2,950 件 ・ 関連度順
概要と使いどころ
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
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。
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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。
Maps query single-cell data onto reference atlases and transfers cell-type labels using scArches surgery (scVI/scANVI), Symphony, Azimuth, CellTypist, scPoli, popV, and foundation models, with explicit out-of-distribution and label-transfer uncertainty. Use when annotating new single-cell datasets against a pre-trained reference, deciding which mapping method fits, or judging whether transferred labels are trustworthy. For de novo clustering and manual annotation see single-cell/cell-annotation; for batch integration without a reference see single-cell/batch-integration.
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。
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
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。
Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.
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。
Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.
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 for implementing smooth, native-feeling animations using React's View Transition API (`<ViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter/exit of components, animate list reorder, implement directional (forward/back) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.
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。
Implement sprite layers, overlays, render targets, shader prototypes, and visual cleanup without leaking gameplay authority.
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 authoritative, deterministic, weighted, and testable randomness without breaking prediction or reproducibility.
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。
Change persistent models and SQLite/PostgreSQL migrations without breaking existing servers.
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。
Spawn, initialize, replace, transfer, and delete entities without breaking lifecycle, transforms, containers, or ownership.
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。
Move entities through containers, hands, slots, storage, equipment, and inventories without corrupting ownership or transforms.
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。
Reads and summarises ScenarioRunner output — the criteria pass/fail tables from --output/--file, the machine-readable --json and --junit result files, and the criteria JSON written alongside a --record recording — and runs the metrics module (metrics_manager.py) to compute custom measurements over a recorded run offline, without the simulator. Use when the user asks "did the scenario pass", "why did it fail", "summarise these results", "compare these runs", or wants distance/speed/lane metrics from a recording.
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。
Inspects a recorded CARLA .log without replaying it — show_recorder_file_info for the header/actors/frames, collision queries between actor categories, and blocked-actor queries for vehicles that got stuck. Use when the user asks to "what's in this recording", "show recorder info", "which vehicles collided", "find collisions in the log", or "which actors were blocked/stuck".
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。
Reads a CARLA Leaderboard results.json — per-route driving score, route completion, infraction penalty and the infraction lists — explains how the score was computed for that leaderboard version, and rescores an existing result under the other version's rules (2.0 multiplicative vs 2.1 additive) without re-running anything. Also diffs two runs and merges sharded result files. Use when the user asks "what did I score", "why is my score low", "did my agent pass", "why don't my scores match the leaderboard", or hands over a results.json.
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。
Runs a route through ScenarioRunner's RouteScenario mode (--route / --route-id) with an autonomous agent driving the ego — the same mechanism the CARLA Leaderboard uses, and the only way to reach the ~34 route-only scenario classes (Accident, ParkedObstacle, HazardAtSideLane, InvadingTurn, HardBreakRoute, YieldToEmergencyVehicle, junction and actor-flow scenarios). Inspects route files, lists route ids and their scenarios, and runs one or all of them. Use when the user asks to "run a route", "drive a route with my agent", "run leaderboard scenarios without the leaderboard", or names a route-only scenario type.
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。
Writes a new ScenarioRunner scenario — a Python class deriving from BasicScenario with its behaviour tree assembled from srunner atomic behaviours and criteria, plus the XML config that gives it a town, ego spawn and parameters — and registers it so `--scenario MyScenario_1` works. Also covers adding a scenario to a route via --additionalScenario. Generates a working skeleton and validates it without a simulator. Use when the user asks to "create/write a new scenario", "add a custom scenario", "make my own traffic situation", or wants to extend the scenario library.
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。
Loads, reloads, and reshapes the map on a running CARLA server via the Python API — get_available_maps, load_world and reload_world (with or without resetting world settings), generate a world from an OpenDRIVE .xodr, and load/unload map layers. Use when the user asks to "load a map", "change the town", "reload the world", "list available maps", "load an xodr/opendrive map", or "load/unload map layers", and when settings (sync mode, fixed delta) must survive the switch.
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。
Convert the canonical markdown+JSON deliverable of any SfSkills runtime agent into Excel, PDF, CSV, Notion card, ServiceNow ticket, or similar downstream format WITHOUT polluting the consumer's project with new dependencies or regenerating from the agent's source logic. NOT for authoring new agent output formats (use DELIVERABLE_CONTRACT.md). NOT for data-export SOQL (use bulk-api-2-patterns).
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。
Discover and configure Bluetooth and Wi-Fi accessories using AccessorySetupKit. Use when presenting a privacy-preserving accessory picker, defining discovery descriptors for BLE or Wi-Fi devices, handling accessory session events, migrating from CoreBluetooth permission-based scanning, or setting up accessories without requiring broad Bluetooth permissions.
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。
MUST USE for deploying Aerospike on Kubernetes. Contains CE-specific YAML templates, validated AerospikeCluster CR examples, and critical constraints that prevent enterprise-only config mistakes (feature-key-file, security sections crash CE pods). Without this skill, deployments fail on first attempt due to CE 8.1 breaking changes (data-size not memory-size, no info port 3003) or webhook map/list shape rules (service/network must be maps; logging must be a list). Triggers on: deploy/create/set up Aerospike on K8s, kind, minikube, EKS, GKE; AerospikeCluster CR; ACKO operator; spec.operations / WarmRestart / PodRestart YAML; NoSQL database on Kubernetes. 9 ready-to-use YAML examples from minimal single-node to full-featured multi-rack.
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 when you need framework-agnostic OpenAPI 3.x guidance — spec structure, metadata and versioning, paths and operations, reusable schemas, security schemes, examples, documentation quality, contract validation (e.g. Spectral), breaking-change awareness, and handoffs to codegen — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Review an OpenAPI; Improve an OpenAPI; Improve API contract; Improve API schema design. Part of cursor-rules-java project
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。
Apply the aoa-safe-infra-change workflow inside an abyss-* repository using repo-relative operational surfaces, explicit local authority notes, rollback posture, and local validation commands. Use when the base infra-change workflow is correct but a thin project overlay is needed for one abyss repo. Do not use when the base skill is sufficient without local adaptation or when the task is really about producing a shareable artifact.
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 when you need to apply Java secure coding best practices — including validating untrusted inputs, defending against injection attacks with parameterized queries, minimizing attack surface via least privilege, applying strong cryptographic algorithms, handling exceptions securely without exposing sensitive data, managing secrets at runtime, avoiding unsafe deserialization, and encoding output to prevent XSS. This should trigger for requests such as Review Java code for secure coding. Part of cursor-rules-java project
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。