Write and review NeMo-RL documentation using its project conventions. Keep the documentation index aligned with added or renamed Markdown files, document new features in the appropriate location, and format function and class docstrings in Google style.
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Documentation conventions for NeMo-RL. Covers docs/index.md updates and docstring format. Do NOT use for: bug fixes, test fixes, dependency bumps, refactoring, CI/CD changes, performance tuning, or any task that does not involve writing or updating documentation.
安装前可以查看交给 AI 代理的指令内容。
When a new markdown doc is added under docs/**/*.md or a markdown file is renamed, ensure that @docs/index.md is updated and the document appears in the most appropriate section.
Use Google style docstrings for classes and functions. These are parseable by Sphinx.
For interfaces that may be used outside a file, prefer docstrings over comments. Comments should be reserved for code within a function or interfaces local to a file.
When a new feature is added, update or create documentation in the docs/ directory that most closely matches the feature. Look at existing docs to find the best fit — if none exists, create a new doc and add it to @docs/index.md.
Documentation changes are not required for bug fixes or CI-related changes.
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Move pandas-style table processing to NVIDIA GPUs using cuDF. Choose minimal-change acceleration or explicit GPU code, handle joins and aggregations, compare results with pandas, and use dask-cuDF when data exceeds GPU memory.
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Customize NVIDIA Nemotron Voice Agent’s Generic Pipecat example for appointment scheduling, five-field patient intake, or a custom healthcare conversation. Build prompts and tool handlers without a separate backend, then validate conversation behavior, services, and voice interaction.
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Calibrate live cameras by recording their RTSP video streams through VIOS and processing the clips with AutoMagicCalib. The workflow imports settings and alignment files, checks readiness, tracks calibration progress, and retrieves results.
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Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.
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Calibrate multiple cameras from recorded MP4 files through the AutoMagicCalib REST API. The skill handles project creation, uploads, input verification, progress checks, and evaluation when reference data is supplied, with optional VGGT refinement.
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Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.
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