'uv' 검색 결과

Coordinate multiple AI agents for research, development, testing, and system analysis. It guides role assignment, communication structures, parallel and sequential tasks, shared memory, progress monitoring, and recovery, bringing separate contributions into reports or coordinated development workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Gather research in parallel and compile a report
• Coordinate application design, implementation, and review
• Run unit, integration, security, and performance tests in parallel

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Consolidating separate agent memory systems
• Migrating SQLite and Markdown data to AgentDB
• Finding stored information with similar meaning

Improve the code that plans, reviews, and controls agent work while keeping the model fixed. Darwin Mode tests changes in isolated environments and retains variants that pass tests and show measurable improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing changes to planning and review logic
• Evaluating retry, tool, and memory policies
• Trying an offline dry run

Improve the MCP server that connects claude-flow v3 to tools. It guides connection reuse, faster tool lookup, request distribution, and communication changes, with monitoring to evaluate response times, errors, memory use, and cache performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyze slow MCP startup and tool responses
• Reuse connections and clean up idle ones
• Improve tool lookup and distribute requests

Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding newly available router models to chat-ui
• Writing concise descriptions of model capabilities
• Updating production and development model entries together

Deploy teams of AI agents on Flow Nexus and automate development, research, or data processing workflows. Define step dependencies and event triggers, assign suitable agents, and monitor progress, queues, and execution history.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automating testing, builds, and deployment
• Distributing research and analysis across agents
• Scheduling data extraction, transformation, and loading

Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Coordinate specialist agents in Claude Flow under a lead agent. Distribute development or research tasks, use voting to make decisions, preserve shared knowledge, and pause or resume work through managed sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Splitting application development among specialist agents
• Comparing technologies through research and voting
• Coordinating code reviews across specialties

Connect agent tasks into a sequence where each step receives the previous output. Use custom prompts or predefined workflows to analyze code, plan refactoring, generate tests, optimize performance, or transform data through multiple stages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn code analysis into an improvement plan
• Prioritize and refactor existing code
• Find test gaps and generate unit tests

Train, deploy, and run predictions with neural networks using Flow Nexus and E2B sandboxes, isolated computing environments. Use existing model templates, distribute training across multiple environments, and check training progress and model performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model to classify images
• Analyze text sentiment with an existing template
• Train an LSTM model for time series forecasting

Turn a feature request into a minimal implementation plan before coding. Identify which files need changes, explain why, define the smallest required interface, and flag broader effects or expanded permissions for the implementer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a feature before coding
• Identify files affected by a change
• Review scope and permission changes

Build custom Claude Code skills with clear instructions, reusable templates, and organized supporting files. The skill guides you through YAML metadata, meaning the skill’s name and description, and separates essential steps from detailed references.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning recurring workflows into custom skills
• Creating skill templates with supporting scripts
• Organizing long instructions into reference files

Organize software development from requirements and architecture through testing, review, and deployment. Use specialized agent roles, shared memory, and test-driven development, where tests are written before code, to coordinate work across the project.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan and implement a new feature
• Refactor legacy code with supporting tests
• Review code security and performance

Restructure claude-flow v3 into focused modules using Domain-Driven Design, which groups code by responsibility. Define module boundaries, event communication, and plugin interfaces to support a modular architecture with independently testable business logic.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Split an oversized orchestrator into focused modules
• Define boundaries between tasks, sessions, and monitoring
• Design plugin extensions and domain events

Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare performance before and after v3 optimization
• Evaluate vector search speed and memory use
• Track performance regressions over time

Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automating login flows
• Filling and submitting web forms
• Extracting product names, prices, and links

Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build agent memory that reuses past experiences
• Record and assess sequences of agent actions
• Consolidate similar experiences into reusable patterns

Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run API, interface, and test work in parallel
• Build a design-to-review workflow with ordered stages
• Share specifications and state across agents

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Manage code changes across multiple AI agents while recording operations and their outcomes. Use previous task histories to find similar attempts, inspect successful patterns, and obtain suggested steps for implementation, code review, deployment, or merging changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Manage code changes by multiple AI agents
• Get deployment suggestions from previous outcomes
• Inspect failed merge attempts before trying again

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Coordinate 15 agents implementing claude-flow v3 across security, core systems, integration, testing, performance, and release work. Provides role assignments, task dependencies, GitHub tracking examples, and a four-phase schedule spanning 14 weeks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assign claude-flow v3 work across 15 agents
• Run tasks in parallel while respecting dependencies
• Track milestones and progress through GitHub issues

Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train agents from historical experience
• Build decision-making for games and navigation
• Combine experience from multiple agents for training