“multi-agent”的搜索结果

在发布文章或部署代码前，由两个独立代理按相同标准审查；仅在双方均通过后继续，否则修改并重新审查，达到迭代上限后将未解决的问题交由人工判断。

• 发布前核查文章事实
• 核对客户文案与品牌规则
• 正式部署前增加独立审查

通过 Claude DevFleet 将开发工作分配给多个 Claude Code 智能体，在独立工作目录中并行执行，并管理任务依赖、进度以及变更、测试和错误报告。

• 将开发计划拆分为带依赖关系的任务
• 在独立工作目录中并行开发
• 检查多个开发任务的进度

将大型功能开发或迁移目标拆成可分多次执行的计划，整理每个步骤的背景、依赖关系和验证方法，支持将工作交接给其他代理。

• 将大型功能拆分为多个PR
• 规划跨多次会话的迁移
• 找出可并行推进的步骤

帮助设计在 Claude Code 中反复推进开发工作的自主循环，涵盖顺序执行与并行任务，并梳理运行间的进度衔接、质量检查、变更合并和停止条件。

• 设计顺序执行的开发流程
• 在多次运行之间衔接进度
• 协调并行任务与变更合并

将视频需求整理成制作计划，按风格组建专业智能体团队并配置制作环境。通过Hermes看板分配任务、查看进度，协助应对停滞或失败的任务。

• 管理简短产品介绍视频的制作
• 让专业智能体分工制作剧情视频
• 规划音乐视频的画面与音频

依据按编程语言分类的资料，协助开发和维护Claude应用。涵盖官方SDK调用、工具集成、模型迁移、费用检查、提示词审查和评估流程。

• 在应用中接入Claude API
• 开发使用自定义工具的智能体
• 为现有Claude集成迁移模型

实现多个AgentDB数据库的同步与管理，将语义相似度搜索与属性筛选结合，并配置搜索结果去重及分布式AI系统的运行方式。

• 在多个AgentDB数据库间共享记忆
• 按日期或分类筛选语义搜索结果
• 分别存储知识和对话

通过agentic-flow为多个AI代理分配任务，组织并行工作或有先后顺序的流程，并配置共享记忆、负载均衡、失败重试与任务重新分配。

• 并行开发API、界面和测试
• 组织从设计到评审的工作流程
• 在代理间共享规范与进度

Coordinate named agent teams on Grok Build using Ruflo MCP tools or a command-line message bus. Organize design, coding, testing, and review into ordered steps, with team-specific messages, status tracking, and shutdown.

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• Coordinating design, coding, testing, and review
• Handing work between named agents on Grok Build
• Tracking messages and progress across multiple teams

Assembles teams of AI agents by analyzing task complexity, required skills, and resource needs. It covers selecting specialists, adjusting team size to workload, and refining coordination using patterns from past executions.

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• Assemble specialists for payment system refactoring
• Scale agents to process large batches of data files
• Find networking specialists for WebSocket debugging

Coordinate multiple AI agents for feature development or refactoring across several files. The skill guides team setup, task assignment, status checks, and recording successful approaches for use in later work.

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• Coordinating changes across three or more files
• Assigning feature development tasks to multiple agents
• Checking progress during a shared refactoring task

Organize complex work across specialized agents with claude-flow. Start and stop agents, check their status and metrics, and manage groups by role, using shared memory to coordinate tasks such as development, reviews, and security checks.

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• Assign bug fixes across research, coding, and testing agents
• Coordinate agents for feature development and review
• Organize specialists for security checks

Set up a coordinated team of three or more AI agents for development tasks spanning multiple files. Configure the team structure, assign specialized roles, and prepare parallel work with named agents and messaging.

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• Implementing a feature across multiple files
• Refactoring across modules with coordinated agents
• Sharing a security audit across an agent team

Coordinates multiple AI agents with agentic-flow to run tasks in parallel or through dependent stages. It covers team communication structures, shared memory, workload balancing, failure recovery, and performance monitoring for complex workflows.

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• Develop APIs, user interfaces, and tests in parallel
• Connect design, implementation, testing, and review stages
• Share API schemas across agents

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.

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

Manage code changes across multiple AI agents and record their operations and outcomes. Use those records to find successful patterns, review past failures, and get suggestions for similar development or deployment tasks.

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• Coordinate code changes across multiple AI agents
• Get deployment suggestions from past results
• Review failures from similar merge attempts

Organize software development from requirements and design through testing, review, and deployment. Uses Claude Flow to coordinate specialized agents, share decisions across sessions, and guide implementation with tests written before code.

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• Developing a feature from requirements through review
• Refactoring legacy code with supporting tests
• Investigating bugs and adding regression tests

Connect agent tasks in sequence, passing each result into the next step. Build custom workflows or use predefined pipelines for code analysis, refactoring, testing, and optimization, with execution progress, timing, and consolidated results.

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• Turn code analysis into an improvement plan
• Prioritize and carry out refactoring
• Find test gaps and generate unit tests

Coordinate specialized AI agents under a lead coordinator for research, development, and review. Configure voting rules, share persistent knowledge, assign tasks, monitor progress, and pause or resume sessions through Claude Flow.

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• Divide application development among specialized agents
• Research and compare GraphQL and REST
• Coordinate reviewers for a pull request

Have agents with different roles challenge a proposed approach, exposing weak assumptions, missed interactions, and unnecessary complexity. Surviving findings go to a dedicated planning agent, which produces sequenced tasks with explicit success criteria.

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• Checking assumptions before committing to a plan
• Finding integration risks and overlooked edge cases
• Challenging unnecessary complexity in a design

Uses multiple agents with different critical roles to challenge assumptions, complexity, risks, and alternatives. Findings that withstand review go to a dedicated planner, who turns them into sequenced tasks with explicit success criteria.

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• Reviewing assumptions before implementation
• Checking integration risks and edge cases
• Comparing alternative approaches

Build Claude applications using guidance for the official Anthropic SDK. Choose an integration approach, implement streaming and tool use, and follow workflows for model migration, prompt audits, evaluation, and cost optimization.

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• Build a summarization app with Claude API
• Choose how to run an agent with custom tools
• Migrate existing code to a newer Claude model

Review code changes in a diff, pull request, branch, or file path to identify bugs and cleanup opportunities. Findings include file locations and concrete failure scenarios, with options to apply fixes or post comments.

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• Review uncommitted changes for bugs
• Check a pull request or branch before merging
• Find duplicated logic and unnecessary work

Reviews Cherry Studio code and documentation against product decisions, architecture rules, implementation checks, and conventions. It selects a review approach based on scope and available agents, then reports findings with guidance for fixes.

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• Reviewing local changes or a branch
• Checking a pull request before merging
• Reviewing architecture documents and project skills