Expert agent for Citrix NetScaler ADC across all form factors and versions. Deep expertise in MPX/VPX/CPX/SDX/BLX platforms, AppExpert policy engine, content switching, GSLB, SSL offload, compression/caching, NetScaler Console (ADM), Kubernetes CPX Ingress, NITRO API, and licensing migration. WHEN: "NetScaler", "Citrix ADC", "NetScaler ADC", "MPX", "VPX", "CPX", "SDX", "AppExpert", "GSLB NetScaler", "NITRO API", "NetScaler Ingress".

chrishuffman50

フレームワーク内部構造

Apache Cassandra 5.0 Expert

Apache Cassandra 5.0 version-specific expert. Deep knowledge of Storage Attached Indexes (SAI), Unified Compaction Strategy (UCS), trie-based indexes, vector search, dynamic data masking, new CQL features, Java 17 requirement, and major architectural improvements. WHEN: "Cassandra 5", "Cassandra 5.0", "SAI", "Storage Attached Index", "UCS", "Unified Compaction", "vector search Cassandra", "trie index Cassandra", "dynamic data masking Cassandra", "Cassandra Java 17".

chrishuffman50

フレームワーク内部構造

Zmk Config

Develop and modify ZMK firmware configuration for the Sofle split keyboard (Nice Nano v2). Covers Kconfig (.conf), Devicetree keymap (.keymap), build matrix (build.yaml), and module management (west.yml).

caophuoclong0

フレームワーク内部構造

Rule Typescript

Middle Earth TypeScript baseline guidelines (compiler, modules, best practices)

carrot-foundation0

フレームワーク内部構造

Typescript Patterns

TypeScript patterns for the @devagents monorepo: typed errors, discriminated unions, type guards, options objects. Load before implementing any TypeScript module.

devlitus0

フレームワーク内部構造

TypeScript Master - Type Safety at Scale

Advanced TypeScript expertise for building type-safe applications at scale. Use when designing complex type systems, creating generic utilities, implementing discriminated unions, fixing type errors, configuring tsconfig.json, working with conditional types/mapped types/template literals, or enforcing strict type safety across large codebases. Triggers on TypeScript types, generics, type inference, tsconfig, type errors, advanced types, or TypeScript best practices.

duylinhdang19980

フレームワーク内部構造

Apache Cassandra Technology Expert

Apache Cassandra technology expert covering ALL versions. Deep expertise in distributed architecture, CQL, data modeling, compaction, repair, cluster operations, and performance tuning. WHEN: "Cassandra", "CQL", "cqlsh", "nodetool", "SSTable", "compaction", "repair", "gossip", "vnodes", "consistency level", "partition key", "clustering key", "tombstone", "hint", "read repair", "anti-entropy", "cassandra.yaml".

chrishuffman50

フレームワーク内部構造

Sme Openclaw Free Apis

Free API integrations for OpenClaw/NemoClaw using GLM-5, Kimi K2.5, MiniMax, NVIDIA API Catalog, and Kilo CLI. Extracted from AI Code King tutorial (bdNf-KieKTY).

BoomerAng90

フレームワーク内部構造

Research Experimenter

Autonomous, fixed-budget optimization of target logic. Use when you need to run iterative experiments (mutations) against a target file to improve its performance or reliability within a strict time limit (The Karpathy Standard).

Clinscott0

フレームワーク内部構造

Blueprint Migration

Versioned migration procedures for upgrading blueprint structure between format versions

managementmaars-art0

フレームワーク内部構造

Bmad Quick Dev

Implement a Quick Tech Spec for small changes or features. Use when the user provides a quick tech spec and says "implement this quick spec" or "proceed with implementation of [quick tech spec]"

Compass-Brand0

フレームワーク内部構造

Llm Breakdown

Have external LLM break the refined design into bite-sized implementation steps. Usage: /llm-breakdown

DanaResearchGroup0

フレームワーク内部構造

Pytorch Model Cli

Guidance for implementing CLI tools that perform inference using PyTorch models in native languages (C/C++/Rust). This skill should be used when tasks involve extracting weights from PyTorch .pth files, implementing neural network forward passes in C/C++, or creating standalone inference tools without Python dependencies.

Zurybr0

フレームワーク内部構造

Pytorch

Expert guidance for deep learning, transformers, diffusion models, and large language model development with basic PyTorch package.

SandroAlex0

フレームワーク内部構造

Portfolio Optimization with C Extensions

Guide for optimizing Python numerical computations with C extensions. This skill should be used when tasks involve creating C extensions for Python, implementing mathematical algorithms (matrix operations, linear algebra) in C, or optimizing computational bottlenecks to achieve significant speedup. Particularly relevant for portfolio risk/return calculations, scientific computing, and performance-critical code requiring validation against baseline implementations.

Zurybr0

フレームワーク内部構造

Write Compressor

This skill provides guidance for implementing custom compression encoders that must be compatible with existing decoders (especially arithmetic coding). It should be used when the task requires writing a compressor/encoder that produces output compatible with a given decompressor/decoder, or when implementing arithmetic coding or similar bit-level compression schemes.

Zurybr0

フレームワーク内部構造

Go Modernize

Use when modernizing Go code to use Go 1.22+ features, updating old patterns, or converting legacy code. Triggers on phrases like "modernize", "update to Go 1.22", "use range N", "apply min/max", or "convert interface{}".

samcharles930

フレームワーク内部構造

Metacircular Evaluator Implementation

Guide for implementing metacircular evaluators—interpreters that can interpret themselves. This skill should be used when building self-interpreting Scheme-like evaluators, debugging multi-level interpretation issues, or implementing language features like environments, closures, and special forms. Focuses on incremental development, continuous metacircular testing, and systematic debugging of nested interpretation failures.

Zurybr0

フレームワーク内部構造

Akita Xp Pair

A age como um Par de Pair Programming Sênior usando metodologias Extreme Programming (XP). Previne over-engineering, exige testes (TDD), e foca em entregas pequenas e refatoração contínua com base na metodologia "The M.Akita Chronicles".

Makadeshbr0

フレームワーク内部構造

Make Doom For Mips

Guidance for cross-compiling complex C programs (like Doom) to run on custom MIPS virtual machines or emulators. This skill should be used when tasked with building software for non-standard MIPS targets that use custom syscall interfaces, require freestanding compilation (-nostdlib), or involve running binaries in JavaScript/custom VM environments.

Zurybr0

フレームワーク内部構造

Menlo Persistence Layer

Implement EF Core + PostgreSQL persistence for the Menlo home management app. Use this skill whenever you are adding new entities, bounded-context slices, entity type configurations, slice context interfaces, interceptors, migrations, or integration tests to the Menlo codebase. Also use it when modifying MenloDbContext, ISoftDeletable implementations, or anything in Menlo.Application that touches the database. If the user is working on data access, a new feature slice, asking how data should be stored, or asking about testing persistence — this skill has the answers and must be followed precisely. Even if the user just says "add an entity" or "make this persistable", invoke this skill.

DigiBanks990

フレームワーク内部構造

Make Mips Interpreter

Guidance for building MIPS interpreters or emulators that execute MIPS binaries. This skill applies when implementing CPU emulation, ELF loaders, instruction decoders, or syscall handlers for MIPS architecture. Use when tasks involve creating virtual machines for MIPS executables, interpreting MIPS assembly instructions, or emulating MIPS system calls.

Zurybr0

フレームワーク内部構造

Skill: high-performance-gpu-operator-development

Use this skill when the user wants to generate or optimize high-performance GPU kernels using languages like Triton or native C++/CUDA. This is applicable for requests like 'write a fast Triton kernel for matrix operations', 'optimize my GPU code for throughput', 'create a custom operator that outperforms the standard library', or 'debug a memory error in a parallel kernel'. It is specifically triggered when performance bottlenecks (latency/bandwidth) or hardware-specific optimizations (register pressure/memory coalescing) are the primary concerns rather than general Python logic.

Dingxingdi0

フレームワーク内部構造

PyTorch Model to CLI Tool Conversion

Guidance for creating standalone CLI tools that perform neural network inference by extracting PyTorch model weights and reimplementing inference in C/C++. This skill applies when tasks involve converting PyTorch models to standalone executables, extracting model weights to portable formats (JSON), implementing neural network forward passes in C/C++, or creating CLI tools that load images and run inference without Python dependencies.

Zurybr0

フレームワーク内部構造

Refactor

Perform structured refactoring operations: extract module, split file, inline abstraction, rename symbol across codebase, simplify complex code, or reduce coupling. Use when the user wants to refactor code, extract a module, split a large file, or reorganize code structure.

driangle0

フレームワーク内部構造

Gtm Icp

Define and refine ICP segments, messaging frameworks, positioning, and objection handling

sahin0

フレームワーク内部構造

Start Config Schema Guardrails

Use when deciding configuration naming conventions, schema rules, or YAML key namespace assignments for outbox/inbox/workflow/kafka/scheduler.

ryan-alexander-zhang0

フレームワーク内部構造

Schemelike Metacircular Eval

Guide for building metacircular evaluators in Scheme-like languages. This skill applies when implementing interpreters that can interpret themselves, handling tasks involving eval/apply loops, environment management, closure implementation, and multi-level interpretation. Use for any metacircular evaluator, Scheme interpreter, or self-interpreting language implementation task.

Zurybr0

フレームワーク内部構造

Flutter Post Figma

figma-implement-design 后处理器。在 figma MCP 生成完 UI 代码后触发,把 MCP 产出的扁平代码补全成项目规范结构(下载 CDN 图到 assets/image/3.0x/{module}/、改 Image.network 为 Image.asset、加 Controller+Binding、登记路由、反推 spec/plan、归档 manifest、生成测试 + review)。和 figma-implement-design 搭配使用,不抢活只补全。

dus2183-commits0

フレームワーク内部構造

Torch Tensor Parallelism

Guidance for implementing tensor parallelism in PyTorch, including ColumnParallelLinear and RowParallelLinear layers. This skill should be used when implementing distributed tensor parallel operations, sharding linear layers across multiple GPUs, or simulating collective operations like all-gather and all-reduce for parallel computation.

Zurybr0

フレームワーク内部構造 Skills | Skills Pool