5,207 件 ・ 関連度順
概要と使いどころ
Walmart review authenticity analyzer. Detect fake reviews, suspicious patterns, and rating manipulation. Includes WFS verified badge analysis, incentivized review detection, and Walmart-specific red flag identification. No API key required.
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。
Amazon review authenticity analyzer. Detect fake reviews, suspicious patterns, and rating manipulation. Includes time clustering detection, content similarity analysis, rating distribution checks, and verified purchase validation. Progressive analysis with L1-L4 depth levels. No API key required.
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。
Amazon brand protection toolkit. Detect hijackers, counterfeits, and unauthorized sellers. Includes MAP violation monitoring, trademark abuse detection, complaint templates for Brand Registry, and test buy evidence collection guides. No API key required.
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。
eBay review and feedback authenticity analyzer. Detect fake reviews, suspicious seller feedback patterns, and buyer manipulation. Includes time clustering detection, content similarity analysis, and eBay-specific red flag identification. No API key required.
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。
Set up competitor price tracking and monitoring workflows. Track price changes, detect promotions, analyze pricing patterns, and get alerts for competitive price movements.
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。
Simulate Bluetooth system-level networks using the Bluetooth Toolbox. Use this skill for any Bluetooth topology: BLE ACL (Asynchronous Connection-Less) data connections, CIS (Connected Isochronous Stream) and LE Audio for stereo speakers or hearing aids, BLE GAP (Generic Access Profile) connection establishment with advertising and scanning, Classic BR/EDR piconets with ACL data and SCO (Synchronous Connection-Oriented) voice links, and BLE+BR/EDR coexistence with adaptive frequency hopping. Covers bluetoothLENode, bluetoothNode, bluetoothLEConnectionConfig, bluetoothLECISConfig, bluetoothLEGAPConfig, bluetoothConnectionConfig, configureConnection, kpi (), updateChannelList, and all Bluetooth statistics.
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。
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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。
Optimize MATLAB design files for GPU Coder to generate faster CUDA code. Iteratively profiles, rewrites, and benchmarks until performance targets are met or diagnostics are resolved. Use when asked to: optimize for GPU Coder, improve GPU codegen performance, profile generated GPU/CUDA code, profile GPU MEX, fix gpuPerformanceAnalyzer diagnostics, speed up GPU MEX, reduce GPU memory transfers, improve kernel parallelism, rewrite MATLAB for CUDA, or run gpuPerformanceAnalyzer.
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。
AWS ECS container orchestration for running Docker containers. Use when deploying containerized applications, configuring task definitions, setting up services, managing clusters, or troubleshooting container issues.
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 Istio and Linkerd service meshes. Configure mTLS, traffic management, and observability. Use when managing microservices communication.
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。
Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.
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。
Secure Docker images and container runtime configurations. Implement non-root users, read-only filesystems, and security contexts. Use when building secure container images or hardening container deployments.
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。
Secure OpenClaw deployments with preflight hardening checks, CI/CD guardrails, container runtime restrictions, and post-deploy verification. Use when shipping OpenClaw with Docker, Kubernetes, or automated release pipelines.
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 containers using Podman, the daemonless container engine. Run rootless containers, create pods, manage images, and use Docker-compatible commands. Use when working with Podman or requiring rootless container operations.
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 eBPF for deep kernel-level observability — trace syscalls, network flows, and application behavior without code changes using Cilium, Tetragon, and bpftrace.
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 container registries including ECR, ACR, GCR, and Docker Hub. Push and pull images, configure authentication, set up repository policies, and implement image lifecycle management. Use when working with container image storage and distribution.
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。
Search library/framework documentation via llms.txt (context7.com). Use for API docs, GitHub repository analysis, technical documentation lookup, latest library features.
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 driving a desktop GUI through a computer-use driver — windows, menus, native apps, OS dialogs. You build a table of safe actions; Jev picks the next one in about 0.4 seconds.
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 unsure which of many installed skills applies to a request, if any, or when asked to make skill loading cheaper or more accurate. Jev ranks the whole catalog and may say no skill is needed.
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 on a mailbox export to sort mail into needs reply, updates, promotional, sales and spam — which messages are addressed to the person at all. For a support queue use jev triage.
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 after any web or API search, before opening results or spending another round. Jev picks which results to read, whether the evidence is enough, and which query to run next from ones you wrote.
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 driving a web page in a browser — clicking, typing, navigating, logged-in or JS-rendered pages. Jev picks each step from the elements observed, under a host allowlist and a step budget.
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 a transcript has to be cut to a fixed size and you must choose which turns go. Jev marks each turn keep, summarize or drop. Measured: it does not make a handoff better.
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 on passages a search just returned (memory, vault, session history, wiki, web) before reading them in. Jev ranks them, drops the irrelevant, and flags prompt injection hidden in the text.
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。