“parallel”的搜索结果

通过OpenAI Codex CLI完成现有项目的功能开发、代码重构和拉取请求审查。也涵盖长时间任务的进度检查，以及使用独立Git工作目录并行修复多个问题的步骤。

• 为现有项目添加功能
• 重构认证模块
• 审查拉取请求

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.

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

Create workflows that pause for approval and resume later, or scripts that distribute work across multiple agents. Choose a saved template for ongoing processes or a JavaScript script to collect structured results from parallel tasks.

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• Build a workflow with approval checkpoints
• Split review work across multiple agents
• Collect structured results from research tasks

Run four cost checks in parallel to assess budget limits, accelerating daily spending, unusual sessions, and projected budget exhaustion. Combine their results into one health status and exit code for automated CI workflows.

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• Combine four cost checks into one CI step
• Detect accelerating daily spending
• Flag sessions with unusually high costs

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.

中文简介正在准备中。当前显示英语简介。

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

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

Coordinate multiple AI agents for research, development, testing, and system analysis using Claude Flow. Choose how agents communicate, assign specialist roles, combine parallel and sequential tasks, and monitor progress through reporting and recovery workflows.

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• Divide research, analysis, and report writing among agents
• Coordinate application development across specialist agents
• Run multiple types of tests in parallel

Execute a written work plan by assigning implementation and verification to subagents. Track progress, isolate work in Git worktrees, record test evidence, and require independent verification before marking tasks complete, with optional pull request delivery.

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• Executing a saved work plan
• Assigning independent tasks to parallel agents
• Resuming work with recorded progress and evidence

Take implementation work through small, independently mergeable pull requests. Use a separate working directory for each PR, record manual QA evidence, write English review descriptions, resolve CI and review findings, then merge and clean up.

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• Deliver implementation work through pull requests
• Split a large task into small, independently mergeable PRs
• Build independent PRs concurrently

Coordinate large migrations, refactors, and bulk renames by splitting them into independent tasks. After plan approval, 5–30 agents work in separate Git worktrees, isolated working directories, then review, test, and open a pull request for each task.

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• Migrate code across many files
• Refactor independent modules in parallel
• Rename identifiers across directories

Review code changes with several independent reviewers working in parallel, then receive organized findings. Request a quick or thorough review, choose to hand off fixes, or run without interaction when another workflow or CI job initiates the review.

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• Review code changes from multiple independent perspectives
• Choose a quick or thorough review
• Receive findings with fixes handed off

Build Python workloads on Modal for cloud execution, including GPU model training and inference, web APIs, scheduled jobs, and parallel batch processing. Configure container environments, persistent storage, and automatic scaling through the Modal SDK.

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• Serving an AI model through a web API
• Running model training or fine-tuning on cloud GPUs
• Processing independent batch inputs in parallel

Use Dask to process tables, numerical arrays, and file collections in pieces across CPU cores or machines. Helps choose data structures, size memory-friendly chunks, coordinate dependent tasks, and diagnose slow or memory-heavy workflows.

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• Aggregate CSV or Parquet files that exceed available memory
• Process large scientific arrays in chunks
• Clean JSON records and logs before analysis

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

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• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates

Design and improve Unity games that process large numbers of entities. Learn ECS, an approach that separates data from behavior, and apply Jobs and Burst patterns to organize parallel processing and address CPU performance bottlenecks.

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• Designing Unity games with thousands of entities
• Converting object-oriented game logic to ECS
• Addressing CPU performance bottlenecks

Organize parallel code reviews across security, performance, architecture, testing, and accessibility. It helps assign review areas, merge duplicate findings, apply consistent severity ratings, and produce one report with locations, impacts, suggested fixes, and prioritized actions.

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• Assigning review areas for a code change
• Merging duplicate findings from multiple reviewers
• Aligning severity ratings across review reports

Split multi-step coding, research, or testing into focused assignments for sub-agents, which are agents handling specific parts of a task. The parent agent retains design decisions, integration, and final verification while independent work runs in parallel.

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• Exploring separate groups of files in parallel
• Assigning code changes with clear file ownership
• Running focused tests alongside other work

Control interactive command-line tools through tmux by sending keystrokes, capturing terminal output, and waiting for prompts. Supports session management and running multiple coding agents in parallel, with helper scripts for finding sessions and detecting text.

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• Operating an interactive Python session
• Waiting for prompts and reading terminal output
• Running Codex or Claude Code sessions in parallel

Research a question by splitting it into two to five topics and assigning parallel documentation agents. Get a combined summary, detailed findings for each topic, and source URLs from web searches and documentation lookups.

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• Researching implementation best practices
• Comparing libraries for a project
• Looking up current framework conventions

Create separate Git working directories for issue fixes, pull request reviews, and feature development. Optional tmux terminal sessions help manage parallel tasks, return to ongoing work, and clean up completed sessions.

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• Working on several issue fixes in parallel
• Preparing a separate workspace for a pull request review
• Returning to an ongoing development session

Explains how to replace the removed $pipeline command with $plan for planning and $team for coordinated parallel work. This retirement notice provides migration guidance rather than an executable workflow of its own.

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• Updating instructions that still reference $pipeline
• Choosing separate commands for planning and parallel execution
• Finding the command for persistent goal runs

Find the migration path for the removed OMX parallel execution command. This retirement notice directs users to replace `$ultrawork` with `$team`, which manages worker panes, shared task state, messaging, dispatch, and coordinated worker lifecycles.

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• Check the replacement for a removed command
• Update old instructions to use `$team`
• Identify which feature manages parallel workers

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

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• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Coordinates specialist agents in Claude Code to work from product requirements through design, implementation, testing, deployment, and operations. Includes task queues, saved progress, parallel reviews, and recovery workflows for managing extended development work.

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• Turning product requirements into development tasks
• Coordinating implementation, testing, and reviews
• Resuming extended work from saved progress