Measure code performance and run benchmarks to investigate slow operations, regressions, or memory pressure. Establish a baseline, compare results under matching conditions, report variability, and rerun correctness tests after optimization.
Coordinate multiple AI agents in a mesh, a network where peers communicate directly. It guides shared decisions, task assignment based on skills and workload, network monitoring, and recovery when agents fail or connections break.
Review a codebase for readability, performance, security, and architectural issues. The skill organizes findings into actionable recommendations, helping teams identify refactoring work, dependency risks, and areas that need better tests.
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.
Review GitHub pull requests with specialized AI agents that check security, performance, architecture, and coding conventions. Generate actionable comments with suggested fixes, and connect review findings to automated approval or change requests.
Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.
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.
Review source code for readability, complexity, duplication, and maintainability without changing files. Receive a structured report with prioritized issues, refactoring suggestions, positive findings, and estimates of technical debt—the work needed to address accumulated code problems.
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.
Coordinate groups of AI agents across multiple GitHub repositories. Analyze dependencies, organize shared updates and refactoring, run tests, and create linked pull requests so related projects can be changed together.
Manage GitHub pull requests from creation through review and merge. Using GitHub CLI, the skill prepares descriptions, coordinates reviewers, tracks automated checks, helps resolve conflicts, and selects merge approaches suited to the branch history.
Plan, train, evaluate, and deploy neural networks using Flow Nexus cloud infrastructure. Coordinate training across multiple computing environments, tune training settings, and manage model versions, predictions, and ongoing performance monitoring.
Analyze networks of people, web pages, or systems using PageRank, a method that scores importance from connections. Identify influential nodes and communities, generate personalized recommendations, and examine communication bottlenecks to guide network improvements.
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.
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.
Coordinate multiple AI agents by adjusting their communication structure and task assignments as workloads change. The skill describes performance monitoring, switching coordination patterns, forecasting agent capacity, and validating changes with rollback when performance worsens.
Plan the conversion of existing Claude Flow commands into specialized agents. Map each command to responsibilities, permitted tools, and activation rules, then outline interaction testing and a gradual rollout that keeps existing commands available during the transition.
Turn specifications and algorithm outlines into a system architecture. Define components, interfaces, data structures, and deployment plans, then document technology choices, security measures, failure handling, and plans for supporting increased usage.
Turn complex goals into actionable steps by examining requirements, expected effects, and costs. Build a path from the current situation to the desired outcome, then revise the plan when actions fail or conditions change.
Coordinate gossip protocols, where nodes pass information among peers, for distributed systems. The skill covers update propagation, state synchronization, conflicting changes, failure detection, and tuning communication so nodes eventually reach a consistent state.
Coordinate tasks across multiple AI agents by tracking workloads and capacity. Redistribute queued work to available agents, adjust priorities and deadlines, and monitor resource use to identify uneven loads and scheduling bottlenecks.
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.
Create and maintain API documentation in OpenAPI 3.0, a standard format for describing APIs. Document endpoints, request and response data, authentication, and errors with clear examples and reusable definitions.
Turn specifications and designs into working code using test-driven development: write failing tests, implement enough code to pass, then refactor. The workflow also covers error handling, performance improvements, documentation, and coordinated changes across components.
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