# When Training Neural Networks Use Flow Nexus Neural > When Training Neural Networks Use Flow Nexus Neural - Skill: `dnyoussef/when-training-neural-networks-use-flow-nexus-neural` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-training-neural-networks-use-flow-nexus-neural` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-training-neural-networks-use-flow-nexus-neural/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-training-neural-networks-use-flow-nexus-neural --- /*============================================================================*/ /* SKILL SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: SKILL version: 1.0.0 description: | [assert|neutral] SKILL skill for security workflows [ground:given] [conf:0.95] [state:confirmed] category: security tags: - general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute SKILL workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic security processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "SKILL", category: "security", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["SKILL", "security", "workflow"], context: "user needs SKILL capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # Flow Nexus Neural Network Training SOP ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ```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 using cloud features) **Optional:** - E2B account for cloud sandboxes - GPU resources for training - Pre-trained model weights **Verification:** ```bash # Check Flow Nexus availability npx flow-nexus@latest --version # Verify MCP connection claude mcp list | grep flow-nexus ``` ## Agent Responsibilities ### ml-developer (Primary Model Architect) **Role:** Design neural network architecture, select hyperparameters, optimize model performance **Expertise:** - Neural network architectures (Transformer, CNN, RNN, GAN, etc.) - Training optimization and hyperparameter tuning - Model evaluation and validation strategies - Transfer learning and fine-tuning **Output:** Model architecture design, training configuration, performance analysis ### flow-nexus-neural (Platform Coordinator) **Role:** Coordinate distributed training across cloud infrastructure, manage resources **Expertise:** - Flow Nexus platform APIs and capabilities - Distributed training coordination - E2B sandbox management - Resource optimization **Output:** Training orchestration, resource allocation, deployment configuration ### cicd-engineer (Deployment Specialist) **Role:** Deploy trained models to production, setup monitoring and scaling **Expertise:** - Model serving infrastructure - Docker containerization - CI/CD pipelines - Monitoring and observability **Output:** Deployment scripts, monitoring dashboards, production configuration ## Phase 1: Setup Flow Nexus **Objective:** Authenticate with Flow Nexus platform and initialize neural training environment **Evidence-Based Validation:** - Authentication token obtained and verified - MCP tools responding correctly - Training environment initialized **ml-developer Actions:** ```bash # 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)" ``` **flow-nexus-neural Actions:** ```bash # Check authentication status mcp__flow-nexus__auth_status { "detailed": true } # If not authenticated, register/login # mcp__flow-nexus__user_register { "email": "user@example.com", "password": "secure_pass" } # mcp__flow-nexus__user_login { "email": "user@example.com", "password": "secure_pass" } # Initialize neural training cluster mcp__flow-nexus__neural_cluster_init { "name": "neural-training-cluster", "architecture": "transformer", "topology": "mesh", "daaEnabled": true, "wasmOptimization": true, "consensus": "proof-of-learning" } # Store cluster ID in memory npx claude-flow@alpha memory s /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/security/SKILL/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "SKILL-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] SKILL_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]