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Found 237 Skills
Comprehensive tmux skill covering process management, session/window orchestration, and ricing (visual customization). Use when managing tmux sessions, running dev servers, setting up floating panes, configuring status bars, installing plugins via TPM, or when the user asks about tmux, tmux-sessionx, tmux-floax, catppuccin tmux theme, or making tmux look good.
API gateway patterns and implementations. Kong, AWS API Gateway, NGINX as gateway, rate limiting, request routing, authentication offloading, and request/response transformation. USE WHEN: user mentions "API gateway", "Kong", "AWS API Gateway", "NGINX gateway", "gateway pattern", "request routing", "BFF" DO NOT USE FOR: reverse proxy basics - use infrastructure skills; service mesh - use `service-mesh`; rate limiting in app - use `rate-limiting`
Guidelines for organizing .NET projects, including solution structure, project references, folder conventions, .slnx format, centralized build properties, and central package management. Use when setting up a new .NET solution with modern best practices, configuring centralized build properties across multiple projects, implementing central package version management, or setting up SourceLink for debugging.
Professional DOCX document creation and editing using OpenXML SDK. Useful for branded reports, polished proposals, and template-based authoring.
Configures nginx load balancing with upstream servers, health checks, and failover strategies. Use when setting up load balancing, distributing traffic across multiple servers, or configuring upstream backends.
Self-contained deploy automation — invoke directly, do not decompose. Deploys a Vibes app to exe.dev VM hosting. Uses nginx on persistent VMs with SSH automation. Supports client-side multi-tenancy via subdomain-based Fireproof database isolation.
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
Deployment & Operations Expert responsible for securely, rollbackable, and observably deploying builds that pass Reviewer and QA gates to servers (PM2 3-process cluster + Nginx reverse proxy + BT Panel). Adheres to engineering baselines including zero-downtime deployment, health checks, rollback within ≤3 minutes, and post-release smoke testing. Handles deployment orchestration, configuration management, traffic management, and monitoring & alerting. Applicable when receiving task cards from the Deploy department or needing to release to production.
Monorepo tooling, task orchestration, and workspace architecture for JavaScript/TypeScript repositories. Use when setting up Turborepo, Nx, pnpm workspaces, or npm workspaces; designing package boundaries; configuring remote caching; optimizing CI for affected packages; managing versioning with Changesets; or untangling circular dependencies. Activate on "monorepo", "turborepo", "nx", "pnpm workspace", "task pipeline", "remote cache", "changesets", "CODEOWNERS", "circular dependency", "affected packages", "workspace". NOT for git submodules or multi-repo federation strategies, non-JavaScript monorepos (Bazel, Pants, Buck), or single-package repository setup.
Use this skill for project schedule management — tracking modules, milestones, and delivery phases stored in YAML. Invoke whenever the user asks about: project progress or delivery status, module status (planned/in_progress/done/deferred), weekly task breakdown, milestone countdowns, risk analysis, linking OpenSpec changes to modules, or syncing schedule data to Yunxiao. Triggers on: "planning", "schedule", "progress", "milestone", "what's this week", "what's left", "mark as done", "排期", "进度", "本周任务", "里程碑", "模块状态", "还剩多少". Do NOT trigger for: calendar reminders, weekly work reports, or Yunxiao tasks without schedule context.
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. Workflow 1 auto-discovers input shapes and parameters from user source code. Workflow 2 exports PyTorch models to ONNX. Workflow 3 converts ONNX to .om via ATC with multi-CANN version support. Workflow 4 adapts the user's full inference pipeline (preprocessing + model + postprocessing) to run end-to-end on NPU. Workflow 5 verifies precision between ONNX and OM outputs. Workflow 6 generates a reproducible README. Supports any standard PyTorch/ONNX model. Use when converting, testing, or deploying models on Ascend AI processors.
Fix errors and warnings in Sphinx docs build