Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.
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.
Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.
Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.
Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.
Build agents that record task outcomes and use past experience to choose future approaches. Implement pattern matching, strategy comparisons, persistent learning records, and metrics for checking how recommendations and results change over time.
Coordinate background tasks with specialized agents for research, optimization, audits, and more. Use execution history, quality scores, success rates, and latency to guide agent selection, track performance, and check benchmark compliance.
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.
Review GitHub pull requests, or proposed code changes, with specialized AI agents covering security, performance, architecture, style, and accessibility. Organize findings, suggest fixes, and post feedback through configurable review workflows.
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.
Find prebuilt WASM agents, software components packaged in WebAssembly format, through gallery browsing and keyword search. Install agents from their configurations, or export and publish your own agents to share with the community.
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.
Manage Flow Nexus accounts, run code in sandboxes, deploy app templates, and track credits. The skill guides platform operations through connected tools, including file storage, execution monitoring, and coding challenge submissions.
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.
Organize GitHub issues, synchronize project boards, and plan sprints, or short work cycles. Coordinate multiple AI agents to break issues into tasks, assign work, update progress, and track dependencies across a project.
Measure performance in the agentic-flow worker system, including trigger detection, agent selection, caching, and concurrent operations. Run a full suite or individual benchmarks to compare response times and resource usage against configured targets.
Work with AI as a coding partner, choosing who writes code and who guides the work. Supports role switching, test generation, ongoing code review, debugging, and step-by-step explanations for learning.
Assess code change risk, locate complex areas, and inspect file statistics with ruflo analysis commands. The skill also guides running and extending ruflo’s regression guards, automated checks that help catch previously fixed problems.
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.
Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.
Analyze performance in Claude Flow swarms, groups of agents working together. Identify delays in communication, processing, memory, and networking, then generate reports and prioritized recommendations. Compare previous runs or apply available automatic optimizations.
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.
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.
Store and retrieve knowledge across agent sessions with AgentDB. Organize memories into tiers, search learned patterns, combine related memories into context, and record causal links to support later work.
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