feat: Implement ai_blueprint_v2.md — Exp 5, 6 & 7 (persona validation, mid-gen consistency, two-pass drafting)

Exp 6 — Iterative Persona Validation (story/style_persona.py + cli/engine.py):
- Added validate_persona(): generates ~200-word sample in persona voice, scores 1–10 via
  lightweight voice-quality prompt; accepts if ≥ 7/10
- cli/engine.py retries create_initial_persona() up to 3× until validation passes
- Expected: -20% Phase 3 voice-drift rewrites

Exp 5 — Mid-gen Consistency Snapshots (cli/engine.py):
- analyze_consistency() called every 10 chapters inside the writing loop
- Issues logged as ⚠️ warnings; non-blocking; score and summary emitted
- Expected: -30% post-generation continuity error rate

Exp 7 — Two-Pass Drafting (story/writer.py):
- After Flash rough draft, Pro model (model_logic) polishes prose against a strict
  checklist: filter words, deep POV, active voice, AI-isms, chapter hook
- max_attempts reduced 3 → 2 since polished prose needs fewer rewrite cycles
- Expected: +0.3 HQS with no increase in per-chapter cost

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

story/style_persona.py

@@ -104,6 +104,86 @@ def create_initial_persona(bp, folder):
         return {"name": "AI Author", "bio": "Standard, balanced writing style."}
 
 
+def validate_persona(bp, persona_details, folder):
+    """Validate a newly created persona by generating a 200-word sample and scoring it.
+
+    Experiment 6 (Iterative Persona Validation): generates a test passage in the
+    persona's voice and evaluates voice quality before accepting it. This front-loads
+    quality assurance so Phase 3 starts with a well-calibrated author voice.
+
+    Returns (is_valid: bool, score: int). Threshold: score >= 7 → accepted.
+    """
+    meta = bp.get('book_metadata', {})
+    genre = meta.get('genre', 'Fiction')
+    tone = meta.get('style', {}).get('tone', 'balanced')
+    name = persona_details.get('name', 'Unknown Author')
+    bio = persona_details.get('bio', 'Standard style.')
+
+    sample_prompt = f"""
+    ROLE: Fiction Writer
+    TASK: Write a 200-word opening scene that perfectly demonstrates this author's voice.
+
+    AUTHOR_PERSONA:
+    Name: {name}
+    Style/Bio: {bio}
+
+    GENRE: {genre}
+    TONE: {tone}
+
+    RULES:
+    - Exactly ~200 words of prose (no chapter header, no commentary)
+    - Must reflect the persona's stated sentence structure, vocabulary, and voice
+    - Show, don't tell — no filter words (felt, saw, heard, realized, noticed)
+    - Deep POV: immerse the reader in a character's immediate experience
+
+    OUTPUT: Prose only.
+    """
+    try:
+        resp = ai_models.model_logic.generate_content(sample_prompt)
+        utils.log_usage(folder, ai_models.model_logic.name, resp.usage_metadata)
+        sample_text = resp.text
+    except Exception as e:
+        utils.log("SYSTEM", f"  -> Persona validation sample failed: {e}. Accepting persona.")
+        return True, 7
+
+    # Lightweight scoring: focused on voice quality (not full 13-rubric)
+    score_prompt = f"""
+    ROLE: Literary Editor
+    TASK: Score this prose sample for author voice quality.
+
+    EXPECTED_PERSONA:
+    {bio}
+
+    SAMPLE:
+    {sample_text}
+
+    CRITERIA:
+    1. Does the prose reflect the stated author persona? (voice, register, sentence style)
+    2. Is the prose free of filter words (felt, saw, heard, noticed, realized)?
+    3. Is it deep POV — immediate, immersive, not distant narration?
+    4. Is there genuine sentence variety and strong verb choice?
+
+    SCORING (1-10):
+    - 8-10: Voice is distinct, matches persona, clean deep POV
+    - 6-7: Reasonable voice, minor filter word issues
+    - 1-5: Generic AI prose, heavy filter words, or persona not reflected
+
+    OUTPUT_FORMAT (JSON): {{"score": int, "reason": "One sentence."}}
+    """
+    try:
+        resp2 = ai_models.model_logic.generate_content(score_prompt)
+        utils.log_usage(folder, ai_models.model_logic.name, resp2.usage_metadata)
+        data = json.loads(utils.clean_json(resp2.text))
+        score = int(data.get('score', 7))
+        reason = data.get('reason', '')
+        is_valid = score >= 7
+        utils.log("SYSTEM", f"  -> Persona validation: {score}/10 {'✅ Accepted' if is_valid else '❌ Rejected'} — {reason}")
+        return is_valid, score
+    except Exception as e:
+        utils.log("SYSTEM", f"  -> Persona scoring failed: {e}. Accepting persona.")
+        return True, 7
+
+
 def refine_persona(bp, text, folder):
     utils.log("SYSTEM", "Refining Author Persona based on recent chapters...")
     ad = bp.get('book_metadata', {}).get('author_details', {})

story/writer.py

@@ -362,7 +362,51 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
         utils.log("WRITER", f"⚠️ Failed Ch {chap['chapter_number']}: {e}")
         return f"## Chapter {chap['chapter_number']} Failed\n\nError: {e}"
 
-    max_attempts = 3
+    # Exp 7: Two-Pass Drafting — Polish the rough draft with the logic (Pro) model
+    # before evaluation. Produces cleaner prose with fewer rewrite cycles.
+    if current_text:
+        utils.log("WRITER", f"  -> Two-pass polish (Pro model)...")
+        guidelines = get_style_guidelines()
+        fw_list = '", "'.join(guidelines['filter_words'])
+        polish_prompt = f"""
+        ROLE: Senior Fiction Editor
+        TASK: Polish this rough draft into publication-ready prose.
+
+        AUTHOR_VOICE:
+        {persona_info}
+
+        GENRE: {genre}
+        TARGET_WORDS: ~{est_words}
+        BEATS (must all be covered): {json.dumps(chap.get('beats', []))}
+
+        POLISH_CHECKLIST:
+        1. FILTER_REMOVAL: Remove all filter words [{fw_list}] — rewrite each to show the sensation directly.
+        2. DEEP_POV: Ensure the reader is inside the POV character's experience at all times — no external narration.
+        3. ACTIVE_VOICE: Replace all 'was/were + -ing' constructions with active alternatives.
+        4. SENTENCE_VARIETY: No two consecutive sentences starting with the same word. Vary length for rhythm.
+        5. STRONG_VERBS: Delete adverbs; replace with precise verbs.
+        6. NO_AI_ISMS: Remove: 'testament to', 'tapestry', 'palpable tension', 'azure', 'cerulean', 'bustling', 'a sense of'.
+        7. CHAPTER_HOOK: Ensure the final paragraph ends on unresolved tension, a question, or a threat.
+        8. PRESERVE: Keep all narrative beats, approximate word count (±15%), and chapter header.
+
+        ROUGH_DRAFT:
+        {current_text}
+
+        OUTPUT: Complete polished chapter in Markdown.
+        """
+        try:
+            resp_polish = ai_models.model_logic.generate_content(polish_prompt)
+            utils.log_usage(folder, ai_models.model_logic.name, resp_polish.usage_metadata)
+            polished = resp_polish.text
+            if polished:
+                polished_words = len(polished.split())
+                utils.log("WRITER", f"  -> Polished: {polished_words:,} words.")
+                current_text = polished
+        except Exception as e:
+            utils.log("WRITER", f"  -> Polish pass failed: {e}. Proceeding with raw draft.")
+
+    # Reduced from 3 → 2 attempts since polish pass already refines prose before evaluation
+    max_attempts = 2
     SCORE_AUTO_ACCEPT = 8
     # Adaptive passing threshold: lenient for early setup chapters, strict for climax/resolution.
     # chapter_position=0.0 → setup (SCORE_PASSING=6.5), chapter_position=1.0 → climax (7.5)