'functions' 검색 결과

Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. If you're thinking relationally, you're thinking wrong. 2025 lesson: Firestore pricing can surprise you. Reads are cheap until they're not. A poorly designed listener can cost more than a dedicated database. Plan your data model for your query patterns, not your data relationships. Use when "firebase, firestore, firebase auth, cloud functions, firebase storage, realtime database, firebase hosting, firebase emulator, security rules, firebase admin, firebase, firestore, cloud-functions, serverless, backend, realtime, authentication, google-cloud" mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Find and list functions in a binary by name, address, regex, or byte pattern. Use as the starting point for binary analysis, to locate specific functions, or to enumerate all functions matching criteria.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Write, configure, and deploy Netlify Edge Functions (Deno runtime at the network edge) in TypeScript/JavaScript. Use when adding request/response manipulation at the edge — auth middleware, geolocation redirects, A/B testing and personalization, content localization, redirects/rewrites, SSR at the edge, or transforming responses — or when configuring path routing, response caching, or edge error handling. Triggers on tasks like "add auth middleware", "geo-based redirect", "A/B testing at the edge", "rewrite requests", or editing files in netlify/edge-functions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bu skill covers security hardening for serverless compute platforms including AWS Lambda, Azure Functions, and Google Cloud Functions. It addresses least privilege IAM roles, dependency vulnerability scanning, secrets management integration, input validation, function URL authentication, and runtime monitoring to protect against injection attacks, credential theft, and supply chain compromises.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Explore code through structural search: find functions and classes, view file outlines, and open only the implementations you need. It uses tree-sitter, a parser that identifies code structure, to limit how much source text is loaded.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding functions related to a feature
• Navigating the structure of a large source file
• Reading only a specific function's implementation

Improve existing code structure while preserving its behavior. Review dependencies and tests, identify maintainability problems such as duplication or long functions, and plan small changes with testing, reversible commits, and a final comparison of results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Splitting long functions into smaller, clearer functions
• Removing duplicated logic and unused code
• Planning improvements to hard-to-maintain legacy code

Build AWS applications with managed services that reduce server administration. Follow patterns for Lambda functions, HTTP APIs, database access, and queued background processing, with guidance on deployment and delays when functions first start.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build HTTP APIs with Lambda and API Gateway
• Read and write application data in DynamoDB
• Process SQS messages with retries and a dead-letter queue

Plan Azure infrastructure around application needs, scale, and budget. Generate Bicep templates, files that define cloud resources as code, then review estimated costs, configure deployment pipelines, and check security settings before production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning Azure infrastructure for a web app
• Choosing services for microservices or event-driven processing
• Generating Bicep templates and deployment pipelines

Check that functions, methods, and imports exist in the software version being used before writing code. Identify uncertain calls, framework mix-ups, and deprecated interfaces so implementation decisions rely on verified details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking functions against an installed library version
• Avoiding API mix-ups between frameworks
• Verifying package import paths before coding

Develop or port CPU kernels, the low-level calculation routines used by MNN. Follow staged implementation guidance, check data layout agreements, register functions, and verify correctness across processor instruction sets and numerical precision settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing or porting an MNN CPU kernel
• Adding support for a processor instruction set
• Changing packed data layouts or tile sizes

Identify which Convex functions drive spending by combining data read per call with call volume. Use deployment evidence or query structure to explain how costs grow with usage and data size, then suggest the cheapest fix for each driver.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find functions that drive Convex spending
• Assess cost growth as data and traffic increase
• Estimate future cost drivers before traffic arrives

Explain an existing Convex app by mapping its tables, relationships, public and internal functions, authentication, and external connections. Trace representative requests through the data flow to provide a readable architecture overview without modifying the app.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand an unfamiliar Convex app
• Map table relationships and data access paths
• Identify public and internal functions

Write, review, and refactor Python code using concrete readability rules. Keep functions focused, reduce nested logic, clarify names and expressions, and apply Python features such as comprehensions, dataclasses, and pathlib while respecting existing codebase conventions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refactoring long functions and deeply nested logic
• Reviewing names and complex expressions
• Organizing code with Python standard library features

Deploy serverless browser automation as cloud functions using Browserbase. Use when the user wants to deploy browser automation to run on a schedule or cron, create a webhook endpoint for browser tasks, run automation in the cloud instead of locally, or asks about Browserbase Functions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Creates and deploys Node.js HTTP web applications on AWS Lambda Web Functions. Guides users through building web apps and APIs with Express, Fastify, or Hono, packaging, deploying via the aws lambda-web CLI, and iterating. Triggers when user wants to deploy a website or web app to AWS as serverless, deploy a Node.js HTTP server to AWS, build a serverless website or API with Node.js, deploy Express/Hono/Fastify apps, set up multi-region HTTP endpoints, connect a Node.js web app to private VPC resources, or explicitly mentions Lambda Web Functions. Does NOT trigger for non-Node.js runtimes, event-driven patterns (SQS, Kinesis, S3 events), or container deployments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluates, configures, and migrates workloads to AWS Lambda Managed Instances (LMI). Runs Lambda Event Functions (standard Lambda) on EC2 instances in the user's account while AWS manages provisioning, patching, scaling, routing, and load balancing. Triggers when queries mention Lambda Managed Instances, LMI, capacity providers, multi-concurrent execution environments, EC2-backed Lambda, persistent Lambda instances, PerExecutionEnvironmentMaxConcurrency, CapacityProviderConfig, cold start elimination via dedicated instances, migrating Lambda Event Functions to managed instances, or cost comparison between Lambda Event Functions and LMI with Savings Plans or Reserved Instances. Also covers long-running and asynchronous workloads and the 90-minute (5400s) function timeout for asynchronous and event-source-mapping (SQS, Kinesis, DynamoDB Streams / ESM) invocations, including how to raise the function timeout up to 90 minutes / 5400s and the related duration limits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy serverless functions on Google Cloud Functions. Configure triggers and manage deployments. Use when implementing serverless workloads on GCP.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

CloudBase function runtime guide for building, deploying, and debugging your own Event Functions or HTTP Functions. This skill should be used when users need application runtime code on CloudBase, not when they are merely calling CloudBase official platform APIs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) <doi:10.1093/bioinformatics/bty633> and 'phangorn' Schliep (2011) <d

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation),

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.