feat: add implementation-level tags for code reviews

- Introduced impl_tags to describe implementation details
- Updated workflows and scripts to handle new impl_tags structure
- Enhanced filtering and counting mechanisms for impl-level tags

Author: MarkusNeusinger

.github/workflows/impl-review.yml

@@ -144,15 +144,17 @@ jobs:
 
             3. **Read library rules**: `prompts/library/${{ steps.pr.outputs.library }}.md`
 
-            4. **MANDATORY: View the plot image**
+            4. **Read impl-tags guide**: `prompts/impl-tags-generator.md` (for step 8)
+
+            5. **MANDATORY: View the plot image**
                - You MUST use the Read tool to open `plot_images/plot.png`
                - Visually analyze the image - this is critical for the review
                - DO NOT skip this step - a review without seeing the image is invalid
                - If the image cannot be read, STOP and report the error
 
-            5. **Evaluate against quality criteria** from `prompts/quality-criteria.md`
+            6. **Evaluate against quality criteria** from `prompts/quality-criteria.md`
 
-            6. **Post verdict as PR comment** on PR #${{ steps.pr.outputs.pr_number }}:
+            7. **Post verdict as PR comment** on PR #${{ steps.pr.outputs.pr_number }}:
 
             ```markdown
             ## AI Review - Attempt ${{ steps.attempts.outputs.display }}/3
@@ -199,7 +201,7 @@ jobs:
             ### Verdict: APPROVED / REJECTED
             ```
 
-            7. **Save review data to files** (for the workflow to parse):
+            8. **Save review data to files** (for the workflow to parse):
                ```bash
                echo "XX" > quality_score.txt
 
@@ -235,7 +237,27 @@ jobs:
                EOF
                ```
 
-            8. **DO NOT add ai-approved or ai-rejected labels** - the workflow will add them after updating metadata.
+            9. **Generate impl_tags** (based on prompts/impl-tags-generator.md):
+               Analyze the implementation code and create impl_tags with 5 dimensions:
+               - `dependencies`: External packages beyond numpy/pandas/plotting library
+               - `techniques`: Visualization techniques (twin-axes, colorbar, etc.)
+               - `patterns`: Code patterns (data-generation, iteration-over-groups, etc.)
+               - `dataprep`: Data transformations (kde, binning, correlation-matrix, etc.)
+               - `styling`: Visual style (publication-ready, alpha-blending, etc.)
+
+               ```bash
+               cat > review_impl_tags.json << 'EOF'
+               {
+                 "dependencies": [],
+                 "techniques": ["colorbar", "annotations"],
+                 "patterns": ["data-generation"],
+                 "dataprep": [],
+                 "styling": ["publication-ready"]
+               }
+               EOF
+               ```
+
+            10. **DO NOT add ai-approved or ai-rejected labels** - the workflow will add them after updating metadata.
 
             **IMPORTANT**: Your review MUST include the "Image Description" section. A review without an image description will be considered invalid.
             **IMPORTANT**: All review data (strengths, weaknesses, image_description, criteria_checklist) is saved to metadata for future regeneration. Be specific!
@@ -314,6 +336,7 @@ jobs:
           image_description = None
           criteria_checklist = None
           verdict = None
+          impl_tags = None
 
           if Path('review_strengths.json').exists():
               try:
@@ -350,6 +373,13 @@ jobs:
               except:
                   pass
 
+          if Path('review_impl_tags.json').exists():
+              try:
+                  with open('review_impl_tags.json') as f:
+                      impl_tags = json.load(f)
+              except:
+                  pass
+
           # Load existing metadata
           with open(metadata_file, 'r') as f:
               data = yaml.safe_load(f)
@@ -372,6 +402,10 @@ jobs:
           if verdict:
               data['review']['verdict'] = verdict
 
+          # Add impl_tags (issue #2434)
+          if impl_tags:
+              data['impl_tags'] = impl_tags
+
           def str_representer(dumper, data):
               if isinstance(data, str) and data.endswith('Z') and 'T' in data:
                   return dumper.represent_scalar('tag:yaml.org,2002:str', data, style="'")

.github/workflows/spec-create.yml

@@ -117,7 +117,7 @@ jobs:
             6. **Create specification files:**
                - Read template: `prompts/templates/specification.md`
                - Read metadata template: `prompts/templates/specification.yaml`
-               - Read tagging guide: `docs/reference/tagging-system.md`
+               - Read tagging guide: `prompts/spec-tags-generator.md`
                - Create directory: `plots/{specification-id}/`
                - Create: `plots/{specification-id}/specification.md` (follow template structure)
                - Create: `plots/{specification-id}/specification.yaml` with:
@@ -213,7 +213,7 @@ jobs:
             6. **Create specification files:**
                - Read template: `prompts/templates/specification.md`
                - Read metadata template: `prompts/templates/specification.yaml`
-               - Read tagging guide: `docs/reference/tagging-system.md`
+               - Read tagging guide: `prompts/spec-tags-generator.md`
                - Create directory: `plots/{specification-id}/`
                - Create: `plots/{specification-id}/specification.md` (follow template structure)
                - Create: `plots/{specification-id}/specification.yaml` with:

CLAUDE.md

@@ -311,6 +311,14 @@ preview_html: null
 # Quality
 quality_score: 92
 
+# Implementation-level tags (describes HOW the code implements the plot)
+impl_tags:
+  dependencies: []  # External packages (scipy, sklearn, etc.)
+  techniques: [colorbar, annotations]  # Visualization techniques
+  patterns: [data-generation]  # Code patterns
+  dataprep: []  # Data transformations
+  styling: [publication-ready]  # Visual style
+
 # Review feedback (used for regeneration)
 review:
   # AI's visual description of the generated plot
@@ -364,8 +372,8 @@ Quality: 92/100 | Created: 2025-01-10
 - **Review feedback** stored in metadata for regeneration (AI reads previous feedback to improve)
 - **Extended review data**: `image_description`, `criteria_checklist`, and `verdict` for targeted fixes
 - Contributors credited via `suggested` field
-- Tags are at spec level (same for all libraries)
-- Per-library metadata updated automatically by `impl-review.yml` (quality score, review feedback)
+- **Two-level tagging**: Spec-level `tags` describe WHAT is visualized (same for all libraries), impl-level `impl_tags` describe HOW code implements it (per-library)
+- Per-library metadata updated automatically by `impl-review.yml` (quality score, review feedback, impl_tags)
 - `sync-postgres.yml` workflow syncs to database on push to main
 - Database stores full spec content (markdown) and implementation code (Python source)
 
@@ -473,7 +481,7 @@ uv run python -c "from core.database import is_db_configured; print(is_db_config
 - Spec content (full markdown from specification.md)
 - Spec metadata (title, description, tags, structured_tags from specification.yaml)
 - Implementation code (full Python source)
-- Implementation metadata (library, variant, quality score, generation info from metadata/*.yaml)
+- Implementation metadata (library, variant, quality score, impl_tags, generation info from metadata/*.yaml)
 - GCS URLs for preview images
 
 **What's in Repository** (source of truth):
@@ -512,6 +520,8 @@ The `prompts/` directory contains AI agent prompts for code generation, quality
 | `quality-evaluator.md` | AI quality evaluation prompt |
 | `spec-validator.md` | Validates plot request issues |
 | `spec-id-generator.md` | Assigns unique spec IDs |
+| `spec-tags-generator.md` | AI rules for spec-level tag assignment |
+| `impl-tags-generator.md` | AI rules for impl-level tag assignment |
 
 ### Using Prompts
 

alembic/versions/a2f4b8c91d23_add_impl_tags.py

@@ -0,0 +1,42 @@
+"""add_impl_tags
+
+Add impl_tags JSONB column to impls table for issue #2434:
+Implementation-level tags describing HOW code is implemented.
+5 dimensions: dependencies, techniques, patterns, dataprep, styling
+
+Revision ID: a2f4b8c91d23
+Revises: 6345896e2e90
+Create Date: 2026-01-07
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision: str = "a2f4b8c91d23"
+down_revision: Union[str, None] = "6345896e2e90"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Add impl_tags JSONB column and GIN index to impls table."""
+    # Add impl_tags column (JSONB for 5 tag dimensions)
+    op.add_column("impls", sa.Column("impl_tags", postgresql.JSONB(), nullable=True))
+
+    # GIN index for fast JSONB containment/existence queries
+    # Enables efficient filtering by any impl_tags dimension:
+    # - dependencies, techniques, patterns, dataprep, styling
+    op.execute("CREATE INDEX ix_impls_impl_tags ON impls USING GIN (impl_tags)")
+
+
+def downgrade() -> None:
+    """Remove impl_tags column and GIN index from impls table."""
+    op.execute("DROP INDEX ix_impls_impl_tags")
+    op.drop_column("impls", "impl_tags")