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Found 1,926 Skills
Build Next.js web applications with Google Gemini Nano Banana image generation APIs (gemini-2.5-flash-image, gemini-3-pro-image-preview). Use when creating image generators, editors, galleries, or any app integrating conversational image generation with server actions, API routes, and storage. Use for "image generation app", "nano banana", "text to image", "AI image generator", or "gemini image". Do NOT use for non-Gemini models, Python/Go backends, model fine-tuning, or image classification/input tasks.
(Public Preview) Perform code upgrades, migrations, codebase analysis, and transformations using AWS Transform custom. Use this skill when a user asks to upgrade, migrate, modernize, analyze, or transform code across a repository. ATX supports any-to-any transformations including language version upgrades (Java, Python, Node.js, Ruby, Go, .NET, etc.), framework upgrades and migrations (Spring Boot, React, Angular, Django, etc.), API and SDK migrations (AWS SDK v1 to v2, boto2 to boto3, JS SDK v2 to v3), library upgrades, code refactoring, architecture migrations (x86 to Graviton/ARM64), language-to-language translations, and custom organization-specific transformations. Executes transformations locally on the user's machine using the ATX CLI. Always use the ATX CLI following the reference files — never attempt to modify code, upgrade dependencies, or run analysis manually.
Build and deploy AI agents with CloudBase Agent SDK (TypeScript & Python). Implements the AG-UI protocol for streaming agent-UI communication. Use when deploying agent servers, using LangGraph/LangChain/CrewAI adapters, building custom adapters, understanding AG-UI protocol events, or building web/mini-program UI clients. Supports both TypeScript (@cloudbase/agent-server) and Python (cloudbase-agent-server via FastAPI).
Alibaba Cloud Tablestore Agent Storage Skill. Use for building and managing Tablestore-based knowledge bases with the `tablestore-agent-storage` Python SDK. Capabilities: - Install and configure the `tablestore-agent-storage` SDK - Create, describe and list knowledge bases (with subspace and custom metadata support) - Upload local files or import OSS documents into a knowledge base - Query document status and list documents - Perform hybrid retrieval (dense vector + full-text) with metadata filtering - Set up local directory sync scripts and scheduled tasks for automatic knowledge base updates Triggers: "知识库", "tablestore", "ots", "表格存储", "agent storage", "knowledge base", "向量检索", "文档上传", "文档导入", "知识库同步", "tablestore-agent-storage", "AgentStorageClient"
GDB debugger skill for C/C++ programs. Use when starting a GDB session, setting breakpoints, stepping through code, inspecting variables, debugging crashes, using reverse debugging (record/replay), remote debugging with gdbserver, or loading core dumps. Activates on queries about GDB commands, segfaults, hangs, watchpoints, conditional breakpoints, pretty-printers, Python GDB scripting, or multi-threaded debugging.
Verify and build the required environment for Triton operator development on the Ascend platform, including configurations of dependencies such as CANN, Python/torch/torch_npu/triton-ascend and PATH environment variables. This is used when users need to configure the Triton operator development environment, check the installation of CANN/torch/triton-ascend, or verify whether the environment is available.
Detects framework-specific anti-patterns, convention violations, and idiom misuse across PHP/Laravel, React/Next.js, and Python/Django/FastAPI codebases. Loads framework-specific reference guides and checks against framework conventions. Generates severity-scored findings with copy-pasteable fix prompts. Trigger phrases: "framework review", "framework check", "laravel best practices", "react best practices", "framework audit", "framework-specific review".
Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Patterns for robust error handling across TypeScript, Python, and Go. Covers typed errors, error boundaries, retries, circuit breakers, and user-facing error messages.
Quantitative strategy generation and optimisation framework via Longbridge — create, modify, and backtest quant strategies: parameter grid search, walk-forward validation, overfitting detection (in-sample vs. out-of-sample), strategy combination (multi-strategy correlation diversification), Sharpe / Calmar ratio optimisation. Generates Python code frameworks for local execution. Triggers: "策略优化", "策略生成", "参数优化", "网格搜索", "回测优化", "过拟合", "walk-forward", "策略回测优化", "策略組合", "策略優化", "策略生成", "參數優化", "網格搜索", "回測優化", "strategy optimization", "strategy generation", "parameter optimization", "grid search", "overfitting", "walk-forward validation", "strategy backtest", "Sharpe ratio", "Calmar ratio".
Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
BFL FLUX API integration guide covering endpoints, async polling patterns, rate limiting, error handling, webhooks, and regional endpoints with Python and TypeScript code examples.