when-training-neural-networks-use-flow-nexus-neural
skillname: when-training-neural-networks-use-flow-nexus-neural
npx skills add majiayu000/claude-skill-registry --skill when-training-neural-networks-use-flow-nexus-neural-dnyoussef-ai-dnyoussef-ai-chrome-extension --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guides a team through setting up a Flow Nexus neural training environment, coordinating distributed training, and deploying models. Defines roles for ml-developer, flow-nexus-neural, and cicd-engineer, and prescribes actions to authenticate, initialize clusters, configure architecture, deploy nodes, create training scripts, run distributed training, monitor progress, and perform validation.
How it works
- Phase 1: Setup Flow Nexus
- Authenticate with Flow Nexus, initialize a neural training cluster with architecture transformer and mesh topology, store cluster ID in memory.
- Prepare deployment environment and configuration files for training (neural/configs/training.json) and memory context updates.
- Phase 2: Configure Neural Network
- Retrieve cluster info, list templates, design a custom Transformer-based architecture with embedding, transformer-encoder, dense layers, dropout, and final softmax. Save to neural/configs/architecture.json and notify coordination.
- Deploy neural nodes (worker, parameter_server, validators), connect in mesh topology, and store node information. Create training script neural/scripts/train.py that loads architecture and training config, prepares dataset, and runs training logic. Create and store training configuration details and post-edit hooks.
- Phase 3: Train Model
- Start distributed training with dataset configuration (training-dataset), monitor status, and persist training metrics (final_loss, final_accuracy) in memory. Build monitoring.json for metrics/alerts/checkpoints and a backup script for checkpoints. Store phase3-complete in memory when done.
- Phase 4: Validate Results
- Retrieve cluster and training metrics, create a Python validation script neural/tests/validation.py to assess final accuracy, convergence, and overfitting, and run it. Run performance benchmarks and distributed inference, store benchmark results, and perform final validation checks. Store phase4-complete in memory with final metrics.
When to use it
Use when training neural networks with Flow Nexus in a distributed environment, requiring explicit orchestration across ml-developer, flow-nexus-neural, and cicd-engineer roles, from setup through validation.
What it can touch
- Tools: claude-code via npx claude-flow@alpha, mcp flow-nexus APIs, shell and Python scripts for training, monitoring, and validation. Files touched include neural/configs/training.json, neural/configs/architecture.json, neural/scripts/train.py, neural/tests/validation.py, neural/configs/monitoring.json, neural/scripts/backup-checkpoints.sh, and various memory keys like neural/cluster-id, neural/phase1-complete, neural/phase2-complete, neural/phase3-complete, neural/phase4-complete, neural/training-metrics, neural/training-job-id, neural/benchmark-results.
Caveats
- Requires Flow Nexus MCP server and optional E2B cloud sandbox access.
- Success criteria depend on validation thresholds (e.g., >85% accuracy, 100 epochs, etc. as specified in the SOP).
- Uses memory keys for persistence across phases; ensure memory store/retrieve commands succeed.
# Flow Nexus Neural Network Training SOP ```yaml metadata: skill_name: when-training-neural-networks-use-flow-nexus-neural version: 1.0.0 category: platform-integration difficulty: advanced estimated_duration: 45-90 minutes trigger_patterns: - "train neural network" - "machine learning model" - "distributed training" - "flow nexus neural" - "E2B sandbox training" dependencies: - flow-nexus MCP server - E2B account (optional for cloud) - Claude Flow hooks agents: - ml-developer (primary model architect) - flow-nexus-neural (platform coordinator) - cicd-engineer (deployment specialist) success_criteria: - Model training completes successfully - Validation accuracy meets requirements (>85%) - Performance benchmarks within thresholds - Cloud deployment verified - Documentation generated ``` ## Overview This SOP provides a systematic workflow for training and deploying neural networks using Flow Nexus platform with distributed E2B sandboxes. It covers architecture selection, distributed training, validation, and production deployment. ## Prerequisites **Required:** - Flow Nexus MCP server installed - Basic understanding of neural network architectures - Authentication credentials (if us
- Overview
- Prerequisites
- Agent Responsibilities
- ml-developer (Primary Model Architect)
- flow-nexus-neural (Platform Coordinator)
- cicd-engineer (Deployment Specialist)
- Phase 1: Setup Flow Nexus
- Phase 2: Configure Neural Network
- Phase 3: Train Model
- Phase 4: Validate Results
- Phase 5: Deploy to Production
- Workflow Summary
- Evidence-Based Success Metrics
- Troubleshooting
Check Flow Nexus availability
npx flow-nexus@latest --version
Verify MCP connection
claude mcp list | grep flow-nexus
Pre-task coordination hook
npx claude-flow@alpha hooks pre-task --description "Setup Flow Nexus for neural training"
Restore session context
npx claude-flow@alpha hooks session-restore --session-id "neural-training-$(date +%s)"
Check authentication status
mcp__flow-nexus__auth_status { "detailed": true }What does the when-training-neural-networks-use-flow-nexus-neural skill do?
skillname: when-training-neural-networks-use-flow-nexus-neural
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill when-training-neural-networks-use-flow-nexus-neural-dnyoussef-ai-dnyoussef-ai-chrome-extension --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.