antspy
無料Use ANTsPy/antspyx for medical image IO, ANTsImage metadata, image operations, registration, segmentation, visualization, and learning helper workflows.
日本語の概要は準備中です。原文の説明を表示しています。
5,150 件 ・ 関連度順
概要と使いどころ
Use ANTsPy/antspyx for medical image IO, ANTsImage metadata, image operations, registration, segmentation, visualization, and learning helper workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Routes AnyDoor zero-shot object-level image customization workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Guides Any4LeRobot dataset conversions, LeRobot-to-RLDS export, and LeRobot v1.6-v3.0 format migrations with schema validation, version-aware environments, and safe publication boundaries.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AnyLabeling desktop annotation, auto-labeling model, dataset export, install, packaging, and release workflows for the PyQt6/ONNX image-labeling package.
日本語の概要は準備中です。原文の説明を表示しています。
Routes Argos Translate workflows for offline machine translation, package installation, CLI usage, runtime settings, and troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Route Apache Airflow repo tasks across Dag authoring, operations, providers, deployment, and contribution workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Routes Agent Starter Pack tasks to the right workflow for creating, maintaining, and deploying generated agent projects.
日本語の概要は準備中です。原文の説明を表示しています。
Guide agents through AgiBot X1 humanoid reinforcement-learning training, checkpoint playback, policy export, and MuJoCo sim2sim workflows with verified configuration contracts and explicit Isaac Gym limits.
日本語の概要は準備中です。原文の説明を表示しています。
Routes AdelaiDet users through legacy-compatible setup, model config selection, training/evaluation, demos, text spotting, dataset preparation, and export/conversion workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection. Covers the local AlphaFold 3 inference package, its JSON dialect, run_alphafold workflow, outputs, troubleshooting, and safe helper scripts.
日本語の概要は準備中です。原文の説明を表示しています。
Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is search-heavy or review-heavy, spans many files, or you want to keep the orchestrator's context small. Not for trivial one-file work.
日本語の概要は準備中です。原文の説明を表示しています。
Inject Honey levers into dispatched subagent prompts.
日本語の概要は準備中です。原文の説明を表示しています。
Autonomous sub-agent hand-off for a fiddly, fully-specified exploit-compile / escalation RUN - the main agent stays on strategy and the board while a cheap sub-agent runs an exact copy-paste checklist behind a false-root/hostname guardrail. Use for "delegate", "offload", "hand this to a sub-agent", "spin a haiku", or the moment a foothold plus a working escalation vector is identified. Main agent dispatches, waits (no parallel duplicate), integrates the result.
日本語の概要は準備中です。原文の説明を表示しています。
On-demand senior red-team lead advisor. Call at a decision point or obstacle to get wiki-grounded direction instead of hammering blindly. Dispatches a fresh RTL subagent that reads the engagement state + evidence + wiki and returns ranked directions with an explicit STOP. Use for "redteamlead", "RTL", "I'm stuck", "where do I go", "what next", "which vector", "should I keep hammering this".
日本語の概要は準備中です。原文の説明を表示しています。
Manage OpenViking collections (OV libraries) from the command line with `ov-cp` — list / create / get / update / usage / get the data-plane API key / delete, plus managing the users and account data spaces of an enterprise-tier library (list / create / register / update / delete), account-scoped users / API keys / usage, and configuring how a library is billed (AgentPlan AFP deduction vs Volcano pay-as-you-go, `--pay-type` / `--seat-id`). Use when the user wants to provision or inspect an OpenViking library, fetch a library's data-plane API key, do the create→get-key cold-start, manage a library's users or accounts / 数据空间, set or switch a library's billing, or otherwise drive the OpenViking control plane (topapi). Authenticates with an Ark AgentPlan ApiKey.
日本語の概要は準備中です。原文の説明を表示しています。
Main app unit testing patterns (BaseUnitTest, BDD naming, mockito-kotlin, AssertJ, StateFlow/LiveData capture, coroutine dispatchers). Use when writing, editing, exploring, debugging, or reviewing unit tests in the store management app. For POS (WooPos*) tests, use the `pos-tests` skill instead.
日本語の概要は準備中です。原文の説明を表示しています。
Build authority by teaching the problem space, not announcing features. Use when the user finds "marketing" distasteful and does none, publishes only product updates, or wants a sustainable content and GitHub-README strategy that developers actually respect.
日本語の概要は準備中です。原文の説明を表示しています。
Sci-fi and punk world art direction for Blender including hard sci-fi, space, cyberpunk, steampunk, dieselpunk, atompunk, solarpunk, biopunk, and clockpunk.
日本語の概要は準備中です。原文の説明を表示しています。
Minimalist and abstract-leaning art direction for Blender with reduced forms, limited palettes, and negative space.
日本語の概要は準備中です。原文の説明を表示しています。
Psychological horror art direction for Blender assets inspired by Silent Hill, Resident Evil, Chilla's Art, Fear to Fathom, and Iron Lung. Use for sparse industrial environments, analog aesthetics, and tension-driven lighting.
日本語の概要は準備中です。原文の説明を表示しています。
Painterly art direction spanning watercolor and oil-painting looks for Blender using soft edges, pigment texture, and illustrative lighting.
日本語の概要は準備中です。原文の説明を表示しています。
Art direction for stealth-game spaces in Blender with shadow volumes, patrol-friendly layouts, and concealment materials.
日本語の概要は準備中です。原文の説明を表示しています。
AAA environment art for modular kits, buildings, terrain, vegetation, architecture, and scene composition. Use for game-optimized environments, storytelling through space, and modular asset pipelines in Blender via MCP.
日本語の概要は準備中です。原文の説明を表示しています。