spec: add ternary-basic specification

plots/ternary-basic/specification.md

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+# ternary-basic: Basic Ternary Plot
+
+## Description
+
+A ternary plot displays three-component compositional data on an equilateral triangle where each vertex represents 100% of one component. Points inside the triangle show compositions that sum to a constant total (usually 100%), with position indicating relative proportions. This visualization is essential for data where three variables are interdependent and constrained to sum to a fixed value.
+
+## Applications
+
+- Analyzing chemical or material compositions (e.g., alloy mixtures, cement formulations)
+- Soil classification using sand, silt, and clay proportions
+- Visualizing market share distribution among three competitors
+- Studying color mixing with RGB or other three-component systems
+
+## Data
+
+- `component_a` (numeric) - Proportion of first component (0-100%)
+- `component_b` (numeric) - Proportion of second component (0-100%)
+- `component_c` (numeric) - Proportion of third component (0-100%)
+- Size: 20-200 points work well; larger datasets may need density visualization
+- Constraint: All three components must sum to 100% (or a constant total)
+
+## Notes
+
+- Grid lines should be drawn at regular intervals (typically 10% or 20%)
+- Each vertex should be clearly labeled with component names
+- Points should be visually distinct with appropriate marker size and color
+- Consider adding tick marks along each edge to aid reading proportions

plots/ternary-basic/specification.yaml

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+# Specification-level metadata for ternary-basic
+# Auto-synced to PostgreSQL on push to main
+
+spec_id: ternary-basic
+title: Basic Ternary Plot
+
+# Specification tracking
+created: 2025-12-15T20:44:43Z
+issue: 1001
+suggested: MarkusNeusinger
+history: []
+
+# Classification tags (applies to all library implementations)
+tags:
+  plot_type:
+    - ternary
+    - triangle
+    - compositional
+  domain:
+    - science
+    - chemistry
+    - geology
+    - general
+  features:
+    - basic
+    - 2d
+    - proportional
+  audience:
+    - intermediate
+  data_type:
+    - numeric
+    - continuous
+    - compositional