feat: Implement ai_blueprint.md action plan — architectural review & optimisations

Steps 1–7 of the ai_blueprint.md action plan executed:

DOCUMENTATION (Steps 1–3, 6–7):
- docs/current_state_analysis.md: Phase-by-phase cost/quality mapping of existing pipeline
- docs/alternatives_analysis.md: 15 alternative approaches with testable hypotheses
- docs/experiment_design.md: 7 controlled A/B experiment specifications (CPC, HQS, CER metrics)
- ai_blueprint_v2.md: New recommended architecture with cost projections and experiment roadmap

CODE IMPROVEMENTS (Step 4 — Experiments 1–4 implemented):
- story/writer.py: Extract build_persona_info() — persona loaded once per book, not per chapter
- story/writer.py: Adaptive scoring thresholds — SCORE_PASSING scales 6.5→7.5 by chapter position
- story/writer.py: Beat expansion skip — if beats >100 words, skip Director's Treatment expansion
- story/planner.py: validate_outline() — pre-generation gate checks missing beats, continuity, pacing
- story/planner.py: Enrichment field validation — warn on missing title/genre after enrich()
- cli/engine.py: Wire persona cache, outline validation gate, chapter_position threading

Expected savings: ~285K tokens per 30-chapter novel (~7% cost reduction)

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

README.md

@@ -115,6 +115,10 @@ Open `http://localhost:5000`.
 - **Dynamic Pacing:** Monitors story progress during writing and inserts bridge chapters to slow a rushing plot or removes redundant ones detected mid-stream — without restarting.
 - **Series Continuity:** When generating Book 2+, carries forward character visual tracking, established relationships, plot threads, and a cumulative "Story So Far" summary.
 - **Persona Refinement Loop:** Every 5 chapters, analyzes actual written text to refine the author persona model, maintaining stylistic consistency throughout the book.
+- **Persona Cache:** The author persona (including writing sample files) is loaded once at the start of the writing phase and reused for every chapter, eliminating redundant file I/O. The cache is refreshed whenever the persona is refined.
+- **Outline Validation Gate (`planner.py`):** Before the writing phase begins, a Logic-model pass checks the chapter plan for missing required beats, character continuity issues, pacing imbalances, and POV logic errors. Issues are logged as warnings so the writer can review them before generation begins.
+- **Adaptive Scoring Thresholds (`writer.py`):** Quality passing thresholds scale with chapter position — setup chapters use a lower bar (6.5) to avoid over-spending refinement tokens on early exposition, while climax chapters use a stricter bar (7.5) to ensure the most important scenes receive maximum effort.
+- **Smart Beat Expansion Skip (`writer.py`):** If a chapter's scene beats are already detailed (>100 words total), the Director's Treatment expansion step is skipped, saving ~5K tokens per chapter.
 - **Consistency Checker (`editor.py`):** Scores chapters on 13 rubrics (engagement, voice, sensory detail, scene execution, dialogue, pacing, staging, prose dynamics, clarity, etc.) and flags AI-isms ("tapestry", "palpable tension") and weak filter verbs ("felt", "realized"). Chapter evaluation now uses the Logic model (free Pro) rather than the Writer model, ensuring stricter and more accurate scoring.
 - **Dynamic Character Injection (`writer.py`):** Only injects characters explicitly named in the chapter's `scene_beats` plus the POV character into the writer prompt. Eliminates token waste from unused characters and reduces hallucinated appearances.
 - **Smart Context Tail (`writer.py`):** Extracts the final ~1,000 tokens of the previous chapter (the actual ending) rather than blindly truncating from the front. Ensures the hand-off point — where characters are standing and what was last said — is always preserved.