“coordination”的搜索结果

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.

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

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.

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

Coordinate a team of AI agents by setting direction, assigning responsibilities, allocating resources, and tracking progress. Support hierarchical decisions, group consensus, and emergency control while recording shared state and preparing handovers to keep work moving.

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• Assigning research, implementation, and memory roles to agents
• Adjusting resource allocation and task priorities
• Tracking shared progress and agent responses

Turn complex goals into actionable plans by mapping dependencies, prioritizing tasks, and allocating time, budget, and people. Supports coordination across multiple agents and revises plans when progress or constraints change.

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• Planning a feature launch under a deadline
• Allocating people and budget across competing goals
• Revising plans when requirements change

Measure throughput, response times, and resource use for systems and groups of agents. Use baseline comparisons, load tests, and performance criteria to identify slowdowns, assess behavior under pressure, and summarize results with recommendations.

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• Comparing performance before and after a code change
• Finding system limits through increasing load
• Checking coordination and load balancing across agents

Monitor agent and system performance through live metrics, resource tracking, bottleneck analysis, and anomaly detection. Track response times and availability against service targets, and prepare dashboard data and performance reports.

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• Investigate slow agent coordination
• Track CPU and memory usage
• Monitor response times against service targets

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

Analyze slow development workflows and agent coordination using execution times, resource use, dependencies, and communication patterns. Identify bottlenecks, prioritize causes, and build an improvement plan covering parallel work, caching, or resource allocation.

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• Investigate slow test execution
• Find unnecessary waits in task sequences
• Reduce communication delays between agents

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

Design and implement ways for multiple agents or distributed computers to agree on decisions. Covers weighted voting, task allocation, coordination, and protocols that handle faulty or malicious participants, with tools for mathematical analysis.

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• Design agreement protocols for multiple agents
• Build voting systems with weighted influence
• Review protocols for faulty or malicious nodes

Initialize a group of collaborating AI agents by choosing how they connect, allocating resources, and setting up communication. Establish shared-memory rules so agents record progress, share outputs, check dependencies, and signal completion.

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• Setting up an agent group for REST API development
• Configuring eight agents in a hierarchy for feature development
• Preparing a mesh of agents for distributed code analysis

Inspect how a task ran across agents by collecting recorded execution spans, or timed processing steps. Build a trace tree showing agent activity, duration, errors, and bottlenecks, with a summary of the execution flow.

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• Investigating a slow task
• Reviewing coordination between agents
• Locating errors in recorded execution

Connect ruflo agents running on different machines to share status, tasks, and results through signed messages. Coordinate work ownership across nodes, verify messages against registered public keys, and synchronize shared rooms over HTTP.

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• Sharing agent status and results across machines
• Coordinating ownership of shared tasks
• Handing work from one node to another

Pause, resume, or complete an AI team while preserving its work evidence. Check the current state before changes, identify unfinished tasks that prevent run completion, and offer an evidence bundle before closing the work.

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• Pause an AI team's work
• Resume a paused team
• Close a run after all tasks are complete

Plan an AI team around a goal, defining roles, deliverables, and a recorded unit budget. After approval, it creates team, run, and task records in RuFlo AI Team; it does not execute the work.

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• Plan roles for a new AI team
• Split a large goal into tasks with clear owners
• Organize a research, release, or security review team

Coordinate software releases on GitHub, from version updates and release notes to testing, builds, deployment, and rollback. It describes workflows using multiple AI agents, including coordinated package releases and gradual deployments with monitoring.

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• Prepare version updates and release notes
• Coordinate releases across multiple packages
• Run tests and security checks before release

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

Coordinate changes across multiple GitHub repositories, align package versions, synchronize documentation, and run integration tests. The skill also guides shared repository templates and organizes AI agents to handle related development tasks.

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• Aligning dependency versions across repositories
• Updating projects that use a shared library
• Standardizing documentation and CI workflows

Set up and operate Ruflo to coordinate multiple AI coding agents. This skill guides project initialization, shared memory, task assignment, health checks, and plugin discovery for work that needs coordinated or parallel execution.

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• Initializing a multi-agent development environment
• Coordinating parallel work across agents
• Keeping searchable memory across sessions

Create and improve GitHub Actions workflows for automated testing and deployment. Coordinate multiple AI agents to review pull requests, investigate failed runs, manage releases, and analyze workflow performance and operating costs.

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• Creating workflows suited to a project's languages
• Improving test parallelization and caching
• Investigating failures and proposing fixes

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.

中文简介正在准备中。当前显示英语简介。

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

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

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

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