Prepare and publish stable Agent Lightning releases through the repository's version bump, pull-request checks, merge, tag, PyPI trusted-publishing, and versioned-documentation workflows. Use when asked to plan, cut, verify, or explain a release; treat nightly TestPyPI builds as a separate path.
Provides the action space, tradeoffs, and evaluation context for improving an editable AI agent against a benchmark while preserving its deployment contract. Use when optimizing agent accuracy, cost, latency, or reliability.
Add a new step under src/nemotron/steps/ / / — manifest (step.toml), runner glue, configs, and per-step README.md. Use when extending the catalog so /nemotron-customize can route to it.
Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment. Use when the user asks facts about the model rather than building a pipeline.
Guide for adding a new benchmark or training environment to NeMo-Gym. Use when the user asks to add, create, or integrate a benchmark, evaluation, training environment, or resources server into NeMo-Gym. Also use when wrapping an existing 3rd-party benchmark library. Covers the full workflow: data preparation…
Manage stacked branches and pull requests with the gh-stack GitHub CLI extension. Use when the user wants to create, push, rebase, sync, navigate, or view stacks of dependent PRs. Triggers on tasks involving stacked diffs, dependent pull requests, branch chains, or incremental code review workflows.
Add or update UniRL model package support. Use when adding diffusion or autoregressive model pipelines, model config dataclasses, Bundle/Pipeline/Stage/Conditions implementations, LoRA targets, FSDP wrapping hints, Sample/Part plumbing, or multimodal text/image/video/audio conditioning.
Create and repair UniRL pull requests. Use when creating a PR, editing a PR body, responding to PR Body or Semantic Pull Request CI failures, running gh pr create, or when the user mentions PR body, pull request template, body check, title check, or pull request formatting.
A development workflow and coding-standard skill for Python, PyTorch, deep-learning code, shell scripts, and configuration files. It requires understanding the project, proposing a plan for approval, writing tests, simplifying code, and reviewing the result.
Use when designing a new benchenv task suite, adding several new tasks to an existing suite, or critiquing a task-set proposal for a mobile-gym App — before any class FooTask(...) is written under benchenv/task/.
Use when adding or modifying offline judge tests for benchenv tasks — specifically entries in OFFLINEJUDGEPOSITIVECASES / OFFLINEJUDGENEGATIVECASES in benchenv/tests/ /testtasks.py, or writing live tests. Triggers after a new task is added, or when tightening judge coverage.
Use when writing or modifying checkgoals() / getanswer() / App check methods in benchenv/task/, or when reviewing a draft task's judge correctness. Triggers include adding a new task, editing a judge method, or diagnosing a judge false-positive/negative.
Port an mjlab task to mjswan: turn a local or remote GitHub repo that registers mjlab tasks into a browser app. Use when the user wants to run, visualize, or deploy an mjlab task (theirs or a third party's) in the browser with mjswan.
Build, inspect, run, and debug OmniSim robotics worlds through the first-party MCP tools. Use for requests involving OmniSim scenes, robots, cameras, contacts, physics checks, or screenshots; do not use for unrelated simulators.