[define|neutral] SKILL := { name: "SKILL", category: "platforms", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

[define|neutral] COGNITIVE_FRAME := { frame: "Compositional", source: "German", force: "Build from primitives?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

[define|neutral] TRIGGER_POSITIVE := { keywords: ["SKILL", "platforms", "workflow"], context: "user needs SKILL capability" } [ground:given] [conf:1.0] [state:confirmed]

Flow Nexus Neural Network Training SOP

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

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:

# 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:

# 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:

# 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/platforms/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] <promise>SKILL_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]