探索 MCP Server 与 Agent Skill

io.github.alibaba/loongcollector/security-check

Whenever commit or push is mentioned

io.github.alibaba/loongcollector/testing-standards

Testing standards for LoongCollector: unit tests, e2e tests, benchmarks. Reference when writing or reviewing tests.

io.github.doccker/cc-use-exp/refactor-safety

当用户要求重构代码（提取组件、合并重复逻辑、重命名变量、优化结构）时触发。提供重构安全检查清单，防止丢失原始上下文。

io.github.doccker/cc-use-exp/refactor-safety

当用户要求重构代码（提取组件、合并重复逻辑、重命名变量、优化结构）时触发。提供重构安全检查清单，防止丢失原始上下文。

io.github.softspark/ai-toolkit/docker-devops

Docker/K8s: Dockerfile, multi-stage, compose, manifests, Helm. Triggers: Docker, Dockerfile, container, Kubernetes, k8s, compose, Helm, pod.

io.github.microsoft/semantic-link-labs/sql_to_dax

Guide for translating SQL aggregation expressions into DAX measures.

io.github.iflytek/skillhub/api-and-namespace-design

API design conventions, namespace coordinate system, RBAC roles, ClawHub compatibility layer, OpenAPI contract sync rules, and CSRF/session handling.

io.github.iflytek/skillhub/backend-module-structure

Rules for the SkillHub backend Maven multi-module clean architecture. Ensures agents place new code in the correct module and respect dependency direction.

io.github.iflytek/skillhub/frontend-conventions

Coding conventions, architecture patterns, and testing rules for the SkillHub React frontend. Ensures agents follow Feature-Sliced Design and use the generated OpenAPI types.

io.github.iflytek/skillhub/testing-and-ci

Testing conventions, CI pipeline rules, and smoke test coverage for SkillHub. Ensures agents write tests correctly and understand the CI gate requirements.

io.github.NVIDIA/SkillSpector/sqp1_clean

Deploy infrastructure using Terraform

io.github.NVIDIA/SkillSpector/sqp2_clean

Deploy a service to the staging environment via shell commands

io.github.InternLM/lmdeploy/docker-build

Build an LMDeploy Docker image and push it to the inner registry.

io.github.github/awesome-copilot/slang-shader-engineer

Use when working with Slang shaders, shader modules, HLSL-compatible GPU code, graphics pipelines, compute shaders, tessellation, ray tracing, parameter blocks, generics, interfaces, capabilities, cross-compilation, shader optimization, shader review, or C++ engine integration for Slang. Trigger on any mention of Slang, .slang files, slangc, SPIR-V from Slang, Slang modules, [shader("compute")], [shader("vertex")], or requests to write/review/refactor shader code with modern language features. Also trigger for Slang-to-HLSL/GLSL/Metal/CUDA cross-compile questions, or when the user says "shader" alongside "generics", "interfaces", "parameter blocks", "autodiff", or "capabilities".

io.github.github/awesome-copilot/refactor-plan

Create a concrete plan before starting a multi-file refactor. Use when the user asks to plan, sequence, scope, or safely execute a refactor across multiple files; always investigate first, output the plan, and wait for confirmation before making code changes.

io.github.pedronauck/skills/kubernetes-specialist

Use when deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.

io.github.redpanda-data/console/react-best-practices

Client-side React performance optimization patterns.

io.github.affaan-m/ECC/mle-workflow

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

io.github.ruvnet/RuView/ruview-cli-api

Use the RuView `wifi-densepose` CLI binary (incl. MAT scan/status/zones/survivors/alerts/export subcommands), the REST API (`wifi-densepose-api`, Axum), and the browser/WASM build (`wifi-densepose-wasm`, `wifi-densepose-wasm-edge`). Use when integrating RuView into another program, scripting it from the shell, exposing it over HTTP, or shipping it to the browser / ESP32-WASM3.

io.github.ruvnet/RuView/ruview-quickstart

Onboarding and first-run for RuView (WiFi-DensePose) — Docker demo with simulated data, repo build, and the fastest path to a live sensing dashboard. Use when someone is new to RuView or wants the shortest path to "it works on my machine".

io.github.ruvnet/RuView/ruview-mmwave

Set up and run RuView mmWave / FMCW radar sensing — ESP32-C6 + Seeed MR60BHA2 (60 GHz, heart rate / breathing rate / presence) and HLK-LD2410 (24 GHz, presence + distance), plus mmWave↔WiFi-CSI sensor fusion (48-byte fused vitals, MR60BHA2/LD2410 auto-detect, v0.5.0+). Use when the deployment includes a millimetre-wave radar alongside or instead of WiFi CSI.

io.github.ruvnet/RuView/ruview-configure

Configure RuView — ESP32 sdkconfig variants, NVS provisioning, WiFi channel / MAC filter overrides (ADR-060), edge intelligence modules (ADR-041), sensing-server flags, multi-node mesh, and Cognitum Seed integration. Use when adjusting how a deployed RuView system behaves without changing code.

io.github.ruvnet/RuView/ruview-advanced-sensing

Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.

io.github.ruvnet/RuView/ruview-verify

Verify a RuView build — full Rust workspace tests, the deterministic Python pipeline proof (SHA-256 Trust Kill Switch), firmware hash manifest, and the ADR-028 witness bundle with one-command self-verification. Use after any significant change, before merging a PR, or to produce an attestation bundle for a recipient.