earth2studio-create-datasourceCreate and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores. May add Python dependencies to pyproject.toml as part of development. Do NOT use for fetching data with existing…earth2studio-create-diagnosticCreate Earth2Studio diagnostic model wrappers for single-step data transformations, including simple derived diagnostics, packaged AutoModel diagnostics, and generative or diffusion diagnostics. Do NOT use for prognostic time-stepping models, data sources, or…earth2studio-create-prognosticCreate Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation. The user may optionally supply a URL or a local path to a reference inference script.earth2studio-data-fetchFetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.earth2studio-deterministic-forecastBuild deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.earth2studio-discoverFind Earth2Studio models, data sources, and examples for a weather/climate use case. Do NOT use for writing inference code, downloading data, or installation.earth2studio-installGuide installing Earth2Studio via uv or pip, selecting model extras, and configuring the environment. Do NOT use for writing inference code, choosing models, or PhysicsNeMo questions.developer-release-rebasePrepare a new minor alpha release of Earth2Studio by rebasing the release candidate branch onto main, bumping the version, updating the changelog, updating the README latest-news highlights, stripping example version tags, and pushing for PR. Use when…
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Vista por repositorio de 1154 skills recopiladas en 102 repositorios de GitHub.
cuopt-installInstall cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.cuopt-developerModify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.cuopt-multi-objective-explorationTrace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).cuopt-numerical-optimization-apiLP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.cuopt-numerical-optimization-formulationLP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.cuopt-routing-api-pythonVehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.cuopt-server-api-pythoncuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.cuopt-skill-evolutionAfter solving a non-trivial problem, detect generalizable learnings and propose skill updates. Always active — applies to every interaction.
nvshmem-select-remote-transportSelect an NVSHMEM remote transport from target system and kernel evidence. Use for inter-node selection, compatibility checks, or configuration.nvshmem-tune-performanceRoute NVSHMEM tuning to data collection, remote transport, NIC-to-PE mapping, or TMA. Do not use for unrelated CUDA, NCCL, or application tuning.nvshmem-collect-performance-dataCollect and package NVSHMEM put/get bandwidth, latency, and other perftest results with system and topology evidence for performance sanity checks.nvshmem-configure-nic-pe-mappingRecommend NVSHMEM NIC-to-PE mappings and environment exports. Use for HCA selection, multi-NIC configuration, or topology-based mapping diagnostics.nvshmem-troubleshoot-and-report-bugsDiagnose NVSHMEM runtime failures and prepare bug reports for launch, crashes, hangs, correctness, transport, or topology issues.nvshmem-get-startedUse when NVSHMEM beginners want a tutorial-style overview on assessment, mental models, first C/C++ or Python NVSHMEM programs, compilation, launching, and next steps.nvshmem-enable-tmaPrepare or review NVSHMEM CUDA kernels for TMA SMEM registration and direct-SMEM transfers. Do not use for unrelated CUDA tuning.nvshmem-installPlan and validate NVSHMEM and NVSHMEM4Py installations. Use for package, container, or source deployments.
flashdreams-preparationUse when changing FlashDreams V2 downloads, compilation, native loading, model construction, runtime validation, or application preloading.flashdreams-postprocessingAdd or modify FlashDreams video post-processing processors, sessions, presets, and runner stream wiring. Use when implementing a new VideoPostProcessorConfig / VideoPostProcessor / VideoPostProcessorSession, registering a --postprocess.preset entry point,…flashdreams-integrationsNavigate the FlashDreams package layout and integration architecture: core vs infra vs recipes vs workspace integrations, Transformer/Encoder/StreamingDecoder/Pipeline config contracts, AR caches, CP, CFG, KV cache, CUDA graph wrapping, runner registration,…integrate-a-modelEnd-to-end workflow for porting an external video diffusion model into a flashdreams integration — scope the architecture, scaffold a workspace-member plugin, reuse an existing recipe, write the checkpoint key-remap, layer model-specific conditioners, wire…maintaining-oss-stateMaintain FlashDreams's OSS-release state — the LICENSE / NOTICE / THIRD-PARTY-NOTICES / REUSE.toml / LICENSES/ / CONTRIBUTING.md collateral that satisfies OSRB Bug 6107043, the per-file SPDX headers, the third-party dependency manifest in THIRD-PARTY-NOTICES,…apply-inference-optimizationsApply FlashDreams-style inference speedups to model integrations after a baseline exists: bounded windows and fixed K/V caches, cache/decode overlap, `torch.compile`, CUDA graph capture, attention backend checks, decoder layout or replacement,…profile-model-performanceInspect and baseline performance for FlashDreams-style model integrations and interactive demos: map the generation path, add trustworthy timing splits, build focused probes, and identify whether decode, model/denoise, cache, data transfer, or presentation…validate-performance-qualityDesign benchmark, quality, and documentation validation for FlashDreams-style performance changes. Use when adding or updating sweep commands, profiler probes, decoder-quality comparisons, compile/cache probes, manual GPU validation, performance summaries,…
warp-closing-issueUse when the user provides Warp commit SHA(s) and GitHub issue number(s) to assess, draft issue comments, post progress updates, or recommend whether issue threads should stay open or close.warp-closing-issueUse when the user provides Warp commit SHA(s) and GitHub issue number(s) to assess, draft issue comments, post progress updates, or recommend whether issue threads should stay open or close.warp-release-notesUse when drafting GitHub release notes for a Warp feature or bugfix release from Towncrier fragments or a tagged final changelog.warp-changelog-auditUse when auditing and recovering Warp changelog fragments, finalizing a release changelog, or synchronizing a tagged release back to main.warp-release-auditUse when generating a Warp pre-release or release-candidate audit report from Towncrier fragments and release history.warp-compile-time-optimizerUse when compile time or startup time is the problem in code that uses Warp: a request to improve, optimize, or cut compile times; an app that is slow to start or stalls at the first wp.launch; seconds of compiling before real work begins; JIT modules…warp-debug-gradientsUse to diagnose and fix incorrect gradients in differentiable Warp programs. Anything trained, optimized, calibrated, or fit through Warp kernels depends on wp.Tape gradients, so treat any misbehavior of such a workflow as a gradient problem until proven…warp-evalEvaluate whether an existing hot path is a credible NVIDIA Warp candidate. Use for irregular or spatial queries, particle or geometry simulation, branch-heavy loops, many small launches, host fallbacks, or large intermediates. CPU-only code and absent GPU…
cosmos3-codebase-navNavigate the Cosmos3 package codebase to find where parameters, configs, defaults, scripts, and documentation live. Use when the user asks "where is X in cosmos3", "how do I find the config for Y", "where are the defaults", "where do I change a parameter", or…cosmos3-codebase-navNavigate the Cosmos3 package codebase to find where parameters, configs, defaults, scripts, and documentation live. Use when the user asks "where is X in cosmos3", "how do I find the config for Y", "where are the defaults", "where do I change a parameter", or…cosmos3-env-troubleshootDiagnose and fix Cosmos3 environment, installation, and runtime errors. Use when the user encounters an ImportError, ModuleNotFoundError, CUDA error, Docker error, checkpoint download failure, or any traceback during setup or inference.cosmos3-inferenceGuide users through running Cosmos3 inference — offline batch generation, online serving with Ray and Gradio, parallelism options, input formats, sampling parameters, and prompt upsampling. Use when the user asks "how do I run inference", "how do I generate a…cosmos3-post-trainingGuide users through Cosmos3 supervised fine-tuning (SFT) post-training: preparing the example dataset and Wan2.2 VAE, converting the base checkpoint to DCP, launching distributed training (paired launch shell recommended, raw `torchrun` as an alternative),…cosmos3-setupGuide users through Cosmos3 installation, environment setup, checkpoint downloading, and verification. Use when the user asks "how do I install cosmos3", "how do I set up the environment", "how do I download checkpoints", "how do I use Docker", or any…cosmos3-env-troubleshootDiagnose and fix Cosmos3 environment, installation, and runtime errors. Use when the user encounters an ImportError, ModuleNotFoundError, CUDA error, Docker error, checkpoint download failure, or any traceback during setup or inference.cosmos3-post-trainingGuide users through Cosmos3 supervised fine-tuning (SFT) post-training: preparing the example dataset and Wan2.2 VAE, converting the base checkpoint to DCP, launching distributed training (paired launch shell recommended, raw `torchrun` as an alternative),…
cuopt-model-mapperMap interpreted optimization problems into cuOpt-native models for the fast path with minimal clarifying questions.cuopt-sandboxRun cuOpt in the NemoClaw sandbox — probe/smoke gates, prefer cancelable Python gRPC jobs, use legacy remote execution only when that API is unavailable, then vendored cuOpt skills.optimization-mode-routerChoose fast direct-to-cuOpt solve versus replayable or auditable model artifact mode.tabular-optimization-ingestionInfer optimization structure from uploaded tables and identify minimal clarifications before cuOpt modeling.cuopt-debuggingTroubleshoot cuOpt LP/MILP problems including errors, wrong results, infeasible solutions, performance issues, and status codes. Use when the user says something isn't working, gets unexpected results, or needs help diagnosing issues.optimization-from-data-orchestratorCoordinate uploaded data plus a natural-language question into interpretation, clarification, cuOpt solve, and a user-facing answer.optimization-intent-routerClassify whether a data-backed request is LP, MILP, QP, routing, or non-optimization analytics.generic-max-supplyMulti-period supply chain planning model: data files, BOM structure, variable/constraint reference for the max-supply base model.
fix-cveFix CVE vulnerabilities by upgrading affected dependencies across Python (uv), and Rust layers. Use when the user mentions CVE numbers, asks to fix security vulnerabilities, or asks to patch dependencies for CVEs.generate-specProduce a feature specification covering API changes, backward compatibility, cross-language impact, performance, and a file-level change plan. Use when designing a new feature, planning a significant change, or the user asks to spec something out.generate-test-planProduce a test plan with specific test names, assertions, and coverage across Python and Rust. Use when planning tests for a feature, after generating a spec, or the user asks for a test plan.release-automationPrepares and automates the release process for multi-storage-client (version bump, release notes, checklist). Use when the user wants to cut a release, bump version, prepare release, publish a new version, or automate release steps.researchBefore designing a feature, research how comparable libraries solve the problem, check best practices, known pitfalls, and upstream changes. Use when starting a new feature, investigating an unfamiliar area, or the user asks to research something.capture-learningAppend a learning to agent-learnings.md when the agent or user identifies a mistake, non-obvious pattern, or useful convention worth remembering. Use when the user says "capture this", "remember this", or "add to learnings".cloud-storage-setupSet up MSC credentials and MCP server integration for cloud storage access. Use when the user wants to interact with cloud storage (S3, GCS, Azure, OCI), asks to set up credentials, connect to a bucket, list objects, move files, or sync data. Checks for…prepare-mrStage files, commit with correct format, push, and generate an MR description. Manual invocation only — has side effects (git operations). Use when the user explicitly asks to prepare, create, or submit an MR.
manage-model-cacheUse when preparing a shared Hugging Face model cache for Slurm or Kubernetes, pinning downloaded model revisions, validating offline model files, or diagnosing missing snapshots and cache permissions.validate-gpu-clusterCheck whether a DeepOps-deployed Slurm or Kubernetes GPU cluster is healthy and report a machine-readable verdict. Use for health checks, post-deploy verification, "is the cluster working?" questions, and after any node or driver change.provision-with-maasProvision or reinstall bare-metal servers and test VMs through Canonical MAAS, map deployed machines into DeepOps Ansible inventory with MAAS tags, validate access, or release them safely. Use when operating DeepOps with a MAAS-owned machine lifecycle.deploy-airgappedPrepare mirrors and transfer artifacts, configure DeepOps, deploy Slurm or Kubernetes GPU clusters without Internet access, and validate them with machine-readable gates. Use for disconnected, restricted-egress, offline, or air-gapped DeepOps installations…deploy-k8s-gpu-clusterDeploy a Kubernetes GPU cluster with DeepOps (Kubespray + GPU Operator) and prove it schedules GPU pods. Use when asked to deploy or rebuild Kubernetes on GPU servers with this repository.deploy-slurm-clusterDeploy a Slurm GPU cluster with DeepOps and prove it works. Use when asked to deploy, install, or rebuild Slurm on one or more GPU servers with this repository.diagnose-driver-installDiagnose NVIDIA driver installation failures on DeepOps-managed nodes — nvidia-smi errors, "No devices were found", DKMS build failures, or GPU pods crash-looping. Use before reinstalling anything.
udf-convert-to-cudaAssists with converting a non-aggregating Apache Spark UDF to a native CUDA RapidsUDF using JNI and libcudf. This is step 2 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-cuda -> udf-benchmark). Use this skill when you have a CPU UDF with…udf-gen-testAssists with generating a unit test for an Apache Spark UDF. This is step 1 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-* -> udf-benchmark). Use this skill when you have a CPU UDF and need to create a unit test for the UDF before…udf-judge-conversionReviews generated UDF tests and GPU/SQL implementations for robustness, anti-cheating, and GPU execution integrity. Use when the user requests a judge/review-agent pass, or when manually reviewing a completed conversion.udf-convert-to-sqlAssists with converting an Apache Spark UDF to a functionally equivalent Spark SQL expression. This is step 2 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-sql -> udf-benchmark). Use this skill when you have a CPU UDF with a unit test…udf-benchmarkAssists with benchmarking and profiling the performance of an Apache Spark UDF on the GPU. This is step 3 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-* -> udf-benchmark). Use this skill when you have a CPU UDF and a RapidsUDF or SQL…udf-convert-to-cudfAssists with converting an Apache Spark UDF to a GPU-accelerated RapidsUDF using cuDF Java APIs. This is step 2 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-cudf -> udf-benchmark). Use this skill when you have a CPU UDF with a unit test…udf-optimize-cudfIteratively optimizes a cuDF RapidsUDF implementation for GPU performance. Use after testing and benchmarking with udf-benchmark. Runs a loop of profiling, optimizing, testing, and benchmarking until performance converges or the iteration budget is exhausted.
compileiq-author-objectiveUse when writing the objective_function= passed to Search(). Covers the two legal signatures (compiler-only str vs mixed list), the baseline-knockout branch, per-eval cache busting, framework-specific --apply-controls injection (raw PTXAS, NVCC, Triton,…compileiq-booster-packUse BEFORE running a full CompileIQ search. Walks through downloading a Booster Pack from NVIDIA/CompileIQ GitHub Releases, applying ACF candidates one at a time to the user's compiler (raw PTXAS, NVCC, Triton, Helion, FlashInfer), and keeping only candidates…compileiq-debugUse when something is wrong: Search() hangs, all evaluations return INVALID_SCORE, scores aren't improving, every config returns the same number, ptxas errors fill the log, CV% is too high, or a winning ACF candidate needs NCU profiling to explain.…compileiq-run-searchUse when composing the Search(...) call and calling .start(). Covers the four worker classes (MultiProcessWorker / IsoMultiProcessWorker / RayWorker / AsyncWorker) and when to pick each, SearchConfiguration sizing rules, dump_results checkpointing,…compileiq-bootstrapUse when starting a fresh CompileIQ project, hitting a socket timeout, or before running any other compileiq-* skill. Verifies CUDA 13.3+, ptxas, GPU access, that `from compileiq.ciq import Search` and friends resolve, and that `PtxasSearchSpace().retrieve()`…compileiq-search-spaceUse when picking the search_space= argument for Search(). Covers the three provider classes (PtxasSearchSpace, NvccSearchSpace, LocalSearchSpaceBin), how to pin a version/variant/tag, the attention-focused 'att' variant for attention kernels (FlashAttention,…compileiq-validate-resultUse AFTER a Search has completed and BEFORE claiming any speedup or shipping an ACF. Loads the dump_results CSV, extracts top-K candidates (single-objective) or the Pareto front (multi-objective), re-measures each against the no-ACF baseline with 100+ trials…
tripy-compilationWork with the nvtripy compilation pipeline. Use when: using tp.compile, creating InputInfo or DimensionInputInfo, understanding the Trace → MLIR → TensorRT flow, configuring optimization levels, working with Executable objects, debugging compilation, using…tripy-constraintsAuthor input/output constraints for nvtripy operations using the declarative constraint DSL. Use when: defining input_requirements or output_guarantees, writing @wrappers.interface decorators, auto-casting dtypes, using GetInput/GetReturn/OneOf/If/Equal,…tripy-debuggingDebug and diagnose errors in nvtripy code. Use when: interpreting TripyException stack traces, enabling MLIR/TensorRT debug output, understanding error reporting with stack_info, using raise_error, configuring debug environment variables, tracing compilation…tripy-documentationWrite API documentation for nvtripy following project conventions. Use when: writing docstrings for ops or modules, adding code examples, using @export.public_api document_under paths, creating Sphinx RST cross-references, understanding the docs build…tripy-new-moduleAdd a new neural network module to nvtripy. Use when: creating an nn layer, implementing a Module subclass, adding a new layer like Linear/LayerNorm/Conv, defining parameters with DefaultParameter or OptionalParameter, using constant_fields decorator.tripy-new-operationAdd a new operation to nvtripy. Use when: implementing a new op, adding a frontend op, creating a trace op, registering an op in the API. Covers the full Frontend → Trace → MLIR pipeline including export decorators, constraint definitions, and init…tripy-testingWrite tests for nvtripy following project conventions. Use when: adding tests for ops, modules, trace operations, or compilation, using pytest parametrize, testing error cases with helper.raises, testing dtype combinations, understanding test directory…