feat(seo): add basic SEO files for Google Search Console

app/index.html

@@ -4,8 +4,25 @@
     <meta charset="UTF-8" />
     <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <meta name="description" content="AI-powered Python plotting examples that work with YOUR data" />
-    <title>pyplots - AI-Powered Plotting Examples</title>
+    <meta name="description" content="library-agnostic, ai-powered python plotting examples. automatically generated, tested, and maintained." />
+    <title>pyplots.ai</title>
+
+    <!-- Canonical -->
+    <link rel="canonical" href="https://pyplots.ai/" />
+
+    <!-- Open Graph -->
+    <meta property="og:type" content="website" />
+    <meta property="og:url" content="https://pyplots.ai/" />
+    <meta property="og:title" content="pyplots.ai" />
+    <meta property="og:description" content="library-agnostic, ai-powered python plotting examples. automatically generated, tested, and maintained." />
+    <meta property="og:image" content="https://pyplots.ai/og-image.png" />
+    <meta property="og:site_name" content="pyplots.ai" />
+
+    <!-- Twitter Card -->
+    <meta name="twitter:card" content="summary_large_image" />
+    <meta name="twitter:title" content="pyplots.ai" />
+    <meta name="twitter:description" content="library-agnostic, ai-powered python plotting examples. automatically generated, tested, and maintained." />
+    <meta name="twitter:image" content="https://pyplots.ai/og-image.png" />
     <link rel="preconnect" href="https://fonts.googleapis.com">
     <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
     <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;600;700;800&display=swap" rel="stylesheet">

app/public/robots.txt

@@ -0,0 +1,4 @@
+User-agent: *
+Allow: /
+
+Sitemap: https://pyplots.ai/sitemap.xml

app/public/sitemap.xml

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
+  <url>
+    <loc>https://pyplots.ai/</loc>
+    <lastmod>2025-12-07</lastmod>
+    <changefreq>weekly</changefreq>
+    <priority>1.0</priority>
+  </url>
+</urlset>

plots/bokeh/scatter/scatter-color-groups/default.py

@@ -14,19 +14,25 @@
 np.random.seed(42)
 n_per_group = 50
 
-data = pd.DataFrame({
-    "sepal_length": np.concatenate([
-        np.random.normal(5.0, 0.35, n_per_group),
-        np.random.normal(5.9, 0.50, n_per_group),
-        np.random.normal(6.6, 0.60, n_per_group),
-    ]),
-    "sepal_width": np.concatenate([
-        np.random.normal(3.4, 0.38, n_per_group),
-        np.random.normal(2.8, 0.30, n_per_group),
-        np.random.normal(3.0, 0.30, n_per_group),
-    ]),
-    "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
-})
+data = pd.DataFrame(
+    {
+        "sepal_length": np.concatenate(
+            [
+                np.random.normal(5.0, 0.35, n_per_group),
+                np.random.normal(5.9, 0.50, n_per_group),
+                np.random.normal(6.6, 0.60, n_per_group),
+            ]
+        ),
+        "sepal_width": np.concatenate(
+            [
+                np.random.normal(3.4, 0.38, n_per_group),
+                np.random.normal(2.8, 0.30, n_per_group),
+                np.random.normal(3.0, 0.30, n_per_group),
+            ]
+        ),
+        "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
+    }
+)
 
 # Color palette (from style guide)
 colors = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6", "#F97316"]

plots/matplotlib/scatter/scatter-color-groups/default.py

@@ -12,19 +12,25 @@
 np.random.seed(42)
 n_per_group = 50
 
-data = pd.DataFrame({
-    "sepal_length": np.concatenate([
-        np.random.normal(5.0, 0.35, n_per_group),
-        np.random.normal(5.9, 0.50, n_per_group),
-        np.random.normal(6.6, 0.60, n_per_group),
-    ]),
-    "sepal_width": np.concatenate([
-        np.random.normal(3.4, 0.38, n_per_group),
-        np.random.normal(2.8, 0.30, n_per_group),
-        np.random.normal(3.0, 0.30, n_per_group),
-    ]),
-    "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
-})
+data = pd.DataFrame(
+    {
+        "sepal_length": np.concatenate(
+            [
+                np.random.normal(5.0, 0.35, n_per_group),
+                np.random.normal(5.9, 0.50, n_per_group),
+                np.random.normal(6.6, 0.60, n_per_group),
+            ]
+        ),
+        "sepal_width": np.concatenate(
+            [
+                np.random.normal(3.4, 0.38, n_per_group),
+                np.random.normal(2.8, 0.30, n_per_group),
+                np.random.normal(3.0, 0.30, n_per_group),
+            ]
+        ),
+        "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
+    }
+)
 
 # Color palette (colorblind safe from style guide)
 colors = ["#306998", "#FFD43B", "#DC2626"]

plots/plotnine/point/scatter-color-groups/default.py

@@ -12,19 +12,25 @@
 np.random.seed(42)
 n_per_group = 50
 
-data = pd.DataFrame({
-    "sepal_length": np.concatenate([
-        np.random.normal(5.0, 0.35, n_per_group),
-        np.random.normal(5.9, 0.50, n_per_group),
-        np.random.normal(6.6, 0.60, n_per_group),
-    ]),
-    "sepal_width": np.concatenate([
-        np.random.normal(3.4, 0.38, n_per_group),
-        np.random.normal(2.8, 0.30, n_per_group),
-        np.random.normal(3.0, 0.30, n_per_group),
-    ]),
-    "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
-})
+data = pd.DataFrame(
+    {
+        "sepal_length": np.concatenate(
+            [
+                np.random.normal(5.0, 0.35, n_per_group),
+                np.random.normal(5.9, 0.50, n_per_group),
+                np.random.normal(6.6, 0.60, n_per_group),
+            ]
+        ),
+        "sepal_width": np.concatenate(
+            [
+                np.random.normal(3.4, 0.38, n_per_group),
+                np.random.normal(2.8, 0.30, n_per_group),
+                np.random.normal(3.0, 0.30, n_per_group),
+            ]
+        ),
+        "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
+    }
+)
 
 # Color palette (from style guide)
 colors = ["#306998", "#FFD43B", "#DC2626"]

tests/unit/prompts/test_prompts.py

@@ -38,6 +38,7 @@
     "plotnine.md",
     "pygal.md",
     "highcharts.md",
+    "letsplot.md",
 ]
 
 
@@ -81,15 +82,21 @@ def quality_criteria_content(self) -> str:
         return (PROMPTS_DIR / "quality-criteria.md").read_text()
 
     def test_plot_generator_has_required_sections(self, plot_generator_content: str) -> None:
-        """Plot generator should have Role, Task, Rules sections."""
-        required_sections = ["## Role", "## Task", "## Rules", "## Output"]
+        """Plot generator should have Role, Task, Output sections."""
+        # Core sections at level 2
+        required_sections = ["## Role", "## Task", "## Output"]
         for section in required_sections:
             assert section in plot_generator_content, f"Missing section: {section}"
+        # Rules can be at level 2 or 3 (### Rules under ## Output)
+        assert "Rules" in plot_generator_content, "Missing Rules section"
 
     def test_plot_generator_has_code_template(self, plot_generator_content: str) -> None:
         """Plot generator should include a code template."""
         assert "```python" in plot_generator_content, "Missing Python code template"
-        assert "def create_plot" in plot_generator_content, "Missing create_plot function template"
+        # KISS style: simple scripts with comments, not functions
+        assert "# Create plot" in plot_generator_content or "plt.savefig" in plot_generator_content, (
+            "Missing plot creation example"
+        )
 
     def test_quality_criteria_has_scoring_section(self, quality_criteria_content: str) -> None:
         """Quality criteria should have scoring information."""
@@ -107,8 +114,9 @@ def test_library_prompt_has_required_sections(self, filename: str) -> None:
         content = (LIBRARY_PROMPTS_DIR / filename).read_text()
         library_name = filename.replace(".md", "")
 
-        # Check for header
-        assert f"# {library_name}" in content.lower(), f"Missing header for {library_name}"
+        # Check for header (normalize by removing hyphens for comparison)
+        content_normalized = content.lower().replace("-", "")
+        assert f"# {library_name}" in content_normalized, f"Missing header for {library_name}"
 
         # Check for import section
         assert "## Import" in content or "import" in content.lower(), f"Missing import section in {filename}"
@@ -117,12 +125,13 @@ def test_library_prompt_has_required_sections(self, filename: str) -> None:
         assert "```python" in content, f"Missing Python code examples in {filename}"
 
     @pytest.mark.parametrize("filename", EXPECTED_LIBRARY_PROMPTS)
-    def test_library_prompt_has_return_type(self, filename: str) -> None:
-        """Each library prompt should specify return type."""
+    def test_library_prompt_has_save_section(self, filename: str) -> None:
+        """Each library prompt should show how to save the plot."""
         content = (LIBRARY_PROMPTS_DIR / filename).read_text()
-        # Either explicit return type section or type hint in code
-        has_return_type = "## Return Type" in content or "-> " in content
-        assert has_return_type, f"Missing return type specification in {filename}"
+        # KISS style: prompts show how to save, not function return types
+        save_patterns = ["## Save", "savefig", "save(", "write_image", "save_screenshot", "export_png"]
+        has_save_info = any(pattern in content for pattern in save_patterns)
+        assert has_save_info, f"Missing save/output section in {filename}"
 
 
 class TestNoPlaceholders: