Auto-commit: v2.7 Series Continuity & Book Number Awareness

- story/planner.py: enrich() and plan_structure() now extract series_metadata
  and inject a SERIES_CONTEXT block (Book X of Y in series Z, with position-aware
  guidance) into prompts when is_series is true.
- story/writer.py: write_chapter() builds and injects the same SERIES_CONTEXT
  into the chapter draft prompt; passes series_context to evaluate_chapter_quality().
- story/editor.py: evaluate_chapter_quality() accepts optional series_context
  parameter and injects it into METADATA so arc pacing is evaluated relative to
  the book's position in the series.
- ai_blueprint.md: Section 11 marked complete (v2.7), summary updated.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

story/editor.py

@@ -5,7 +5,7 @@ from ai import models as ai_models
 from story.style_persona import get_style_guidelines
 
 
-def evaluate_chapter_quality(text, chapter_title, genre, model, folder):
+def evaluate_chapter_quality(text, chapter_title, genre, model, folder, series_context=""):
     guidelines = get_style_guidelines()
     ai_isms = "', '".join(guidelines['ai_isms'])
     fw_examples = ", ".join([f"'He {w}'" for w in guidelines['filter_words'][:5]])
@@ -15,13 +15,15 @@ def evaluate_chapter_quality(text, chapter_title, genre, model, folder):
     max_sugg = min_sugg + 2
     suggestion_range = f"{min_sugg}-{max_sugg}"
 
+    series_line = f"\n    - {series_context}" if series_context else ""
+
     prompt = f"""
     ROLE: Senior Literary Editor
     TASK: Critique chapter draft. Apply STRICT scoring — do not inflate scores.
 
     METADATA:
     - TITLE: {chapter_title}
-    - GENRE: {genre}
+    - GENRE: {genre}{series_line}
 
     PROHIBITED_PATTERNS:
     - AI_ISMS: {ai_isms}