Agent Skill 目录与安装教程 — ClawHub、SkillsMP 与开源 Skill

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.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.

io.github.browser-use/browser-use/browser-use

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, or extract information from web pages.

io.github.affaan-m/ECC/ck

Claude Code 的每个项目持久化记忆。在会话启动时自动加载项目上下文，通过 git 活动追踪会话，并写入原生记忆。命令运行确定性的 Node.js 脚本——行为在不同模型版本间保持一致。

io.github.affaan-m/ECC/hexagonal-architecture

设计、实现并重构端口与适配器系统，具有清晰的领域边界、依赖反转以及跨 TypeScript、Java、Kotlin 和 Go 服务的可测试用例编排。

io.github.affaan-m/ECC/agent-sort

通过将技能、命令、规则、钩子和额外内容并行进行仓库感知审查，为特定仓库构建基于证据的 ECC 安装计划，将其分为 DAILY 和 LIBRARY 两类。当 ECC 应精简为项目实际所需而非加载完整包时使用。

io.github.affaan-m/ECC/agent-introspection-debugging

针对AI代理故障的结构化自调试工作流程，包括捕获、诊断、受限恢复和内省报告。

io.github.affaan-m/ECC/api-connector-builder

通过匹配目标仓库现有的集成模式，构建一个新的API连接器或提供者。适用于在不发明第二种架构的情况下添加一个集成。