skill-installation
uvwt/agentdock1.2k
uvwt/agentdock1.2kskill-installation
审查、安装、配置、验证、更新和移除 AgentDock Skill 时使用；负责来源校验、安全评估、环境配置、content digest、精确 skill_ref 与当前 managed 内容验收。
DevOps 与云 Skill
「DevOps 与云」分类共 291 个 Skill，按仓库 star 排序。分类自动生成，仅供参考。
simulink-configure-code-interfaces
matlab/simulink-agentic-toolkit1.2k
matlab/simulink-agentic-toolkit1.2ksimulink-configure-code-interfaces
Use the skill to control and verify the code interface configuration of your model — how Simulink® model elements are represented in the generated C or C++ code. This includes — (1) specifying names, types (storage classes), and placement of variables that represent model elements (for example, making a model parameter tunable as a global extern variable); (2) specifying names, types, and placement of functions that represent model algorithms; (3) selecting the deployment type (Component, Subcomponent, or Automatic); (4) selecting the interface configuration type (data or service interface); (5) linking a shared Embedded Coder dictionary to a model; (6) creating Embedded Coder dictionary entries and setting their properties; (7) for service interface configuration — specifying service interface definitions, including sender, receiver, client, and server services. Items 1 and 2 can be set per element or as a category-wide default. Item 1 applies to GRT and ERT models; the rest to ERT models only.
simulink-configure-model-for-code-generationmatlab/simulink-agentic-toolkit1.2k
matlab/simulink-agentic-toolkit1.2ksimulink-configure-model-for-code-generation
Configure Simulink models for Embedded Coder (ERT), Simulink Coder (GRT rapid-prototyping), or AUTOSAR code generation. Use when the user asks to generate embedded C or C++ code, run a full build of a model, produce a code generation report, configure a model for production/ECU deployment or rapid-prototyping code, target ARM or x86 hardware, apply MISRA C/C++ compliance (ERT/AUTOSAR only — Simulink Coder does not ship MISRA profiles), or set up GRT, ERT, AUTOSAR, or shared-library targets. Handles target selection, hardware mapping, model hierarchy propagation, and constraint introspection via the configure_for_codegen function. Do NOT use for GRT shared-library variants (grt_malloc.tlc), DDS, or ROS, or for iterative optimization workflows that measure baseline metrics, apply targeted changes, and re-measure to confirm improvement.
simulink-configure-real-time-modelmatlab/simulink-agentic-toolkit1.2k
matlab/simulink-agentic-toolkit1.2ksimulink-configure-real-time-model
Configure a Simulink model for Simulink Real-Time code generation and deployment. Use when setting up a model for Simulink Real-Time, selecting the correct system target file, fixing TLC/STF mismatches, applying fixed-step solver settings, validating target-platform compatibility, or configuring Simulink Real-Time build options before model build.
simulink-run-real-time-applicationmatlab/simulink-agentic-toolkit1.2k
matlab/simulink-agentic-toolkit1.2ksimulink-run-real-time-application
Build, deploy, run, monitor, tune, and stop Simulink Real-Time applications. Use when the user wants to build a Simulink Real-Time model, load/start/stop an `.mldatx` on a target, set stop time or start options, drive root inports/playback/parameter stimulation, inspect installed or running apps, tune parameters with `getparam`/`setparam`, read ad hoc signal values with `getsignal`, manage parameter sets, control recording, or choose between file logging, live streaming, and file-log import. Covers `slbuild`, `install`, `load`, `start`, `stop`, `setStopTime`, `tg.Stimulation`, `startRecording`/`stopRecording`, `tg.FileLog.import`, `slrealtime.exportRun`, `getparam`/`setparam`, `getsignal`, and installed app management for Speedgoat targets and Simulink Real-Time Linux devices.
coreml
dpearson2699/swift-ios-skills1.2k
dpearson2699/swift-ios-skills1.2kcoreml
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
api-gateway
itsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kapi-gateway
AWS API Gateway for REST and HTTP API management. Use when creating APIs, configuring integrations, setting up authorization, managing stages, implementing rate limiting, or troubleshooting API issues.
bedrockitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kbedrock
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
cloudformationitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kcloudformation
AWS CloudFormation infrastructure as code for stack management. Use when writing templates, deploying stacks, managing drift, troubleshooting deployments, or organizing infrastructure with nested stacks.
cloudwatchitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kcloudwatch
AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.
cognitoitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kcognito
AWS Cognito user authentication and authorization service. Use when setting up user pools, configuring identity pools, implementing OAuth flows, managing user attributes, or integrating with social identity providers.
ecsitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kecs
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.
eksitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2keks
AWS EKS Kubernetes management for clusters, node groups, and workloads. Use when creating clusters, configuring IRSA, managing node groups, deploying applications, or integrating with AWS services.
eventbridgeitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2keventbridge
AWS EventBridge serverless event bus for event-driven architectures. Use when creating rules, configuring event patterns, setting up scheduled events, integrating with SaaS, or building cross-account event routing.
iamitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kiam
AWS Identity and Access Management for users, roles, policies, and permissions. Use when creating IAM policies, configuring cross-account access, setting up service roles, troubleshooting permission errors, or managing access control.
lambdaitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2klambda
AWS Lambda serverless functions for event-driven compute. Use when creating functions, configuring triggers, debugging invocations, optimizing cold starts, setting up event source mappings, or managing layers.
s3itsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2ks3
AWS S3 object storage for bucket management, object operations, and access control. Use when creating buckets, uploading files, configuring lifecycle policies, setting up static websites, managing permissions, or implementing cross-region replication.
secrets-manageritsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2ksecrets-manager
AWS Secrets Manager for secure secret storage and rotation. Use when storing credentials, configuring automatic rotation, managing secret versions, retrieving secrets in applications, or integrating with RDS.
snsitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2ksns
AWS SNS notification service for pub/sub messaging. Use when creating topics, managing subscriptions, configuring message filtering, sending notifications, or setting up mobile push.
sqsitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2ksqs
AWS SQS message queue service for decoupled architectures. Use when creating queues, configuring dead-letter queues, managing visibility timeouts, implementing FIFO ordering, or integrating with Lambda.
step-functionsitsmostafa/aws-agent-skills1.2k
itsmostafa/aws-agent-skills1.2kstep-functions
AWS Step Functions workflow orchestration with state machines. Use when designing workflows, implementing error handling, configuring parallel execution, integrating with AWS services, or debugging executions.
matlab-deploy-ai-modelmatlab/matlab-agentic-toolkit1.1k
matlab/matlab-agentic-toolkit1.1kmatlab-deploy-ai-model
Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.
matlab-deploy-embedded-aimatlab/matlab-agentic-toolkit1.1k
matlab/matlab-agentic-toolkit1.1kmatlab-deploy-embedded-ai
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.
matlab-interpret-machine-learning-modelmatlab/matlab-agentic-toolkit1.1k
matlab/matlab-agentic-toolkit1.1kmatlab-interpret-machine-learning-model
Interpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. Find which predictors, features, or columns matter most; explain why the model made a specific prediction, including diagnosing predictions it got wrong; show how a predictor affects the output; and compare how the model behaves across cohorts or subgroups. Uses model-agnostic techniques and model-native measures, and works on custom models (such as a dlnetwork) through a prediction function handle. Use for model interpretability, explainability, and feature-importance questions on tabular data, not for training, tuning, feature selection, deploying models, or models trained on image, text, signal, or other non-tabular data.