Add persistent knowledge system that enables Atomizer to learn from every
session and improve over time.
## New Files
- knowledge_base/lac.py: LAC class with optimization memory, session insights,
and skill evolution tracking
- knowledge_base/__init__.py: Package initialization
- .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation
- docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions
## Updated Files
- CLAUDE.md: Added LAC section, communication style, AVERVS execution framework,
error classification, and "Atomizer Claude" identity
- 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration
- 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference
- 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern
## LAC Features
- Query similar past optimizations before starting new ones
- Record insights (failures, success patterns, workarounds)
- Record optimization outcomes for future reference
- Suggest protocol improvements based on discoveries
- Simple JSONL storage (no database required)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Analysis of 258 FEA trials showed best trial (#45) hitting bounds on
5 parameters. Expanded bounds to allow exploration of promising regions:
- lateral_inner_angle: max 28.5 → 30.0° (was at 99.2% of range)
- lateral_inner_pivot: min 9.0 → 7.0 mm (was at 4.6% of range)
- lateral_middle_pivot: min 18.0 → 15.0 mm (was at 7.7% of range)
- whiffle_min: min 35.0 → 30.0 mm (was at 4.0% of range)
- whiffle_outer_to_vertical: min 68.0 → 60.0° (was at 5.3% of range)
- blank_backface_angle: narrowed to 4.1-4.2° (focus on optimal region)
V14 seeds from 496 prior FEA trials (V11+V12+V13) using TPE sampler.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The Method Selector now uses relative accuracy thresholds to assess
NN suitability by comparing NN error to problem variability (CV ratio).
NNQualityAssessor features:
- Physics-based objective classification (linear, smooth, nonlinear, chaotic)
- CV ratio computation: nn_error / coefficient_of_variation
- Turbo suitability score based on relative thresholds
- Data collection from validation_report.json, turbo_report.json, and study.db
Quality thresholds by objective type:
- Linear (mass, volume): max 2% error, CV ratio < 0.5
- Smooth (frequency): max 5% error, CV ratio < 1.0
- Nonlinear (stress, stiffness): max 10% error, CV ratio < 2.0
- Chaotic (contact, buckling): max 20% error, CV ratio < 3.0
CLI output now includes:
- Per-objective NN quality table with error, CV, ratio, and quality indicator
- Turbo suitability and hybrid suitability percentages
- Warnings when NN error exceeds physics-based thresholds
Updated SYS_15_METHOD_SELECTOR.md to v2.0 with full NN Quality Assessment documentation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Phase 2 - Structural Analysis:
- extract_principal_stress: σ1, σ2, σ3 principal stresses from OP2
- extract_strain_energy: Element and total strain energy
- extract_spc_forces: Reaction forces at boundary conditions
Phase 3 - Multi-Physics:
- extract_temperature: Nodal temperatures from thermal OP2 (SOL 153/159)
- extract_temperature_gradient: Thermal gradient approximation
- extract_heat_flux: Element heat flux from thermal analysis
- extract_modal_mass: Modal effective mass from F06 (SOL 103)
- get_first_frequency: Convenience function for first natural frequency
Documentation:
- Updated SYS_12_EXTRACTOR_LIBRARY.md with E12-E18 specifications
- Updated NX_OPEN_AUTOMATION_ROADMAP.md marking Phase 3 complete
- Added test_phase3_extractors.py for validation
All extractors follow consistent API pattern returning Dict with
success, data, and error fields for robust error handling.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Dashboard enhancements:
- Add Analysis page with tabs: Overview, Parameters, Pareto, Correlations, Constraints, Surrogate, Runs
- Add PlotlyCorrelationHeatmap for parameter-objective correlation analysis
- Add PlotlyFeasibilityChart for constraint satisfaction visualization
- Add PlotlySurrogateQuality for FEA vs NN prediction comparison
- Add PlotlyRunComparison for comparing optimization runs within a study
Real-time improvements:
- Replace watchdog file-watching with SQLite database polling for better Windows reliability
- Add DatabasePoller class with 2-second polling interval
- Enhanced WebSocket messages: trial_completed, new_best, pareto_update, progress
Desktop notifications:
- Add useNotifications hook using Web Notifications API
- Add NotificationSettings toggle component
- Notify users when new best solutions are found
Config editor:
- Add PUT /studies/{study_id}/config endpoint with auto-backup
- Add ConfigEditor modal with tabs: General, Variables, Objectives, Settings, JSON
- Prevents editing while optimization is running
Enhanced Pareto visualization:
- Add dark mode styling with transparent backgrounds
- Add stats bar showing Pareto, FEA, NN, and infeasible counts
- Add Pareto front connecting line for 2D view
- Add table showing top 10 Pareto-optimal solutions
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Phase 1 - Accurate study status detection:
- Add is_optimization_running() to check for active processes
- Add get_accurate_study_status() with proper status logic
- Status now: not_started, running, paused, completed
- Add "paused" status styling (orange) to Home page
Phase 2 - Global Claude terminal:
- Create ClaudeTerminalContext for app-level state
- Create GlobalClaudeTerminal floating component
- Terminal persists across page navigation
- Shows green indicator when connected
- Remove inline terminal from Dashboard
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Reorganize dashboard: control panel on top, charts stacked vertically
- Add Set Context button to Claude terminal for study awareness
- Add conda environment instructions to CLAUDE.md
- Fix STUDY_REPORT.md location in generate-report.md skill
- Claude terminal now sends study context with skills reminder
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add trial limiting (300 max) and reduce polling to 15s for large studies
- Make dashboard layout wider with col-span adjustments
- Claude terminal now runs from Atomizer root for CLAUDE.md/skills access
- Add study context display in terminal on connect
- Add KaTeX math rendering styles for study reports
- Add surrogate tuner module for hyperparameter optimization
- Fix backend proxy to port 8001
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add embedded Claude Code terminal with xterm.js for full CLI experience
- Create WebSocket PTY backend for real-time terminal communication
- Add terminal status endpoint to check CLI availability
- Update dashboard to use Claude Code terminal instead of API chat
- Add optimization control panel with start/stop/validate actions
- Add study context provider for global state management
- Update frontend with new dependencies (xterm.js addons)
- Comprehensive README documentation for all new features
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- python launch_dashboard.py starts both backend and frontend
- Ctrl+C gracefully shuts down both servers
- Color-coded terminal output for status
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add ConvergencePlot component with running best, statistics, gradient fill
- Add ParameterImportanceChart with Pearson correlation analysis
- Add StudyReportViewer with KaTeX math rendering and full markdown support
- Update pruning endpoint to query Optuna database directly
- Add /report endpoint for STUDY_REPORT.md files
- Fix chart data transformation for single/multi-objective studies
- Update Protocol 13 documentation with new components
- Update generate-report skill with dashboard integration
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- environment.yml: Added PyTorch with CUDA 12.1, PyG (torch-geometric),
and TensorBoard for neural network training
- INSTALL_INSTRUCTIONS.md: Step-by-step guide for installing Miniconda
and setting up the Atomizer environment
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- requirements.txt: Added all dependencies including PyTorch,
torch-geometric, tensorboard for neural network training
- install.bat: One-click installation script that installs all
dependencies with proper version constraints
- train_neural.bat: Training script that runs parametric neural
network training on collected FEA data
Usage:
1. Double-click install.bat to install dependencies
2. Double-click train_neural.bat to train on bracket study
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Rebuilds missing neural network components based on documentation:
- neural_models/parametric_predictor.py: Design-conditioned GNN that
predicts all 4 optimization objectives (mass, frequency, displacement,
stress) directly from design parameters. ~500K trainable parameters.
- train_parametric.py: Training script with multi-objective loss,
checkpoint saving with normalization stats, and TensorBoard logging.
- Updated __init__.py to export ParametricFieldPredictor and
create_parametric_model for use by optimization_engine/neural_surrogate.py
These files enable the neural acceleration workflow:
1. Collect FEA training data (189 trials already collected)
2. Train parametric model: python train_parametric.py --train_dir ...
3. Run neural-accelerated optimization with --enable-nn flag
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Permanently integrates the Atomizer-Field GNN surrogate system:
- neural_models/: Graph Neural Network for FEA field prediction
- batch_parser.py: Parse training data from FEA exports
- train.py: Neural network training pipeline
- predict.py: Inference engine for fast predictions
This enables 600x-2200x speedup over traditional FEA by replacing
expensive simulations with millisecond neural network predictions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major additions:
- Training data export system for AtomizerField neural network training
- Bracket stiffness optimization study with 50+ training samples
- Intelligent NX model discovery (auto-detect solutions, expressions, mesh)
- Result extractors module for displacement, stress, frequency, mass
- User-generated NX journals for advanced workflows
- Archive structure for legacy scripts and test outputs
- Protocol documentation and dashboard launcher
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Problem:
When running optimization studies with multiple solutions (e.g., static + modal),
NX opens solution monitor windows for each trial. These windows superpose and cause
usability issues during long optimization runs.
Solution:
- Automatically disable solution monitor when solving all solutions (solution_name=None)
- Loop through all solutions and set "solution monitor" property to False
- Implemented in solve_simulation.py before solve execution (lines 271-295)
- Includes error handling with graceful fallback
Benefits:
- No monitor window pile-up during optimization studies
- Better performance (no GUI overhead)
- No user configuration required - works automatically
- Based on user-recorded journal (journal_monitor_window_off.py)
Documentation:
- Updated docs/NX_MULTI_SOLUTION_PROTOCOL.md with solution monitor control section
- Added implementation details and when the feature activates
- Cross-referenced user's recorded journal
Implementation: optimization_engine/solve_simulation.py
Documentation: docs/NX_MULTI_SOLUTION_PROTOCOL.md
Reference: nx_journals/user_generated_journals/journal_monitor_window_off.py
Phase 1.3.1 Complete - Logging Integration:
1. Updated .claude/skills/create-study.md:
- Added IMPORTANT section on structured logging from Phase 1.3
- Documents logger import and initialization
- Lists all structured logging methods (trial_start, trial_complete, etc.)
- References drone_gimbal_arm as template
2. Created studies/uav_arm_optimization/:
- Multi-objective NSGA-II study (50 trials)
- Same type as drone_gimbal_arm but renamed for UAV context
- Full integration with Phase 1.3 logging system
- Configuration: minimize mass + maximize frequency
- Running to validate complete logging system
Benefits:
- All future studies created via skill will have consistent logging
- Production-ready error handling and file logging from day 1
- Color-coded console output for better monitoring
- Automatic log rotation (50MB, 3 backups)
Related: Phase 1.2 (Configuration), Phase 1.3 (Logger), Phase 1.3.1 (Integration)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Migrate drone_gimbal_arm study as reference implementation for Phase 1.3 logging system.
Changes:
- Replace all print() statements with logger calls throughout run_optimization.py
- Add logger.trial_start() and logger.trial_complete() for structured trial logging
- Use logger.trial_failed() for error handling with full tracebacks
- Add logger.study_start() and logger.study_complete() for lifecycle logging
- Replace constraint violation prints with logger.warning()
- Create comprehensive LOGGING_MIGRATION_GUIDE.md with before/after examples
Benefits:
- Color-coded console output (green INFO, yellow WARNING, red ERROR)
- Automatic file logging to 2_results/optimization.log with rotation (50MB, 3 backups)
- Structured format with timestamps for dashboard integration
- Professional error handling with exc_info=True
- Reference implementation for migrating remaining studies
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements JSON Schema validation for optimization configurations to ensure
consistency across all studies and prevent configuration errors.
Added:
- optimization_engine/schemas/optimization_config_schema.json
- Comprehensive schema for Protocol 10 & 11 configurations
- Validates objectives, constraints, design variables, simulation settings
- Enforces standard field names (goal, bounds, parameter, threshold)
- optimization_engine/config_manager.py
- ConfigManager class with schema validation
- CLI tool: python config_manager.py <config.json>
- Type-safe accessor methods for config elements
- Custom validations: bounds check, multi-objective consistency, location check
- optimization_engine/schemas/README.md
- Complete documentation of standard configuration format
- Validation examples and common error fixes
- Migration guidance for legacy configs
- docs/07_DEVELOPMENT/Phase_1_2_Implementation_Plan.md
- Detailed implementation plan for remaining Phase 1.2 tasks
- Migration tool design, integration guide, testing plan
Testing:
- Validated drone_gimbal_arm_optimization config successfully
- ConfigManager works with drone_gimbal format (new standard)
- Identifies legacy format issues in bracket studies
Standards Established:
- Configuration location: studies/{name}/1_setup/
- Objective direction: "goal" not "type"
- Design var bounds: "bounds": [min, max] not "min"/"max"
- Design var name: "parameter" not "name"
- Constraint threshold: "threshold" not "value"
Next Steps (Phase 1.2.1+):
- Config migration tool for legacy studies
- Integration with run_optimization.py
- Update create-study Claude skill with schema reference
- Migrate bracket studies to new format
Relates to: Phase 1.2 MVP Development Plan
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
- New create-study.md skill for complete study scaffolding
- Interactive discovery process for problem understanding
- Automated generation of all study infrastructure:
- optimization_config.json with protocol selection
- workflow_config.json for future intelligent workflows
- run_optimization.py with proper multi-objective/multi-solution support
- reset_study.py for database management
- README.md with comprehensive documentation
- NX_FILE_MODIFICATIONS_REQUIRED.md when needed
- Protocol selection guidance (Protocol 10 vs 11)
- Extractor mapping to centralized library
- Multi-solution workflow detection
- Dashboard integration instructions
- User interaction best practices with confirmation steps
- Common patterns and critical reminders
- Reference to existing studies as templates
Enables users to create complete, working optimization studies
from natural language descriptions with proper Claude-guided workflow.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
MAJOR ARCHITECTURE REFACTOR - Clean Study Folders
Problem Identified by User:
"My study folder is a mess, why? I want some order and real structure to develop
an insanely good engineering software that evolve with time."
- Every substudy was generating duplicate extractor code
- Study folders polluted with reusable library code (generated_extractors/, generated_hooks/)
- No code reuse across studies
- Not production-grade architecture
Solution - Centralized Library System:
Implemented smart library with signature-based deduplication:
- Core extractors in optimization_engine/extractors/
- Studies only store metadata (extractors_manifest.json)
- Clean separation: studies = data, core = code
Architecture:
BEFORE (BAD):
studies/my_study/
generated_extractors/ ❌ Code pollution!
extract_displacement.py
extract_von_mises_stress.py
generated_hooks/ ❌ Code pollution!
llm_workflow_config.json
results.json
AFTER (GOOD):
optimization_engine/extractors/ ✓ Core library
extract_displacement.py
extract_stress.py
catalog.json
studies/my_study/
extractors_manifest.json ✓ Just references!
llm_workflow_config.json ✓ Config
optimization_results.json ✓ Results
New Components:
1. ExtractorLibrary (extractor_library.py)
- Signature-based deduplication
- Centralized catalog (catalog.json)
- Study manifest generation
- Reusability across all studies
2. Updated ExtractorOrchestrator
- Uses core library instead of per-study generation
- Creates manifest instead of copying code
- Backward compatible (legacy mode available)
3. Updated LLMOptimizationRunner
- Removed generated_extractors/ directory creation
- Removed generated_hooks/ directory creation
- Uses core library exclusively
4. Updated Tests
- Verifies extractors_manifest.json exists
- Checks for clean study folder structure
- All 18/18 checks pass
Results:
Study folders NOW ONLY contain:
✓ extractors_manifest.json - references to core library
✓ llm_workflow_config.json - study configuration
✓ optimization_results.json - optimization results
✓ optimization_history.json - trial history
✓ .db file - Optuna database
Core library contains:
✓ extract_displacement.py - reusable across ALL studies
✓ extract_von_mises_stress.py - reusable across ALL studies
✓ extract_mass.py - reusable across ALL studies
✓ catalog.json - tracks all extractors with signatures
Benefits:
- Clean, professional study folder structure
- Code reuse eliminates duplication
- Library grows over time, studies stay clean
- Production-grade architecture
- "Insanely good engineering software that evolves with time"
Testing:
E2E test passes with clean folder structure
- No generated_extractors/ pollution
- Manifest correctly references library
- Core library populated with reusable extractors
- Study folder professional and minimal
Documentation:
- Added comprehensive architecture doc (docs/ARCHITECTURE_REFACTOR_NOV17.md)
- Includes migration guide
- Documents future work (hooks library, versioning, CLI tools)
Next Steps:
- Apply same architecture to hooks library
- Add auto-generated documentation for library
- Implement versioning for reproducibility
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
CRITICAL FIXES:
1. Parameter Range Parsing Bug
- LLM returns bounds as [min, max] array, but code was looking for 'min'/'max' keys
- This caused all parameters to default to 0-1 range instead of actual mm values
- Example: "20 to 30 mm" was being used as 0.2-1.0mm instead of 20-30mm
2. Missing Workflow Documentation
- Added automatic saving of LLM workflow config to output directory
- Creates llm_workflow_config.json with complete optimization setup
- Includes design variables, bounds, objectives, constraints, engineering features
Changes:
- optimization_engine/llm_optimization_runner.py:
* Lines 205-211: Parse 'bounds' array from LLM output
* Lines 80-84: Save workflow config JSON for transparency
* Maintains backward compatibility with old 'min'/'max' format
Test Results:
BEFORE:
- beam_half_core_thickness: 0.27-0.95mm (WRONG!)
- beam_face_thickness: 0.07-0.73mm (WRONG!)
AFTER:
- beam_half_core_thickness: 20.16-28.16mm (CORRECT!)
- beam_face_thickness: 21.69-24.73mm (CORRECT!)
E2E test now passes with realistic parameter values and proper documentation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
**CRITICAL FIX**: FEM results were identical across trials
**Root Cause**:
The LLM runner was passing design_vars to simulation_runner(), which then passed
them to NX Solver's expression_updates parameter. The solve journal tried to
update hardcoded expression names (tip_thickness, support_angle) that don't exist
in the beam model, causing the solver to ignore updates and use cached geometry.
**Solution**:
Match the working 50-trial optimization workflow:
1. model_updater() updates PRT file via NX import journal
2. Part file is closed/flushed to disk
3. simulation_runner() runs WITHOUT passing design_vars
4. NX solver loads SIM file, which references the updated PRT from disk
5. FEM regenerates with updated geometry automatically
**Changes**:
- llm_optimization_runner.py: Call simulation_runner() without arguments
- run_optimization.py: Remove design_vars parameter from simulation_runner closure
- import_expressions.py: Added theSession.Parts.CloseAll() to flush changes
- test_phase_3_2_e2e.py: Fixed remaining variable name bugs
**Test Results**:
✅ Trial 0: objective 7,315,679
✅ Trial 1: objective 9,158.67
✅ Trial 2: objective 7,655.28
FEM results are now DIFFERENT for each trial - optimization working correctly!
**Remaining Issue**: LLM parsing "20 to 30 mm" as 0-1 range (separate fix needed)
Critical bug fix for LLM mode optimization:
**Problem**:
- NXParameterUpdater.update_expressions() uses NX journal to import expressions (default use_nx_import=True)
- The NX journal directly updates the PRT file on disk and saves it
- But then run_optimization.py was calling updater.save() afterwards
- save() writes self.content (loaded at initialization) back to file
- This overwrote the NX journal changes with stale binary content!
**Result**: All optimization trials produced identical FEM results because the model was never actually updated.
**Fixes**:
1. Removed updater.save() call from model_updater closure in run_optimization.py
2. Added theSession.Parts.CloseAll() in import_expressions.py to ensure changes are flushed and file is released
3. Fixed test_phase_3_2_e2e.py variable name (best_trial_file → results_file)
**Testing**: Verified expressions persist to disk correctly with standalone test.
Next step: Address remaining issue where FEM results are still identical (likely solve journal not reloading updated PRT).
Added helper scripts to make running E2E tests easier:
1. .env.example - Template for API key storage
2. run_e2e_with_env.py - Loads API key from .env and runs E2E test
3. monitor_e2e.py - Real-time monitoring script for live output
4. run_e2e_test.bat - Windows batch script for easy execution
These scripts make it easy to:
- Store API key securely in .env (already in .gitignore)
- Run E2E test without manually setting environment variables
- Monitor test progress in real-time
Usage:
python run_e2e_with_env.py # Background execution
python monitor_e2e.py # Live output in terminal
API key is stored in .env (not committed to git) and automatically
loaded by helper scripts.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Created comprehensive roadmap for remaining Phase 3.2 work:
Week 1 Summary (COMPLETE):
- Task 1.2: LLMOptimizationRunner wired to production
- Task 1.3: Minimal example created
- All tests passing, documentation updated
Immediate Next Steps:
- Task 1.4: End-to-end integration test (2-4 hours)
Week 2 Plan - Robustness & Safety (16 hours):
- Code validation system (syntax, security, schema)
- Fallback mechanisms for all failure modes
- Comprehensive test suite (>80% coverage)
- Audit trail for generated code
Week 3 Plan - Learning System (20 hours):
- Template library with validated code patterns
- Knowledge base integration
- Success metrics and learning from patterns
Week 4 Plan - Documentation (12 hours):
- User guide for LLM mode
- Architecture documentation
- Demo video and presentation
Success Criteria:
- Production-ready LLM mode with safety validation
- Fallback mechanisms for robustness
- Learning system that improves over time
- Complete documentation for users
Known Gaps:
1. LLMWorkflowAnalyzer Claude Code integration (Phase 2.7)
2. Manual mode integration (lower priority)
Recommendations:
1. Complete Task 1.4 E2E test this week
2. Use API key for testing (don't block on Claude Code)
3. Prioritize safety (Week 2) before features
4. Build template library early (Week 3)
Overall Progress: 25% complete (1 week / 4 weeks)
Timeline: ON TRACK
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
