Results for “base”

Write unit and integration tests for Angular v20+ applications using Vitest or Jasmine with TestBed and modern testing patterns. Use for testing components with signals, OnPush change detection, services with inject(), and HTTP interactions. Triggers on test creation, testing signal-based components, mocking dependencies, or setting up test infrastructure. Don't use for E2E testing with Cypress or Playwright, or for testing non-Angular JavaScript/TypeScript code.

Expert-level skill for Jira Query Language (JQL). Use when the user asks about writing, debugging, optimizing, or understanding JQL queries; needs to filter Jira issues by complex criteria, date ranges, history, or cross-project conditions; wants to build saved filters, dashboard gadgets, or automation rules; or needs guidance on JQL performance, functions, operators, history operators (WAS/CHANGED), relative dates, role-based query patterns, or the JQL REST API.

Project Lombok for reducing Java boilerplate. Covers annotations for getters, setters, constructors, builders, logging, and more. Based on production patterns from castellino and gestionale-presenze projects. USE WHEN: user mentions "lombok", "@Data", "@Builder", "@Slf4j", asks about "boilerplate reduction", "getters/setters", "@RequiredArgsConstructor", "@Value" DO NOT USE FOR: Java language features - use `java` skill instead DO NOT USE FOR: MapStruct integration - use `mapstruct` skill DO NOT USE FOR: Spring annotations - use `backend-spring-boot` skill

Comprehensive expertise in blockchain game development, including play-to-earn (P2E) mechanics, play-to-own (P2O) models, in-game economies, NFT integration, dual token systems, and sustainable game tokenomics. Covers Unity/Unreal blockchain integration, anti-cheat considerations, and DeFi-gaming fusion. Use when "web3 game, blockchain game, play to earn, P2E, play to own, P2O, game economy, in-game tokens, gaming NFT, game items, metaverse, gamefi, skill-based gaming, gaming rewards, virtual economies, " mentioned.

Conjunto abrangente de ferramentas para modelos de linguagem de proteínas, incluindo ESM3 (design multimodal generativo de proteínas em sequência, estrutura e função) e ESM C (embeddings e representações eficientes de proteínas). Use essa skill ao trabalhar com sequências de proteínas, estruturas ou predição de função; designing de proteínas inovadoras; geração de embeddings de proteínas; inverse folding; ou tarefas de engenharia de proteínas. Suporta tanto uso local de modelos quanto Forge API baseada em nuvem para inferência escalável.

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.

Query the STRING API for protein-protein interactions (59M proteins, 20B interactions across 5000+ species), building interaction networks, discovering functional partners, and running GO/KEGG/Pfam enrichment on protein lists. Use when constructing a protein-protein interaction network, expanding from seed proteins to functional partners, or running PPI-based enrichment for systems biology; for curated metabolic pathway maps and reactions prefer alterlab-kegg, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

Port MATLAB Data Acquisition Toolbox code from the discouraged (legacy) session-based interface (daq.createSession, addAnalogInputChannel, startBackground, DataAvailable listeners, queueOutputData, wait) to the recommended DataAcquisition interface (daq("ni"), addinput, start, ScansAvailableFcn, write, preload). Use when migrating legacy DAQ scripts, converting session-API calls, working with DataAcquisition objects, writing multi-feature DAQ scripts that combine triggers, callbacks, continuous acquisition, or analog output. Also use when a user says their DAQ script "used to work" or "errors on R20XX", since modernizing legacy session-API code is a frequent fix for those failures. Trigger keywords: daq, DAQ, NI, session interface, addtrigger, addclock, ScansAvailableFcn, ScansRequiredFcn, daq.createSession, addAnalogInputChannel, startBackground, queueOutputData, DataAvailable, evt.Data, evt.TimeStamps, wait(d), preload, discouraged, legacy, modernize, R2020a.

Use this skill for on-chain DEX operations: token search, swap quotes, DEX trading, wallet portfolio/balance queries, gas estimation, and transaction broadcasting across 20+ blockchains (Ethereum, Solana, Base, BSC, Arbitrum, Polygon, etc.). Use when user says: 'swap ETH for USDC', 'buy token on-chain', 'DEX swap', 'token search on-chain', 'wallet balance', 'portfolio value', 'gas price', 'broadcast transaction', 'trending on-chain tokens', 'hot tokens', 'token holders', 'token liquidity', 'smart money signal', 'whale signal', 'K-line on-chain', '链上交易', '链上swap', 'DEX交易', '买币', '链上行情', '钱包余额', '持仓', 'gas费', '广播交易', '链上热门币', '聪明钱', '巨鲸信号'. Powered by OKX Web3 DEX API with 500+ liquidity sources. MUST run node scripts — NEVER fabricate on-chain data. For CEX trading (Binance/OKX spot/futures), use aicoin-trading. For CEX market data (funding rates, OI, liquidation), use aicoin-market.