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Delegate feature development, code refactoring, and pull request reviews to OpenAI Codex CLI. The skill also covers monitoring long tasks and fixing multiple issues in parallel using separate Git working directories.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.