feat: Add DevLoop automation and HTML Reports

## DevLoop - Closed-Loop Development System
- Orchestrator for plan → build → test → analyze cycle
- Gemini planning via OpenCode CLI
- Claude implementation via CLI bridge
- Playwright browser testing integration
- Test runner with API, filesystem, and browser tests
- Persistent state in .devloop/ directory
- CLI tool: tools/devloop_cli.py

Usage:
  python tools/devloop_cli.py start 'Create new feature'
  python tools/devloop_cli.py plan 'Fix bug in X'
  python tools/devloop_cli.py test --study support_arm
  python tools/devloop_cli.py browser --level full

## HTML Reports (optimization_engine/reporting/)
- Interactive Plotly-based reports
- Convergence plot, Pareto front, parallel coordinates
- Parameter importance analysis
- Self-contained HTML (offline-capable)
- Tailwind CSS styling

## Playwright E2E Tests
- Home page tests
- Test results in test-results/

## LAC Knowledge Base Updates
- Session insights (failures, workarounds, patterns)
- Optimization memory for arm support study

knowledge_base/lac/session_insights/success_pattern.jsonl

@@ -9,3 +9,12 @@
 {"timestamp": "2025-12-29T09:47:47.612485", "category": "success_pattern", "context": "Disk space optimization for FEA studies", "insight": "Per-trial FEA files are ~150MB but only OP2+JSON (~70MB) are essential. PRT/FEM/SIM/DAT are copies of master files and can be deleted after study completion. Archive to dalidou server for long-term storage.", "confidence": 0.95, "tags": ["disk_optimization", "archival", "study_management", "dalidou"], "related_files": ["optimization_engine/utils/study_archiver.py", "docs/protocols/operations/OP_07_DISK_OPTIMIZATION.md"]}
 {"timestamp": "2026-01-02T14:30:00", "category": "success_pattern", "context": "Study Interview Mode implementation and routing update", "insight": "STUDY CREATION DEFAULT: Interview Mode is now the DEFAULT for all study creation requests. Triggers: create a study, new study, set up study, optimize this, minimize mass - any study creation intent. Benefits: (1) Material-aware validation checks stress vs yield, (2) Anti-pattern detection warns about mass-no-constraint, (3) Auto extractor mapping E1-E10, (4) State persistence for interrupted sessions, (5) Blueprint generation with full validation. Skip with: skip interview, quick setup, manual config. Implementation: optimization_engine/interview/ with StudyInterviewEngine, QuestionEngine, EngineeringValidator, StudyBlueprint. All 129 tests passing.", "confidence": 1.0, "tags": ["interview_mode", "study_creation", "default", "validation", "anti_pattern", "materials"], "related_files": [".claude/skills/modules/study-interview-mode.md", "docs/protocols/operations/OP_01_CREATE_STUDY.md", "optimization_engine/interview/study_interview.py"]}
 {"timestamp": "2026-01-02T14:45:00", "category": "success_pattern", "context": "Study Interview Mode implementation complete", "insight": "INTERVIEW MODE DEFAULT: Study creation now uses Interview Mode by default for all study creation requests. This is a major usability improvement. Triggers: create a study, new study, set up, optimize this - any study creation intent. Key features: (1) Material-aware validation with 12 materials and fuzzy name matching, (2) Anti-pattern detection for 12 common mistakes, (3) Auto extractor mapping E1-E24, (4) 7-phase interview flow, (5) State persistence for interrupted sessions, (6) Blueprint validation before generation. Skip with: skip interview, quick setup, manual. Implementation in optimization_engine/interview/ with 129 tests passing. Full documentation in: .claude/skills/modules/study-interview-mode.md, docs/protocols/operations/OP_01_CREATE_STUDY.md", "confidence": 1.0, "tags": ["interview_mode", "study_creation", "default", "usability", "materials", "anti_pattern", "validation"], "related_files": [".claude/skills/modules/study-interview-mode.md", "docs/protocols/operations/OP_01_CREATE_STUDY.md", "optimization_engine/interview/"]}
+{"timestamp": "2026-01-22T13:00:00", "category": "success_pattern", "context": "DevLoop closed-loop development system implementation", "insight": "DEVLOOP PATTERN: Implemented autonomous development cycle that coordinates Gemini (planning) + Claude Code (implementation) + Dashboard (testing) + LAC (learning). 7-stage loop: PLAN -> BUILD -> TEST -> ANALYZE -> FIX -> VERIFY -> LOOP. Key components: (1) DevLoopOrchestrator in optimization_engine/devloop/, (2) DashboardTestRunner for automated testing, (3) GeminiPlanner for strategic planning with mock fallback, (4) ClaudeCodeBridge for implementation, (5) ProblemAnalyzer for failure analysis. API at /api/devloop/* with WebSocket for real-time updates. CLI tool at tools/devloop_cli.py. Frontend panel DevLoopPanel.tsx. Test with: python tools/devloop_cli.py test --study support_arm", "confidence": 0.95, "tags": ["devloop", "automation", "testing", "gemini", "claude", "dashboard", "closed-loop"], "related_files": ["optimization_engine/devloop/orchestrator.py", "tools/devloop_cli.py", "docs/guides/DEVLOOP.md"]}
+{"timestamp": "2026-01-22T13:37:05.355957", "category": "success_pattern", "context": "Extracting mass from Nastran BDF files", "insight": "Use BDFMassExtractor from bdf_mass_extractor.py for reliable mass extraction. It uses elem.Mass() which handles unit conversions properly. The simpler extract_mass_from_bdf.py now wraps this.", "confidence": 0.9, "tags": ["mass", "bdf", "extraction", "pyNastran"]}
+{"timestamp": "2026-01-22T13:47:38.696196", "category": "success_pattern", "context": "Stress extraction from NX Nastran OP2 files", "insight": "pyNastran returns stress in kPa for NX kg-mm-s unit system. Divide by 1000 to get MPa. Must check ALL solid element types (CTETRA, CHEXA, CPENTA, CPYRAM) to find true max. Elemental Nodal gives peak stress (143.5 MPa), Elemental Centroid gives averaged (100.3 MPa).", "confidence": 0.95, "tags": ["stress", "extraction", "units", "pyNastran", "nastran"]}
+{"timestamp": "2026-01-22T15:12:01.584128", "category": "success_pattern", "context": "Dashboard study discovery", "insight": "Dashboard now supports atomizer_spec.json as primary config. Updated _load_study_info() in optimization.py to check atomizer_spec.json first, then fall back to optimization_config.json. Studies with atomizer_spec.json are now discoverable.", "confidence": 0.9, "tags": ["dashboard", "atomizer_spec", "config", "v2.0"]}
+{"timestamp": "2026-01-22T15:12:01.584128", "category": "success_pattern", "context": "Extracting stress from NX Nastran results", "insight": "CONFIRMED: pyNastran returns stress in kPa for NX kg-mm-s unit system. Divide by 1000 for MPa. Must check ALL solid types (CTETRA, CHEXA, CPENTA, CPYRAM) - CHEXA often has highest stress. Elemental Nodal (143.5 MPa) vs Elemental Centroid (100.3 MPa) - use Nodal for conservative peak stress.", "confidence": 1.0, "tags": ["stress", "extraction", "units", "nastran", "verified"]}
+{"timestamp": "2026-01-22T15:23:37.040324", "category": "success_pattern", "context": "Creating new study with DevLoop workflow", "insight": "DevLoop workflow: plan -> create dirs -> copy models -> atomizer_spec.json -> validate canvas -> run_optimization.py -> devloop test -> FEA validation. 8 steps completed for support_arm_lightweight.", "confidence": 0.95, "tags": ["devloop", "workflow", "study_creation", "success"]}
+{"timestamp": "2026-01-22T15:23:37.040324", "category": "success_pattern", "context": "Single-objective optimization with constraints", "insight": "Single-objective with constraints: one objective in array, constraints use threshold+operator, penalty in objective function, canvas edges ext->obj for objective, ext->con for constraints.", "confidence": 0.9, "tags": ["optimization", "single_objective", "constraints", "canvas"]}
+{"timestamp": "2026-01-22T16:15:11.449264", "category": "success_pattern", "context": "Atomizer UX System implementation - January 2026", "insight": "New study workflow: (1) Put files in studies/_inbox/project_name/models/, (2) Optionally add intake.yaml and context/goals.md, (3) Run atomizer intake project_name, (4) Run atomizer gate study_name to validate with test trials, (5) If passed, approve with --approve flag, (6) Run optimization, (7) Run atomizer finalize study_name to generate interactive HTML report. The CLI commands are: intake, gate, list, finalize.", "confidence": 1.0, "tags": ["workflow", "ux", "cli", "intake", "validation", "report"]}
+{"timestamp": "2026-01-22T21:10:37.956764", "category": "success_pattern", "context": "Stage 3 arm study setup and execution with DevLoop", "insight": "DevLoop test command (devloop_cli.py test --study) successfully validated study setup before optimization. The 5 standard tests (directory, spec JSON, README, run_optimization.py, model dir) caught structure issues early. Full workflow: (1) Copy model files, (2) Create atomizer_spec.json with extractors/objectives/constraints, (3) Create run_optimization.py from template, (4) Create README.md, (5) Run DevLoop tests, (6) Execute optimization.", "confidence": 0.95, "tags": ["devloop", "study_creation", "workflow", "testing"]}