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-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-auditUse when generating a Warp pre-release or release-candidate audit report from Towncrier fragments and release history.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-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…
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NVIDIA · 저장소와 대표 skills - 3페이지
87개 GitHub 저장소에서 수집된 961개 skills를 저장소 단위로 보여줍니다.
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),…
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,…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,…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,…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…python-docstring-styleWrite Python docstrings and inline comments matching the flashdreams house style — SPDX header, one-line module docstring, Google-style function docstrings (Args/Returns/Raises), PEP 257 attribute docstrings on dataclass/class fields *and on module-level…
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.
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…fix-cveFix CVE vulnerabilities by upgrading affected dependencies across Python (uv), Go, 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, Rust, and Go. Use when planning tests for a feature, after generating a spec, or the user asks for a test plan.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.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.
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-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.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.earth2studio-deterministic-forecastBuild deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.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…developer-bump-versionBump the Earth2Studio version on main to start a new development cycle. This unblocks commits on main after a release branch has been merged. Performs a CHANGELOG update (new blank section) and a hatch version bump. Use when main already has the release merge…
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…
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.
create-custom-graderUse when converting an existing benchmark, rubric, verifier, task YAML/JSON, or domain check into SkillEvaluator BYOG/BYOT custom evaluation.api-callerCall any REST API dynamically. Make GET, POST, PUT, DELETE requests to any endpoint with custom headers and JSON body.calculatorEvaluate mathematical expressions and unit conversions. Handles arithmetic, percentages, exponents, and common unit conversions (temperature, distance, weight). No external dependencies.task-listRequired for 4+ step requests; add tasks at start and update status after each step.text-analyzerAnalyze text content and produce statistics including word count, line count, character count, most frequent words, and readability metrics. Works on any plain text input provided inline or from a file path.simpleSummarize short user notes into clear action items.
analysis-scriptsWrite and run Python scripts to analyze quantum experiment data stored in HDF5 files. Use when the user asks to analyze experiment results, fit peaks or curves, extract features from measurement arrays, or when reusable analysis logic should be saved…experiment-executionReference guide for running individual quantum calibration experiments and interpreting their results. Use when the user asks to run a single experiment (e.g. resonator spectroscopy, qubit spectroscopy, T1, T2), inspect experiment plots with the VLM, or query…vlm-configurationConfigure the Vision Language Model (VLM) used for analyzing experiment plots. Use when the user asks to change the VLM provider or model, adjust temperature or max_tokens, enable or disable thinking mode, or troubleshoot VLM-related behavior in config.yaml.workflow-executionExecute a previously planned calibration workflow node by node, tracking progress and handling failures. Use when the user asks to run, resume, or continue an existing workflow, or to execute a sequence of calibration steps that has already been defined in…workflow-planningPlan a new calibration workflow by discussing experiment sequences, success/failure criteria, and extracted parameters with the user before any files are created. Use when the user asks to plan, design, or create a new calibration sequence, or to build a…writing-experiment-scriptsAuthor new experiment scripts that are compatible with the lab system's auto-discovery. Use when the user asks to create a new experiment type, add a custom measurement, or write a Python script that should be discoverable via the `lab` and `run_experiment`…