AGENT SKILLS · ミッション装備
実際に使われている Agent Skills
公開リポジトリの検証済み Skills を比較し、機能、内容、導入状況、出典を確認してから追加できます。
- 掲載範囲
- 検証済み
- Skills
- 23,007
- リポジトリ
- 3551
- 最終同期
- 2026/11/04
diegosouzapwomni-settings
Read and update global application settings: system prompts, thinking budget, IP filters, payload rules, combo defaults, and require-login configuration.
- 1時間の導入数
- 1
- GitHub Stars
- 7.1万
- 更新日
- 9月28日
diegosouzapwomni-tunnels
Create and manage secure tunnels (ngrok, Cloudflare Tunnel, custom) to expose OmniRoute to the internet or share access with remote agents and CI pipelines.
- 1時間の導入数
- 1
- GitHub Stars
- 7.1万
- 更新日
- 9月28日
diegosouzapwomni-version-manager
Install, start, stop, restart, and update embedded services (9Router, CLIProxyAPI). Monitor service status, retrieve logs, and configure auto-start for local-only service endpoints.
- 1時間の導入数
- 1
- GitHub Stars
- 7.1万
- 更新日
- 9月28日
vercel-labsweb-animation-design
Design and implement web animations that feel natural and purposeful. Use this skill proactively whenever the user asks questions about animations, motion, easing, timing, duration, springs, transitions, or animation performance. This includes questions about how to animate specific UI elements, which easing to use, animation best practices, or accessibility considerations for motion. Triggers on: easing, ease-out, ease-in, ease-in-out, cubic-bezier, bounce, spring physics, keyframes, transform,
- 1時間の導入数
- 1
- GitHub Stars
- 5829
- 更新日
- 8月29日
alirezarezvanisenior-data-engineer
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
- 1時間の導入数
- 1
- GitHub Stars
- 2.7万
- 更新日
- 8月30日
everyincce-debug
Diagnosis loop for bugs and failing behavior. Use when asked to debug or fix failing or slow behavior.
- 1時間の導入数
- 1
- GitHub Stars
- 2.5万
- 更新日
- 9月28日
everyincce-retune
Retune a skill corpus for a new model, measurement-first: mine the run archive for a baseline, establish a noise floor, audit the corpus adversarially, then cut in measured passes until a pre-registered bar clears. Requires a benchmark harness that can A/B two builds of the corpus; refuses without one.
- 1時間の導入数
- 1
- GitHub Stars
- 2.5万
- 更新日
- 9月28日
nvidiafoundationpose-pipeline
Adapt BOP datasets, run the FoundationPose perception pipeline with TAO depth, and evaluate or re-score pose results. Use for dataset runs and result comparisons; environment installation belongs to foundationpose-setup.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-lerobot-viz
Serve and visually inspect a converted LeRobot dataset in the browser. Use for videos and state/action timelines; do not use for raw workflow HDF5 or incomplete conversion output.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-workflow
Orient users to the i4h workflow runtime and route them to the correct stage skill. Use for architecture, support, or where-to-start questions; do not execute a known stage.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-workflow-dataset-annotate
Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model. Use for visual success labels; do not use for replay, policy evaluation, or recordings without frames.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-workflow-dataset-replay
Replay a workflow HDF5 episode through its original Scene. Use for visual trajectory and recording verification; do not use for policy evaluation or LeRobot data.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-workflow-dataset-teleop
Record demonstrations through a workflow's teleop Task into workflow HDF5. Use for keyboard, leader, VR, or bus input; do not use for policy evaluation or autonomous rule-based Tasks.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-workflow-scene-edit
Edit an existing workflow Scene or task contract. Use for assets, layout, cameras, randomization, task text, or success rules; do not use to create a new workflow.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-workflow-train-rl
Use when training, evaluating, or exporting Workflow policies with online RSL-RL or RLinf, including RL checkpoint and Workflow handoff.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiai4h-workflow-validate
Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaisaac-mission-control-showcase
Run and validate an end-to-end Mission Control showcase with a locally installed Isaac Sim launched in its GUI window, driven through the isaac-sim-remote Python server, with Nova Carter SIL. Use for demos, showcase replays, Mission Control driving a simulated robot, or diagnosing the integrated small-warehouse scenario. Detect existing Isaac Sim installations without modifying them, automatically select a usable runtime without prompting whenever compatibility can be confirmed, and delegate req
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaomniverse-cad-to-simready
Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaomniverse-realtime-viewer
Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaphysical-ai-defect-image-generation
- Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect im
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaphysical-ai-image-attribute-augmentation
Run the PAIDF Orchestration Image Attribute Augmentation DAG on Kubernetes - person-crop clothing augmentation, attribute search, and augmented dataset generation. Select for requests about image attribute augmentation, person attribute search, person re-identification data, clothing augmentation, attribute captions, augmentation payloads, run status, or result retrieval. Runs environment setup first when controller readiness is unknown. Not for video or defect-image generation.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaphysical-ai-neural-reconstruction
Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, carline adaptation, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiawarp-compile-time-optimizer
- Use 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 recompiling on every run or every CI job. Only applies when the code being optimized uses Warp kernels. Not for steady-state kernel runtime, memory, correctness, building Warp itself from source, or nvcc/C++ build times.
- 1時間の導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
tech-leads-clubtlc-discover
Interviews an unshaped idea into a verdict and a design document - decisions, flows, schema, contracts - that anyone can plan from without having been in the room. Use when the user says "research this", "help me understand this problem", "should we build this", "discovery", "explore this problem", or "tlc-discover". Do NOT use to cut a finished design into tasks, or to implement.
- 1時間の導入数
- 1
- GitHub Stars
- 6964
- 更新日
- 9月20日