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Found 272 Skills
This Skill summarizes common TypeScript issues and their solutions in Lynx development, mainly covering environment configuration, type extending, event handling, components, and ReactLynx advanced usages. Trigger Scenarios: - User inputs TypeScript error messages related to Lynx and seeks fix suggestions - LSP diagnoses Lynx-related TypeScript errors, proactively invoke query to get fix solutions - User asks about TypeScript best practices or common errors related to Lynx, proactively invoke query to provide guidance - User requests to configure the TypeScript environment of the current project to support Lynx development, proactively invoke query to provide configuration steps
Run Nx generators with prioritization for workspace-plugin generators. Use this when generating code, scaffolding new features, or automating repetitive tasks in the monorepo.
Automate Acculynx tasks via Rube MCP (Composio). Always search tools first for current schemas.
nginx C module debugging guidelines based on the official nginx development guide. This skill should be used when debugging nginx C module crashes, memory bugs, request flow issues, or production problems. Triggers on tasks involving segfault analysis, coredump debugging, GDB inspection, memory leak detection, request phase tracing, AddressSanitizer setup, or nginx module troubleshooting.
Onnx Converter - Auto-activating skill for ML Deployment. Triggers on: onnx converter, onnx converter Part of the ML Deployment skill category.
Nginx Ingress Manager - Auto-activating skill for DevOps Advanced. Triggers on: nginx ingress manager, nginx ingress manager Part of the DevOps Advanced skill category.
Specializes in analyzing Lynx trace data to diagnose performance issues and provide actionable optimization strategies. Key Scenarios: - Loading Performance: Diagnosing slow startup metrics (FCP, FMP, TTI) and white screen issues. - Smoothness Analysis: Investigating root causes for scroll jank, frame drops, and interaction lag. - Regression Detection: Comparing traces to identify performance degradation or verify optimization gains between versions. - Pipeline Deep Dive: Pinpointing bottlenecks in specific rendering stages like Layout, Paint, JS execution, and background threads. - Native Module Analysis: Investigating performance issues related to native module calls.
nginx C module directive design guidelines for creating admin-friendly configuration interfaces. This skill should be used when designing nginx module directives — deciding what to expose vs hardcode, naming conventions, scope placement, default values, variable design, and validation patterns. Triggers on tasks involving ngx_command_t design, directive naming, configuration API design, nginx module public interface, or directive deprecation.
NGINX integration. Manage data, records, and automate workflows. Use when the user wants to interact with NGINX data.
Remap the function_id:pc_index to the original source code position by provided debug info json file.
Implements high-performance local machine learning inference in the browser using ONNX Runtime Web. Use this skill when the user needs privacy-first, low-latency, or offline AI capabilities (e.g., image classification, object detection, or NLP) without server-side processing.
Generate, derive, optimize, and diagnose structured AI image prompts for luminous cinematic Eastern xianxia environments under one inherited master canon with measurable divine scale, fixed five-layer space, 40–60% content-bearing breathing air, monumental Eastern architecture, railing-free edges, and coherent immortals and costume. Cover one-location scenic landmarks, immense inhabited celestial realms, cloud-borne palace cities, high-key Eastern sky-megastructure imagery, and the explicit `仙界大境原典` creative route. Use for Chinese fantasy scenery, places where immortals live, heavenly capitals, colossal celestial gates, architectural fragments above cloud seas, measurable divine scale, unusual framing, color and lighting direction, style-preserving variants, prompt rewrites, diagnosis, or direct image generation. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 16:9 for one prompt and never infer a nine-image series from a vertical ratio.