Results for “ma”

Route web design, motion, WebGL, and visual-styling requests into the narrowest matching sub-skill of the 79-skill MengTo/Skills web-design family — Style/Design-System Packs, Full-Site Quality & Direction, Motion & Scroll Systems, WebGL/3D Backgrounds & Shaders, Library Embeds & Integrations, Component & State Effects, and CSS Technique Primitives. Use when picking and fetching the right visual recipe for a marketing site, landing page, portfolio, or dashboard shell, then combining layers in direction → layout → background → motion → component-state → polish order. Triggers on: web design, landing page, awwwards, GSAP, Lenis, scroll animation, WebGL background, shader, glass UI, dark mode design system, editorial layout, cursor trail, progressive blur, gradient border, three.js background, vanta, globe.gl, unicorn studio, marketing site polish.

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.

Comprehensive Word (.docx) skill: create, read, edit, and manipulate Word documents end-to-end. Covers turning Markdown or structured text into polished Word output, filling reusable templates ({{token}} or reference-doc), applying correct Chinese (CJK) typography defaults, generating bespoke docs from scratch with docx-js, and low-level OOXML patching including tracked changes and comments. Triggers include any mention of 'Word doc', 'word document', '.docx', '报告', '备忘录', '信函', '合同', '会议纪要', 'Markdown 转 Word', 'md 转 docx', '套模板生成 Word', '中文 Word 报告', 'tracked changes', '修订标记', 'Word 批注', 'OOXML', or requests to insert/replace images, perform find-and-replace, or convert content into a polished Word document. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Structured threat modeling skill using the PASTA framework (Process for Attack Simulation and Threat Analysis) combined with Adam Shostack's 4-question framework. Use this skill whenever the user asks to do threat modeling, security analysis, map the attack surface, identify threats, or review an application for security risks — even if they don't mention PASTA or a specific framework by name. Core activities: Component Mapping (architecture + data flows), Critical Assessment (business impact prioritization), and Logic Flaw Identification (attacker mindset on business logic). Produces: component map diagram (Mermaid), data flow diagram (Mermaid), attack tree (Mermaid), STRIDE threat table, prioritized risk register, and an actionable mitigation plan. Invoke proactively for any security review, architecture review, or "what could go wrong?" session.

Use essa skill para processar e analisar grandes conjuntos de dados tabulares (bilhões de linhas) que excedem a RAM disponível. Vaex excels em operações DataFrame out-of-core, avaliação lazy, agregações rápidas, visualização eficiente de big data e machine learning em datasets grandes. Aplique quando usuários precisarem trabalhar com arquivos CSV/HDF5/Arrow/Parquet grandes, realizar estatísticas rápidas em datasets massivos, criar visualizações de big data ou construir pipelines de ML que não cabem em memória.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Use for writing, reviewing, debugging, testing, or optimizing Python code that embeds DuckDB, executes analytical SQL, manages DuckDB connections and transactions, builds DuckDB relations, queries Parquet/CSV/Arrow/pandas/Polars inputs, or exports query results. Trigger on connection scope, parameters, replacement scans, materialization, concurrency, extensions, and query plans. Do not use for generic SQL with another engine, DuckDB CLI-only work, server-database administration, dbt-only projects, or dataframe work that does not call DuckDB.

Redige embargos de declaracao do CPC 1.022-1.026 contra qualquer decisao (omissao, contradicao, obscuridade, erro material), no prazo de 5 dias uteis, sem preparo, que INTERROMPEM o prazo do recurso principal (1.026). Cobre o prequestionamento (Sumula 98/STJ — nao protelatorio) e a multa do 1.026 §2-4. Use quando o operador disser embargar, embargos de declaracao, ED, omissao na sentenca, contradicao, obscuridade, erro material, prequestionar, juiz nao analisou meu pedido.

GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.

Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Spawns multiple parallel AI agents that compete on the same task using isolated git worktrees, evaluates results, and merges the best solution.

Interviews users about their email patterns, business context, and priorities to build a structured knowledge base that powers personalized inbox triage.

Creates and manages Cloud SQL instances, databases, and users for MySQL, PostgreSQL, and SQL Server using gcloud CLI commands.

Generates llms.txt and llms-full.txt files that provide LLM-friendly access to wiki documentation, following the llms.txt specification.

Perform CRUD operations, run queries, and manage containers on Azure Cosmos DB NoSQL API using the Python SDK.

Build AI applications using the Azure AI Projects SDK for TypeScript, managing agents, connections, deployments, datasets, indexes, and evaluations.

Send, receive, peek, and manage messages in Azure Storage Queues using the @azure/storage-queue SDK for TypeScript/JavaScript.

Send real-time messages and manage WebSocket connections using Azure Web PubSub Service SDK for Python.

Deploy and manage Docker containers on AWS ECS with Fargate or EC2, including task definitions, services, auto scaling, and troubleshooting.

Operate the VSS alert pipeline for real-time monitoring, Alert-Bridge subscriptions, Slack notifications, incident queries, and camera onboarding.

Create a comprehensive Product Requirements Document using an 8-section template covering problem, objectives, segments, value propositions, solution, and release planning.

Write reliable, maintainable Go tests using table-driven tests, subtests, benchmarks, fuzzing, and golden files following TDD methodology.

Write reliable, maintainable Kotlin tests using Kotest, MockK, coroutine testing, property-based testing, and Kover coverage following TDD methodology.

Create and optimize marketing images including blog heroes, social graphics, and ad visuals using AI generation and design tools.

Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.

Retrieve deterministic search results, metadata, and full-document Markdown from arXiv, PubMed/PMC, and US policy corpora with daily freshness checks.

Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery, for dataframe and ML workflows.

Shape a fuzzy intention into a concrete, measurable objective with explicit evidence, scope bounds, and a stop condition before starting work.

Resolve IL trim and AOT analyzer warnings in .NET projects to make them compatible with Native AOT and trimming.

Share state between Blazor components across render mode boundaries using cascading values, scoped services with change events, or CascadingValueSource via DI.

Converts Stitch designs into modular Vite and React components, or syncs existing components to match updated designs.

Coordinates end-to-end replication workflows for AEM 6.5 LTS, including environment setup, production incident response, and performance optimization.

Drafts professional emails that match your personal writing style by analyzing sent emails for tone, greeting, structure, and sign-off patterns.

Generates comprehensive GitHub project plans with Epic > Feature > Story/Enabler > Test hierarchy, dependencies, priorities, and automated issue tracking.

Launch new products from idea to first customers using frameworks for positioning, outreach, and experimentation.

Create, apply, and manage AgentRC policies to customize readiness scoring, disable checks, override impact levels, set pass-rate thresholds, and enforce CI gating.