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+# 🔄 AI-Powered Plot Generation Workflow
+
+## Overview
+
+This workflow describes the complete AI-driven process from identifying a new plot type to quality-assured implementation across multiple Python libraries and Python versions.
+
+## Main Workflow
+
+```mermaid
+graph TD
+    A[👤 Human/AI finds new plot type] --> B{Plot exists in specs?}
+    B -->|No| C[📝 Create spec from template]
+    B -->|Similar| D[🔍 Distance check against existing plots]
+    D -->|Too similar| E[❌ Reject: Duplicate]
+    D -->|Different enough| C
+    C --> F[🤖 Claude Code analyzes spec]
+    F --> G[📚 Library decision]
+    G --> H[💻 Code generation for all libraries]
+    H --> I[🧪 Automated testing]
+    I --> J{Tests passed?}
+    J -->|No| H
+    J -->|Yes| K[🎨 Preview generation]
+    K --> L[👁️ Multi-AI quality assessment]
+    L --> M{Median >= 85%?}
+    M -->|No| N[🔧 Code optimization by AI team]
+    N --> I
+    M -->|Yes| O[📊 Distance calculation]
+    O --> P[💾 Save to DB + GCS]
+    P --> Q[🚀 Deployment]
+    Q --> R[⏰ Scheduled optimization loop]
+    R --> S[🔄 Continuous improvement]
+    S --> I
+
+    style A fill:#e1f5ff
+    style C fill:#fff4e1
+    style F fill:#f0e1ff
+    style L fill:#ffe1e1
+    style O fill:#e1ffe1
+    style Q fill:#e1ffe1
+```
+
+## Detailed Process
+
+### 1. Spec Creation & Validation
+
+```mermaid
+graph LR
+    A[New plot type identified] --> B[Load spec template]
+    B --> C[Fill out spec]
+    C --> D{Complete?}
+    D -->|No| C
+    D -->|Yes| E[Distance check against all specs]
+    E --> F{Distance >= threshold?}
+    F -->|No| G[Human/AI decides]
+    F -->|Yes| H[Create specs/{spec-id}.md]
+    G -->|Reject| I[End]
+    G -->|Accept| H
+    H --> J[Git commit + push]
+```
+
+**Details:**
+- **Input**: Plot type name (e.g., "ROC Curve", "Violin Plot with Significance")
+- **Template**: `docs/spec-template.md`
+- **Distance check**: Cosine similarity on spec embeddings
+- **Threshold**: 0.3 (30% difference required)
+
+### 2. Library Analysis & Code Generation
+
+```mermaid
+graph TD
+    A[📄 New spec available] --> B[🤖 Claude Code agent starts]
+    B --> C[Analyze spec]
+    C --> D[Identify suitable libraries]
+    D --> E{For each library}
+    E --> F1[matplotlib implementation]
+    E --> F2[seaborn implementation]
+    E --> F3[plotly implementation]
+    E --> F4[bokeh implementation]
+
+    F1 --> G1[Generate default.py]
+    F2 --> G2[Generate default.py]
+    F3 --> G3[Generate default.py]
+    F4 --> G4[Generate default.py]
+
+    G1 --> H[Code review by 2nd AI]
+    G2 --> H
+    G3 --> H
+    G4 --> H
+
+    H --> I{Code quality OK?}
+    I -->|No| J[Feedback → Regeneration]
+    J --> E
+    I -->|Yes| K[Generate tests]
+    K --> L[Run pytest]
+```
+
+**Library Decision:**
+- matplotlib: Always (baseline)
+- seaborn: When statistical/categorical
+- plotly: When interactivity makes sense
+- bokeh: When streaming/dashboard
+- altair: When declarative is better
+- others: As needed
+
+### 3. Multi-AI Quality Assessment
+
+```mermaid
+graph TD
+    A[🎨 Preview generated] --> B[AI Reviewer #1]
+    A --> C[AI Reviewer #2]
+    A --> D[AI Reviewer #3]
+
+    B --> E1[Score: 0-100]
+    C --> E2[Score: 0-100]
+    D --> E3[Score: 0-100]
+
+    E1 --> F[Aggregate scores]
+    E2 --> F
+    E3 --> F
+
+    F --> G{Median >= 85?}
+    G -->|Yes| H{Variance <= 15?}
+    G -->|No| I[Collect majority feedback]
+    H -->|Yes| J[✅ Quality gate passed]
+    H -->|No| K[Initiate AI discussion]
+    K --> L[Find consensus]
+    L --> M{Agreement?}
+    M -->|Yes| J
+    M -->|No| I
+    I --> N[Request code optimization]
+    N --> O[Back to code generation]
+```
+
+**Quality Criteria Checklist:**
+
+Each AI reviewer checks:
+
+1. **Spec Compliance**
+   - ✓ All data requirements implemented
+   - ✓ Optional parameters work
+   - ✓ Expected output fulfilled
+
+2. **Visual Quality**
+   - ✓ No overlapping labels
+   - ✓ Axis labels complete and readable
+   - ✓ Legend present (if needed)
+   - ✓ Grid visible but not dominant
+   - ✓ Colors distinguishable (colorblind-safe)
+   - ✓ Appropriate figure size
+   - ✓ No text outside figure bounds
+   - ✓ Consistent font sizes
+
+3. **Technical Quality**
+   - ✓ Code runs without errors
+   - ✓ Correct data types used
+   - ✓ Edge cases handled (NaN, empty data)
+   - ✓ Acceptable performance (< 5s for 10k rows)
+
+4. **Library Best Practices**
+   - ✓ Idiomatic code for the library
+   - ✓ Recommended API methods used
+   - ✓ No deprecation warnings
+
+**AI Reviewer Configuration:**
+
+```python
+reviewers = [
+    {
+        "id": "reviewer_1",
+        "model": "claude-sonnet-4",
+        "temperature": 0.3,  # Strict
+        "focus": "visual_quality"
+    },
+    {
+        "id": "reviewer_2",
+        "model": "claude-sonnet-4",
+        "temperature": 0.5,  # Balanced
+        "focus": "spec_compliance"
+    },
+    {
+        "id": "reviewer_3",
+        "model": "claude-sonnet-4",
+        "temperature": 0.7,  # Creative
+        "focus": "user_experience"
+    }
+]
+```
+
+### 4. Automated Testing Pipeline
+
+```mermaid
+graph LR
+    A[Code generated] --> B[Build test matrix]
+    B --> C1[Python 3.10]
+    B --> C2[Python 3.11]
+    B --> C3[Python 3.12]
+
+    C1 --> D1[Run pytest]
+    C2 --> D2[Run pytest]
+    C3 --> D3[Run pytest]
+
+    D1 --> E1{Passed?}
+    D2 --> E2{Passed?}
+    D3 --> E3{Passed?}
+
+    E1 -->|Yes| F[Version 3.10 ✓]
+    E2 -->|Yes| G[Version 3.11 ✓]
+    E3 -->|Yes| H[Version 3.12 ✓]
+
+    E1 -->|No| I1[Fix for 3.10]
+    E2 -->|No| I2[Fix for 3.11]
+    E3 -->|No| I3[Fix for 3.12]
+
+    I1 --> J{Separate file?}
+    I2 --> J
+    I3 --> J
+
+    J -->|Yes| K[Create py310.py/py311.py]
+    J -->|No| L[Adapt default.py]
+
+    K --> D1
+    L --> D1
+
+    F --> M{All 3 OK?}
+    G --> M
+    H --> M
+
+    M -->|Yes| N[✅ Tests passed]
+    M -->|No| O[⚠️ Partial support]
+```
+
+**Test Requirements:**
+
+Each implementation must have tests for:
+- Basic functionality (happy path)
+- Edge cases (empty data, NaN, inf)
+- Different data shapes (few/many rows)
+- Optional parameters
+- Error handling
+
+**Example Test:**
+
+```python
+# tests/unit/plots/matplotlib/scatter/test_scatter_basic_001.py
+import pytest
+import pandas as pd
+import numpy as np
+from plots.matplotlib.scatter.scatter_basic_001.default import create_plot
+
+
+class TestScatterBasic001:
+    def test_basic_functionality(self):
+        """Test basic functionality"""
+        data = pd.DataFrame({
+            'x': [1, 2, 3, 4, 5],
+            'y': [2, 4, 6, 8, 10]
+        })
+        fig = create_plot(data, x='x', y='y')
+        assert fig is not None
+        assert len(fig.axes) == 1
+
+    def test_with_nan_values(self):
+        """Test with NaN values"""
+        data = pd.DataFrame({
+            'x': [1, 2, np.nan, 4, 5],
+            'y': [2, np.nan, 6, 8, 10]
+        })
+        fig = create_plot(data, x='x', y='y')
+        assert fig is not None
+
+    def test_empty_data(self):
+        """Test with empty data"""
+        data = pd.DataFrame({'x': [], 'y': []})
+        with pytest.raises(ValueError):
+            create_plot(data, x='x', y='y')
+
+    def test_large_dataset(self):
+        """Test with large dataset"""
+        data = pd.DataFrame({
+            'x': np.random.randn(10000),
+            'y': np.random.randn(10000)
+        })
+        import time
+        start = time.time()
+        fig = create_plot(data, x='x', y='y')
+        duration = time.time() - start
+        assert duration < 5.0  # Max 5 seconds
+
+    @pytest.mark.parametrize("python_version", ["3.10", "3.11", "3.12"])
+    def test_python_versions(self, python_version):
+        """Test across Python versions"""
+        # Run in CI with different Python versions
+        data = pd.DataFrame({
+            'x': [1, 2, 3],
+            'y': [2, 4, 6]
+        })
+        fig = create_plot(data, x='x', y='y')
+        assert fig is not None
+```
+
+### 5. Distance Calculation & Clustering
+
+```mermaid
+graph TD
+    A[Plot successfully created] --> B[Generate embeddings]
+    B --> C1[Spec text embedding]
+    B --> C2[Preview image embedding]
+    B --> C3[Code structure embedding]
+
+    C1 --> D[Combine embeddings]
+    C2 --> D
+    C3 --> D
+
+    D --> E[Calculate distance to all plots]
+    E --> F[Update clustering]
+    F --> G[Save cluster metadata]
+    G --> H[Update recommendations]
+```
+
+**Distance Metrics:**
+
+1. **Semantic Distance** (Spec Text)
+   - Embedding: Claude text embeddings
+   - Metric: Cosine similarity
+   - Weight: 40%
+
+2. **Visual Distance** (Preview Image)
+   - Embedding: CLIP image embeddings
+   - Metric: Euclidean distance
+   - Weight: 30%
+
+3. **Structural Distance** (Code)
+   - Features: AST analysis, API calls
+   - Metric: Jaccard similarity
+   - Weight: 30%
+
+**Composite Distance:**
+```python
+def calculate_distance(plot1, plot2):
+    semantic_dist = cosine_distance(plot1.spec_embedding, plot2.spec_embedding)
+    visual_dist = euclidean_distance(plot1.image_embedding, plot2.image_embedding)
+    structural_dist = jaccard_distance(plot1.code_features, plot2.code_features)
+
+    return (
+        0.4 * semantic_dist +
+        0.3 * visual_dist +
+        0.3 * structural_dist
+    )
+```
+
+### 6. Continuous Optimization Loop
+
+```mermaid
+graph TD
+    A[⏰ Scheduled trigger: daily] --> B[Load plots with score < 90]
+    B --> C{For each plot}
+
+    C --> D[AI Optimizer #1]
+    C --> E[AI Optimizer #2]
+    C --> F[AI Optimizer #3]
+
+    D --> G1[Optimization proposal 1]
+    E --> G2[Optimization proposal 2]
+    F --> G3[Optimization proposal 3]
+
+    G1 --> H[Generate code variants]
+    G2 --> H
+    G3 --> H
+
+    H --> I[Test all variants]
+    I --> J[Quality assessment for all]
+
+    J --> K{Best variant better?}
+    K -->|Yes| L[Get AI consensus]
+    K -->|No| M[No change]
+
+    L --> N{Majority agrees?}
+    N -->|Yes| O[Replace code]
+    N -->|No| M
+
+    O --> P[Regenerate preview]
+    P --> Q[Update quality score]
+    Q --> R[Git commit]
+
+    M --> S[Next plot]
+    R --> S
+    S --> C
+```
+
+**Optimization Strategies:**
+
+1. **Visual Refinement**
+   - Better color palettes
+   - Optimized label positions
+   - Improved font sizes
+   - Margin optimization
+
+2. **Code Quality**
+   - More idiomatic code
+   - Better performance
+   - Clearer variable names
+   - More detailed docstrings
+
+3. **Feature Enhancement**
+   - Additional optional parameters
+   - Better error handling
+   - More flexibility
+
+**AI Optimizer Configuration:**
+
+```python
+optimizers = [
+    {
+        "id": "optimizer_visual",
+        "model": "claude-sonnet-4",
+        "temperature": 0.6,
+        "focus": "visual_improvements",
+        "max_changes": 3  # Max 3 changes per iteration
+    },
+    {
+        "id": "optimizer_code",
+        "model": "claude-sonnet-4",
+        "temperature": 0.4,
+        "focus": "code_quality",
+        "max_changes": 5
+    },
+    {
+        "id": "optimizer_features",
+        "model": "claude-sonnet-4",
+        "temperature": 0.7,
+        "focus": "feature_enhancement",
+        "max_changes": 2
+    }
+]
+```
+
+**Consensus Mechanism:**
+
+```python
+def optimize_plot(spec_id: str, library: str):
+    """Optimizes a plot through AI consensus"""
+
+    # 1. Load current version
+    current_code = load_implementation(spec_id, library)
+    current_score = get_quality_score(spec_id, library)
+
+    # 2. Collect optimization proposals
+    proposals = []
+    for optimizer in optimizers:
+        proposal = optimizer.suggest_improvements(current_code)
+        proposals.append(proposal)
+
+    # 3. Implement and test all proposals
+    variants = []
+    for proposal in proposals:
+        code = apply_changes(current_code, proposal.changes)
+        if passes_tests(code):
+            preview = generate_preview(code)
+            score = quality_assessment(preview)
+            variants.append({
+                'code': code,
+                'score': score,
+                'proposal': proposal
+            })
+
+    # 4. Find best variant
+    best = max(variants, key=lambda v: v['score'])
+
+    # 5. Is it better?
+    if best['score'] <= current_score:
+        return False  # No improvement
+
+    # 6. Get consensus
+    votes = []
+    for reviewer in reviewers:
+        vote = reviewer.compare(current_code, best['code'])
+        votes.append(vote)  # True = improvement, False = no improvement
+
+    # 7. Majority decision
+    if sum(votes) > len(votes) / 2:
+        # Majority says: improvement!
+        save_implementation(spec_id, library, best['code'])
+        return True
+
+    return False
+```
+
+## Quality Assurance
+
+### Quality Gates
+
+```mermaid
+graph LR
+    A[Code generated] --> B{Pytest}
+    B -->|Pass| C{Coverage >= 90%?}
+    B -->|Fail| Z[❌ Reject]
+    C -->|Yes| D{Ruff linting}
+    C -->|No| Z
+    D -->|Pass| E{Type checking}
+    D -->|Fail| Z
+    E -->|Pass| F{Visual quality >= 85?}
+    E -->|Fail| Z
+    F -->|Yes| G{AI consensus}
+    F -->|No| Z
+    G -->|Yes| H{Python 3.10-3.12}
+    G -->|No| Z
+    H -->|All pass| I[✅ Accept]
+    H -->|Some fail| J[⚠️ Partial]
+```
+
+### Quality Report Format
+
+```json
+{
+  "spec_id": "scatter-basic-001",
+  "library": "matplotlib",
+  "variant": "default",
+  "timestamp": "2025-11-19T10:30:00Z",
+
+  "tests": {
+    "pytest_passed": true,
+    "coverage": 94.2,
+    "python_versions": {
+      "3.10": "pass",
+      "3.11": "pass",
+      "3.12": "pass"
+    }
+  },
+
+  "linting": {
+    "ruff_errors": 0,
+    "ruff_warnings": 0,
+    "type_errors": 0
+  },
+
+  "visual_quality": {
+    "scores": [87, 92, 89],
+    "median": 89,
+    "variance": 6.3,
+    "consensus": true
+  },
+
+  "criteria": {
+    "axes_labeled": true,
+    "grid_visible": true,
+    "no_overlap": true,
+    "readable_text": true,
+    "legend_present": true,
+    "colorblind_safe": true,
+    "appropriate_size": true
+  },
+
+  "distance": {
+    "nearest_plot": "scatter-advanced-005",
+    "distance": 0.42,
+    "cluster_id": "scatter_plots_basic"
+  },
+
+  "overall_score": 89.5,
+  "status": "approved",
+
+  "reviewers": [
+    {
+      "id": "reviewer_1",
+      "score": 87,
+      "feedback": "Good plot, minor margin improvements possible"
+    },
+    {
+      "id": "reviewer_2",
+      "score": 92,
+      "feedback": "Excellent spec compliance, all criteria met"
+    },
+    {
+      "id": "reviewer_3",
+      "score": 89,
+      "feedback": "Great user experience, intuitive API"
+    }
+  ]
+}
+```
+
+## Deployment Pipeline
+
+```mermaid
+graph LR
+    A[Quality gates passed] --> B[Preview to GCS]
+    B --> C[Metadata to PostgreSQL]
+    C --> D[Git commit + push]
+    D --> E[CI/CD triggered]
+    E --> F[Build container]
+    F --> G[Deploy to Cloud Run]
+    G --> H[Health check]
+    H --> I{Healthy?}
+    I -->|Yes| J[Redirect traffic]
+    I -->|No| K[Rollback]
+    J --> L[✅ Live]
+```
+
+## Monitoring & Analytics
+
+### Key Metrics
+
+1. **Quality Metrics**
+   - Average quality score per library
+   - Number of plots per quality range (< 85, 85-90, > 90)
+   - Consensus rate in AI reviews
+   - Optimization success rate
+
+2. **Performance Metrics**
+   - Time from spec to deployment
+   - Test execution time
+   - Preview generation time
+   - API response times
+
+3. **Coverage Metrics**
+   - Number of specs
+   - Number of implementations per spec
+   - Python version coverage
+   - Library coverage
+
+4. **Usage Metrics**
+   - Most popular plots
+   - Most frequent library choice
+   - Plot distance distribution
+   - Cluster sizes
+
+## Summary
+
+This workflow ensures:
+
+✅ **Fully automated** code generation and testing
+✅ **Multi-AI consensus** for objective quality assessment
+✅ **Continuous optimization** through scheduled AI reviews
+✅ **Multi-version support** for Python 3.10-3.12
+✅ **Intelligent clustering** for better discoverability
+✅ **High quality standards** through multi-layered gates
+✅ **Reproducible results** through clear processes
+
+The entire process is **AI-first**, **specification-driven**, and **quality-focused**.