensionalOS/dimos
Rules for writing, fixing and reviewing pytest unit tests that are hermetic: behavior-focused, deterministic, isolated and cheap to run.
4.6k GitHub stars~1.4k tokensUpdated today
Testing & QAAuto-check passed

More from NVIDIA/skills

All 386 skills in this repo

Vss Deploy Detection Tracking 2D
NVIDIA/skills
Official
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
3.5k GitHub starsUsed in 1 repo~4.5k tokens
Auto-check passed
HSB IP Def File Tool
NVIDIA/skills
Official
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
3.5k GitHub stars~2.9k tokensUpdated today
Auto-check passed
Isaac Sim Mission Control Showcase
NVIDIA/skills
Official
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
3.5k GitHub stars~4.8k tokensUpdated today
Auto-check passed
Defect Image Generation with Cosmos AnomalyGen
NVIDIA/skills
Official
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
3.5k GitHub stars~5k tokensUpdated today
Auto-check: notes
Physical AI Video Augmentation on OSMO
NVIDIA/skills
Official
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
3.5k GitHub stars~4.7k tokensUpdated today
Auto-check: notes
TAO Detection KPI Analysis
NVIDIA/skills
Official
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
3.5k GitHub stars~2.7k tokensUpdated today
Auto-check: notes

Works with

Questions about Mcore Testing

What does Mcore Testing do?

Test system for Megatron-LM. An agent skill from NVIDIA/skills. Mcore Testing is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Test system for Megatron-LM.

When should I use Mcore Testing?

Mcore Testing fits situations like: tasks that involve Unit testing.

How do I install Mcore Testing in Claude Code?

Run `npx skills add NVIDIA/skills --skill mcore-testing -a claude-code`. Or copy the skill folder (skills/mcore-testing in NVIDIA/skills) into .claude/skills/mcore-testing in your project. Claude Code loads it when a task matches its description.

How do I install Mcore Testing in Codex?

Run `npx skills add NVIDIA/skills --skill mcore-testing -a codex`. Or copy the skill folder (skills/mcore-testing in NVIDIA/skills) into .agents/skills/mcore-testing in your project. Codex loads it when a task matches its description.

Can I use Mcore Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill mcore-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mcore-testing, .gemini/skills/mcore-testing, .github/skills/mcore-testing and .opencode/skills/mcore-testing in your project.

What does Mcore Testing need to run?

Going by SKILL.md and its folder, Mcore Testing needs the command-line tools its instructions call (uv and python). Our summary lists: Python 3.

Does Mcore Testing access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Mcore Testing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mcore Testing use?

Mcore Testing is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mcore Testing use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mcore Testing?

Skills that share tags, products or a category with Mcore Testing: Issue Writer (NVIDIA/container-canary, 308 stars), Dependency Pins (cognitivegears/ha-escpos-thermal-printer, 112 stars), Llava Onevision2 Consistency (EvolvingLMMs-Lab/LLaVA-OneVision-2, 1.2k stars) and Fixing Flaky Tests (PostHog/posthog, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mcore Testing?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Mcore Testing

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Testing Guide

Answer-First Testing Facts

Test Layout

How Tests Execute

Recipe YAML Structure

Disabling a Test Without Deleting It

Running Unit Tests Locally

Marker filters

CI parity

Gotchas

Adding a Unit Test

Adding a Functional / Integration Test

Common Pitfalls

Files

Compare with similar skills

Similar skills

More from NVIDIA/skills

Related

Questions about Mcore Testing