feat: Add MLP surrogate with Turbo Mode for 100x faster optimization
Neural Acceleration (MLP Surrogate): - Add run_nn_optimization.py with hybrid FEA/NN workflow - MLP architecture: 4-layer (64->128->128->64) with BatchNorm/Dropout - Three workflow modes: - --all: Sequential export->train->optimize->validate - --hybrid-loop: Iterative Train->NN->Validate->Retrain cycle - --turbo: Aggressive single-best validation (RECOMMENDED) - Turbo mode: 5000 NN trials + 50 FEA validations in ~12 minutes - Separate nn_study.db to avoid overloading dashboard Performance Results (bracket_pareto_3obj study): - NN prediction errors: mass 1-5%, stress 1-4%, stiffness 5-15% - Found minimum mass designs at boundary (angle~30deg, thick~30mm) - 100x speedup vs pure FEA exploration Protocol Operating System: - Add .claude/skills/ with Bootstrap, Cheatsheet, Context Loader - Add docs/protocols/ with operations (OP_01-06) and system (SYS_10-14) - Update SYS_14_NEURAL_ACCELERATION.md with MLP Turbo Mode docs NX Automation: - Add optimization_engine/hooks/ for NX CAD/CAE automation - Add study_wizard.py for guided study creation - Fix FEM mesh update: load idealized part before UpdateFemodel() New Study: - bracket_pareto_3obj: 3-objective Pareto (mass, stress, stiffness) - 167 FEA trials + 5000 NN trials completed - Demonstrates full hybrid workflow 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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docs/protocols/extensions/EXT_04_CREATE_SKILL.md
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# EXT_04: Create New Skill
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<!--
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PROTOCOL: Create New Skill or Module
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LAYER: Extensions
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VERSION: 1.0
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STATUS: Active
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LAST_UPDATED: 2025-12-05
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PRIVILEGE: admin
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LOAD_WITH: []
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-->
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## Overview
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This protocol guides you through creating new skills or skill modules for the LLM instruction system. Skills provide task-specific guidance to Claude sessions.
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**Privilege Required**: admin
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---
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## When to Use
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| Trigger | Action |
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|---------|--------|
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| Need new LLM capability | Follow this protocol |
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| "create skill", "new skill" | Follow this protocol |
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| Task pattern needs documentation | Follow this protocol |
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---
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## Skill Types
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| Type | Location | Purpose | Example |
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|------|----------|---------|---------|
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| Bootstrap | `.claude/skills/0X_*.md` | LLM orientation | 00_BOOTSTRAP.md |
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| Core | `.claude/skills/core/` | Always-load skills | study-creation-core.md |
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| Module | `.claude/skills/modules/` | Optional, load-on-demand | extractors-catalog.md |
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| Dev | `.claude/skills/DEV_*.md` | Developer workflows | DEV_DOCUMENTATION.md |
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---
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## Step-by-Step Guide
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### Step 1: Determine Skill Type
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**Bootstrap (0X_)**: System-level LLM guidance
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- Task classification
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- Context loading rules
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- Execution patterns
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**Core**: Essential task skills that are always loaded
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- Study creation
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- Run optimization (basic)
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**Module**: Specialized skills loaded on demand
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- Specific extractors
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- Domain-specific (Zernike, neural)
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- Advanced features
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**Dev (DEV_)**: Developer-facing workflows
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- Documentation maintenance
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- Testing procedures
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- Contribution guides
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### Step 2: Create Skill File
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#### For Core/Module Skills
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```markdown
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# {Skill Name}
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**Version**: 1.0
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**Purpose**: {One-line description}
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---
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## Overview
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{What this skill enables Claude to do}
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---
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## When to Load
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This skill should be loaded when:
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- {Condition 1}
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- {Condition 2}
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---
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## Quick Reference
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{Tables with key patterns, commands}
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---
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## Detailed Instructions
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### Pattern 1: {Name}
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{Step-by-step instructions}
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**Example**:
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\`\`\`python
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{code example}
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\`\`\`
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### Pattern 2: {Name}
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{Step-by-step instructions}
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---
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## Code Templates
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### Template 1: {Name}
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\`\`\`python
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{copy-paste ready code}
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\`\`\`
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---
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## Validation
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Before completing:
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- [ ] {Check 1}
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- [ ] {Check 2}
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---
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## Related
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- **Protocol**: [{related}]({path})
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- **Module**: [{related}]({path})
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```
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### Step 3: Register Skill
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#### For Bootstrap Skills
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Add to `00_BOOTSTRAP.md` task classification tree.
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#### For Core Skills
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Add to `02_CONTEXT_LOADER.md`:
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```yaml
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{TASK_TYPE}:
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always_load:
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- core/{skill_name}.md
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```
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#### For Modules
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Add to `02_CONTEXT_LOADER.md`:
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```yaml
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{TASK_TYPE}:
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load_if:
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- modules/{skill_name}.md: "{condition}"
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```
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### Step 4: Update Navigation
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Add to `01_CHEATSHEET.md` if relevant to common tasks.
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### Step 5: Test
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Test with fresh Claude session:
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1. Start new conversation
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2. Describe task that should trigger skill
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3. Verify correct skill is loaded
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4. Verify skill instructions are followed
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---
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## Skill Design Guidelines
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### Structure
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- **Front-load**: Most important info first
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- **Tables**: Use for structured data
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- **Code blocks**: Complete, copy-paste ready
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- **Checklists**: For validation steps
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### Content
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- **Task-focused**: What should Claude DO?
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- **Prescriptive**: Clear instructions, not options
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- **Examples**: Show expected patterns
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- **Validation**: How to verify success
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### Length Guidelines
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| Skill Type | Target Lines | Rationale |
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|------------|--------------|-----------|
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| Bootstrap | 100-200 | Quick orientation |
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| Core | 500-1000 | Comprehensive task guide |
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| Module | 150-400 | Focused specialization |
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### Avoid
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- Duplicating protocol content (reference instead)
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- Vague instructions ("consider" → "do")
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- Missing examples
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- Untested code
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---
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## Module vs Protocol
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**Skills** teach Claude HOW to interact:
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- Conversation patterns
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- Code templates
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- Validation steps
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- User interaction
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**Protocols** document WHAT exists:
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- Technical specifications
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- Configuration options
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- Architecture details
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- Troubleshooting
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Skills REFERENCE protocols, don't duplicate them.
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---
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## Examples
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### Example: Domain-Specific Module
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`modules/thermal-optimization.md`:
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```markdown
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# Thermal Optimization Module
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**Version**: 1.0
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**Purpose**: Specialized guidance for thermal FEA optimization
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---
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## When to Load
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Load when:
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- "thermal", "temperature", "heat" in user request
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- Optimizing for thermal properties
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---
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## Quick Reference
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| Physics | Extractor | Unit |
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|---------|-----------|------|
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| Max temp | E11 | K |
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| Gradient | E12 | K/mm |
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| Heat flux | E13 | W/m² |
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---
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## Objective Patterns
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### Minimize Max Temperature
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\`\`\`python
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from optimization_engine.extractors import extract_temperature
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def objective(trial):
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# ... run simulation ...
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temp_result = extract_temperature(op2_file)
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return temp_result['max_temperature']
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\`\`\`
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### Minimize Thermal Gradient
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\`\`\`python
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from optimization_engine.extractors import extract_thermal_gradient
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def objective(trial):
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# ... run simulation ...
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grad_result = extract_thermal_gradient(op2_file)
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return grad_result['max_gradient']
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\`\`\`
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---
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## Configuration Example
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\`\`\`json
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{
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"objectives": [
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{
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"name": "max_temperature",
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"type": "minimize",
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"unit": "K",
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"description": "Maximum temperature in component"
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}
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]
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}
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\`\`\`
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---
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## Related
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- **Extractors**: E11, E12, E13 in SYS_12
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- **Protocol**: See OP_01 for study creation
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```
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---
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## Troubleshooting
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| Issue | Cause | Solution |
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|-------|-------|----------|
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| Skill not loaded | Not in context loader | Add loading rule |
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| Wrong skill loaded | Ambiguous triggers | Refine conditions |
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| Instructions not followed | Too vague | Make prescriptive |
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---
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## Cross-References
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- **Context Loader**: `.claude/skills/02_CONTEXT_LOADER.md`
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- **Bootstrap**: `.claude/skills/00_BOOTSTRAP.md`
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- **Related**: [EXT_03_CREATE_PROTOCOL](./EXT_03_CREATE_PROTOCOL.md)
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---
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## Version History
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| Version | Date | Changes |
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|---------|------|---------|
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| 1.0 | 2025-12-05 | Initial release |
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