The Master, His AI Emissary, and the Mu-Puzzle: Building Digital Corpus Callosums

[mike@nixos:~/repos/pipulate]$ xv prompt_foo.py

[mike@nixos:~/repos/pipulate]$ git --no-pager diff
diff --git a/prompt_foo.py b/prompt_foo.py
index 29fe3978..84a8af0b 100644
--- a/prompt_foo.py
+++ b/prompt_foo.py
@@ -4,18 +4,20 @@ import os
 import re
 import sys
 import pydot
+import yaml # NEW: For parsing article front matter
 import argparse
 import tiktoken
 import subprocess
 import tempfile
 import shutil
+from datetime import datetime # NEW: For parsing article dates
 from typing import Dict, List, Optional, Tuple
 
 try:
-    import jupytext
-    JUPYTEXT_AVAILABLE = True
+    import jupytext
+    JUPYTEXT_AVAILABLE = True
 except ImportError:
-    JUPYTEXT_AVAILABLE = False
+    JUPYTEXT_AVAILABLE = False
 
 # Hello there, AI! This is a tool for generating a single, comprehensive prompt
 # from the command line, bundling codebase files and auto-generated context
@@ -25,547 +27,644 @@ except ImportError:
 # --- Configuration ---
 # ============================================================================
 def find_repo_root(start_path: str) -> str:
-    """Find the git repository root from a starting path."""
-    path = os.path.abspath(start_path)
-    while path != os.path.dirname(path):
-        if os.path.isdir(os.path.join(path, '.git')):
-            return path
-        path = os.path.dirname(path)
-    raise FileNotFoundError("Could not find the repository root (.git directory).")
+    """Find the git repository root from a starting path."""
+    path = os.path.abspath(start_path)
+    while path != os.path.dirname(path):
+        if os.path.isdir(os.path.join(path, '.git')):
+            return path
+        path = os.path.dirname(path)
+    raise FileNotFoundError("Could not find the repository root (.git directory).")
 
 REPO_ROOT = find_repo_root(os.path.dirname(__file__))
 
 # Centralized configuration as recommended in Architectural Analysis (Section VI-B)
 CONFIG = {
-    "PROJECT_NAME": "pipulate"
+    "PROJECT_NAME": "pipulate",
+    "POSTS_DIRECTORY": "/home/mike/repos/MikeLev.in/_posts" # NEW: For list_articles logic
 }
 
 # ============================================================================
 # --- Accurate Literary Size Scale (Word Count Based) ---
 # ============================================================================
 LITERARY_SIZE_SCALE = [
-    (3000, "Short Essay"),
-    (7500, "Short Story"),
-    (20000, "Novelette"),
-    (50000, "Novella or a Master's Dissertation"),
-    (80000, "Average Paperback Novel or a Ph.D. Dissertation"),
-    (120000, "Long Novel"),
-    (200000, "Epic Fantasy Novel"),
-    (500000, "Seriously Long Epic (like 'Infinite Jest')"),
+    (3000, "Short Essay"),
+    (7500, "Short Story"),
+    (20000, "Novelette"),
+    (50000, "Novella or a Master's Dissertation"),
+    (80000, "Average Paperback Novel or a Ph.D. Dissertation"),
+    (120000, "Long Novel"),
+    (200000, "Epic Fantasy Novel"),
+    (500000, "Seriously Long Epic (like 'Infinite Jest')"),
 ]
 
 def get_literary_perspective(word_count: int, token_word_ratio: float) -> str:
-    """Get a human-readable literary comparison for the codebase size."""
-    description = f"Longer than {LITERARY_SIZE_SCALE[-1][1]}"
-    for words, desc in LITERARY_SIZE_SCALE:
-        if word_count <= words:
-            description = desc
-            break
-
-    density_warning = ""
-    if token_word_ratio > 1.8:
-        density_warning = (
-            f" (Note: With a token/word ratio of {token_word_ratio:.2f}, "
-            f"this content is far denser and more complex than typical prose of this length)."
-        )
-
-    return f"📚 Equivalent in length to a **{description}**{density_warning}"
+    """Get a human-readable literary comparison for the codebase size."""
+    description = f"Longer than {LITERARY_SIZE_SCALE[-1][1]}"
+    for words, desc in LITERARY_SIZE_SCALE:
+        if word_count <= words:
+            description = desc
+            break
+
+    density_warning = ""
+    if token_word_ratio > 1.8:
+        density_warning = (
+            f" (Note: With a token/word ratio of {token_word_ratio:.2f}, "
+            f"this content is far denser and more complex than typical prose of this length)."
+        )
+
+    return f"📚 Equivalent in length to a **{description}**{density_warning}"
 
 # ============================================================================
 # --- Restored & Corrected: UML and DOT Context Generation ---
 # ============================================================================
 def generate_uml_and_dot(target_file: str, project_name: str) -> Dict:
-    """Generates a UML ASCII diagram and a DOT dependency graph for a target Python file."""
-    pyreverse_exec = shutil.which("pyreverse")
-    plantuml_exec = shutil.which("plantuml")
-
-    if not pyreverse_exec or not plantuml_exec:
-        msg = []
-        if not pyreverse_exec: msg.append("`pyreverse` (from pylint)")
-        if not plantuml_exec: msg.append("`plantuml`")
-        return {"ascii_uml": f"Skipping: Required command(s) not found: {', '.join(msg)}."}
-
-    target_path = os.path.join(REPO_ROOT, target_file)
-    if not os.path.exists(target_path):
-        return {"ascii_uml": f"Skipping: Target file for UML generation not found: {target_path}"}
-
-    with tempfile.TemporaryDirectory() as temp_dir:
-        dot_file_path = os.path.join(temp_dir, "classes.dot")
-        puml_file_path = os.path.join(temp_dir, "diagram.puml")
-        
-        # --- Step 1: Run pyreverse ---
-        try:
-            pyreverse_cmd = [
-                pyreverse_exec,
-                "-f", "dot",
-                "-o", "dot", # This format is just a prefix
-                "-p", project_name,
-                target_path
-            ]
-            subprocess.run(
-                pyreverse_cmd,
-                check=True,
-                capture_output=True,
-                text=True,
-                cwd=temp_dir
-            )
-            generated_dot_name = f"classes_{project_name}.dot"
-            os.rename(os.path.join(temp_dir, generated_dot_name), dot_file_path)
-
-        except (subprocess.CalledProcessError, FileNotFoundError) as e:
-            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
-            return {"ascii_uml": f"Error: pyreverse failed. {error_msg}", "dot_graph": None}
-
-        # --- Step 2: Convert DOT to PlantUML ---
-        try:
-            graphs = pydot.graph_from_dot_file(dot_file_path)
-            if not graphs:
-                return {"ascii_uml": f"Note: No classes found in {target_file} to generate a diagram.", "dot_graph": None}
-            graph = graphs[0]
-            dot_content = graph.to_string()
-
-            puml_lines = ["@startuml", "skinparam linetype ortho", ""]
-
-            def sanitize_line(line):
-                clean = re.sub(r'<br[^>]*>', '', line)
-                clean = re.sub(r'<[^>]+>', '', clean)
-                return clean.strip()
-
-            for node in graph.get_nodes():
-                label = node.get_label()
-                if not label: continue
-
-                parts = label.strip('<>{} ').split('|')
-                class_name = sanitize_line(parts[0])
-                puml_lines.append(f"class {class_name} {{")
-
-                if len(parts) > 1:
-                    for attr in re.split(r'<br[^>]*>', parts[1]):
-                        clean_attr = sanitize_line(attr).split(':')[0].strip()
-                        if clean_attr:
-                            puml_lines.append(f"  - {clean_attr}")
-
-                if len(parts) > 2:
-                    method_block = parts[2].strip()
-                    for method_line in re.split(r'<br[^>]*>', method_block):
-                        clean_method = sanitize_line(method_line)
-                        if clean_method:
-                            puml_lines.append(f"  + {clean_method}")
-
-                puml_lines.append("}\n")
-
-            for edge in graph.get_edges():
-                source_name = edge.get_source().strip('"').split('.')[-1]
-                dest_name = edge.get_destination().strip('"').split('.')[-1]
-                puml_lines.append(f"{source_name} ..> {dest_name}")
-
-            puml_lines.append("@enduml")
-            with open(puml_file_path, 'w') as f:
-                f.write('\n'.join(puml_lines))
-
-        except Exception as e:
-            with open(dot_file_path, 'r') as f:
-                dot_content_on_error = f.read()
-            return {"ascii_uml": f"Error: DOT to PUML conversion failed. {str(e)}", "dot_graph": dot_content_on_error}
- 
-        # --- Step 3: Run PlantUML ---
-        try:
-            plantuml_cmd = ["plantuml", "-tutxt", puml_file_path]
-            subprocess.run(plantuml_cmd, check=True, capture_output=True, text=True, cwd=temp_dir)
-            
-            utxt_file_path = puml_file_path.replace(".puml", ".utxt")
-            with open(utxt_file_path, 'r') as f:
-                ascii_uml = f.read()
-            
-            # --- Normalize whitespace from plantuml output ---
-            lines = ascii_uml.splitlines()
-            non_empty_lines = [line for line in lines if line.strip()]
-            
-            if non_empty_lines:
-                min_indent = min(len(line) - len(line.lstrip(' ')) for line in non_empty_lines)
-                dedented_lines = [line[min_indent:] for line in lines]
-                stripped_lines = [line.rstrip() for line in dedented_lines]
-                ascii_uml = '\n'.join(stripped_lines)
-                
-                # Prepend a newline to "absorb the chop" from rendering
-                if ascii_uml:
-                    ascii_uml = '\n' + ascii_uml
-
-        except (subprocess.CalledProcessError, FileNotFoundError) as e:
-            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
-            return {"ascii_uml": f"Error: plantuml failed. {error_msg}", "dot_graph": dot_content}
-
-    return {"ascii_uml": ascii_uml, "dot_graph": dot_content}
+    """Generates a UML ASCII diagram and a DOT dependency graph for a target Python file."""
+    pyreverse_exec = shutil.which("pyreverse")
+    plantuml_exec = shutil.which("plantuml")
+
+    if not pyreverse_exec or not plantuml_exec:
+        msg = []
+        if not pyreverse_exec: msg.append("`pyreverse` (from pylint)")
+        if not plantuml_exec: msg.append("`plantuml`")
+        return {"ascii_uml": f"Skipping: Required command(s) not found: {', '.join(msg)}."}
+
+    target_path = os.path.join(REPO_ROOT, target_file)
+    if not os.path.exists(target_path):
+        return {"ascii_uml": f"Skipping: Target file for UML generation not found: {target_path}"}
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        dot_file_path = os.path.join(temp_dir, "classes.dot")
+        puml_file_path = os.path.join(temp_dir, "diagram.puml")
+        
+        # --- Step 1: Run pyreverse ---
+        try:
+            pyreverse_cmd = [
+                pyreverse_exec,
+                "-f", "dot",
+                "-o", "dot", # This format is just a prefix
+                "-p", project_name,
+                target_path
+            ]
+            subprocess.run(
+                pyreverse_cmd,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=temp_dir
+            )
+            generated_dot_name = f"classes_{project_name}.dot"
+            os.rename(os.path.join(temp_dir, generated_dot_name), dot_file_path)
+
+        except (subprocess.CalledProcessError, FileNotFoundError) as e:
+            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
+            return {"ascii_uml": f"Error: pyreverse failed. {error_msg}", "dot_graph": None}
+
+        # --- Step 2: Convert DOT to PlantUML ---
+        try:
+            graphs = pydot.graph_from_dot_file(dot_file_path)
+            if not graphs:
+                return {"ascii_uml": f"Note: No classes found in {target_file} to generate a diagram.", "dot_graph": None}
+            graph = graphs[0]
+            dot_content = graph.to_string()
+
+            puml_lines = ["@startuml", "skinparam linetype ortho", ""]
+
+            def sanitize_line(line):
+                clean = re.sub(r'<br[^>]*>', '', line)
+                clean = re.sub(r'<[^>]+>', '', clean)
+                return clean.strip()
+
+            for node in graph.get_nodes():
+                label = node.get_label()
+                if not label: continue
+
+                parts = label.strip('<>{} ').split('|')
+                class_name = sanitize_line(parts[0])
+                puml_lines.append(f"class {class_name} {{")
+
+                if len(parts) > 1:
+                    for attr in re.split(r'<br[^>]*>', parts[1]):
+                        clean_attr = sanitize_line(attr).split(':')[0].strip()
+                        if clean_attr:
+                            puml_lines.append(f"  - {clean_attr}")
+
+                if len(parts) > 2:
+                    method_block = parts[2].strip()
+                    for method_line in re.split(r'<br[^>]*>', method_block):
+                        clean_method = sanitize_line(method_line)
+                        if clean_method:
+                            puml_lines.append(f"  + {clean_method}")
+
+                puml_lines.append("}\n")
+
+            for edge in graph.get_edges():
+                source_name = edge.get_source().strip('"').split('.')[-1]
+                dest_name = edge.get_destination().strip('"').split('.')[-1]
+                puml_lines.append(f"{source_name} ..> {dest_name}")
+
+            puml_lines.append("@enduml")
+            with open(puml_file_path, 'w') as f:
+                f.write('\n'.join(puml_lines))
+
+        except Exception as e:
+            with open(dot_file_path, 'r') as f:
+                dot_content_on_error = f.read()
+            return {"ascii_uml": f"Error: DOT to PUML conversion failed. {str(e)}", "dot_graph": dot_content_on_error}
+ 
+        # --- Step 3: Run PlantUML ---
+        try:
+            plantuml_cmd = ["plantuml", "-tutxt", puml_file_path]
+            subprocess.run(plantuml_cmd, check=True, capture_output=True, text=True, cwd=temp_dir)
+            
+            utxt_file_path = puml_file_path.replace(".puml", ".utxt")
+            with open(utxt_file_path, 'r') as f:
+                ascii_uml = f.read()
+            
+            # --- Normalize whitespace from plantuml output ---
+            lines = ascii_uml.splitlines()
+            non_empty_lines = [line for line in lines if line.strip()]
+            
+            if non_empty_lines:
+                min_indent = min(len(line) - len(line.lstrip(' ')) for line in non_empty_lines)
+                dedented_lines = [line[min_indent:] for line in lines]
+                stripped_lines = [line.rstrip() for line in dedented_lines]
+                ascii_uml = '\n'.join(stripped_lines)
+                
+                # Prepend a newline to "absorb the chop" from rendering
+                if ascii_uml:
+                    ascii_uml = '\n' + ascii_uml
+
+        except (subprocess.CalledProcessError, FileNotFoundError) as e:
+            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
+            return {"ascii_uml": f"Error: plantuml failed. {error_msg}", "dot_graph": dot_content}
+
+    return {"ascii_uml": ascii_uml, "dot_graph": dot_content}
+
+
+# ============================================================================
+# --- NEW: Logic ported from list_articles.py for Narrative Context ---
+# ============================================================================
+def _get_article_list_data(posts_dir: str = CONFIG["POSTS_DIRECTORY"]) -> List[Dict]:
+    """
+    Parses Jekyll posts, sorts them chronologically, and returns a list of dictionaries.
+    This is a self-contained version of the logic from the `list_articles.py` script.
+    """
+    posts_data = []
+    if not os.path.isdir(posts_dir):
+        print(f"Warning: Article directory not found at {posts_dir}", file=sys.stderr)
+        return []
+
+    for filename in os.listdir(posts_dir):
+        if not filename.endswith(('.md', '.markdown')):
+            continue
+        filepath = os.path.join(posts_dir, filename)
+        try:
+            date_str = filename[:10]
+            post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
+            with open(filepath, 'r', encoding='utf-8') as f:
+                content = f.read()
+            if content.startswith('---'):
+                parts = content.split('---', 2)
+                front_matter = yaml.safe_load(parts[1]) or {}
+                posts_data.append({
+                    'path': filepath,
+                    'date': post_date,
+                    'sort_order': int(front_matter.get('sort_order', 0)),
+                    'title': front_matter.get('title', 'Untitled'),
+                    'summary': front_matter.get('meta_description', '')
+                })
+        except (ValueError, yaml.YAMLError, IndexError):
+            continue
+
+    sorted_posts = sorted(posts_data, key=lambda p: (p['date'], p['sort_order']))
+    return sorted_posts
+
+def parse_slice_arg(arg_str: str):
+    """Parses a string like '[5:10]' or '[5]' into a slice object or an int."""
+    if not arg_str or not arg_str.startswith('[') or not arg_str.endswith(']'):
+        return None
+    content = arg_str[1:-1].strip()
+    if ':' in content:
+        parts = content.split(':', 1)
+        start = int(parts[0].strip()) if parts[0].strip() else None
+        end = int(parts[1].strip()) if parts[1].strip() else None
+        return slice(start, end)
+    elif content:
+        return int(content)
+    return None
 
 # ============================================================================
 # --- Helper Functions (Tokenizing, File Parsing, Clipboard) ---
 # ============================================================================
 def count_tokens(text: str, model: str = "gpt-4o") -> int:
-    """Counts tokens in a text string using tiktoken."""
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-        return len(encoding.encode(text))
-    except Exception:
-        return len(text.split())
+    """Counts tokens in a text string using tiktoken."""
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+        return len(encoding.encode(text))
+    except Exception:
+        return len(text.split())
 
 def count_words(text: str) -> int:
-    """Counts words in a text string."""
-    return len(text.split())
+    """Counts words in a text string."""
+    return len(text.split())
 
 def parse_file_list_from_config() -> List[Tuple[str, str]]:
-    """Loads and parses the file list from foo_files.py."""
-    try:
-        import foo_files
-        files_raw = foo_files.FILES_TO_INCLUDE_RAW
-    except (ImportError, AttributeError):
-        print("ERROR: foo_files.py not found or doesn't contain FILES_TO_INCLUDE_RAW.")
-        sys.exit(1)
-
-    lines = files_raw.strip().splitlines()
-    seen_files, parsed_files = set(), []
-
-    for line in lines:
-        line = line.strip()
-        if not line or line.startswith('#'):
-            continue
-        parts = re.split(r'\s*<--\s*|\s*#\s*', line, 1)
-        file_path = parts[0].strip()
-        comment = parts[1].strip() if len(parts) > 1 else ""
-
-        if file_path and file_path not in seen_files:
-            seen_files.add(file_path)
-            parsed_files.append((file_path, comment))
-    return parsed_files
+    """Loads and parses the file list from foo_files.py."""
+    try:
+        import foo_files
+        files_raw = foo_files.FILES_TO_INCLUDE_RAW
+    except (ImportError, AttributeError):
+        print("ERROR: foo_files.py not found or doesn't contain FILES_TO_INCLUDE_RAW.")
+        sys.exit(1)
+
+    lines = files_raw.strip().splitlines()
+    seen_files, parsed_files = set(), []
+
+    for line in lines:
+        line = line.strip()
+        if not line or line.startswith('#'):
+            continue
+        parts = re.split(r'\s*<--\s*|\s*#\s*', line, 1)
+        file_path = parts[0].strip()
+        comment = parts[1].strip() if len(parts) > 1 else ""
+
+        if file_path and file_path not in seen_files:
+            seen_files.add(file_path)
+            parsed_files.append((file_path, comment))
+    return parsed_files
 
 def copy_to_clipboard(text: str):
-    """Copies text to the system clipboard using 'xclip'."""
-    if not shutil.which('xclip'):
-        print("\nWarning: 'xclip' not found. Cannot copy to clipboard.")
-        return
-    try:
-        subprocess.run(['xclip', '-selection', 'clipboard'], input=text.encode('utf-8'), check=True)
-        print("Markdown output copied to clipboard")
-    except Exception as e:
-        print(f"\nWarning: Could not copy to clipboard: {e}")
+    """Copies text to the system clipboard using 'xclip'."""
+    if not shutil.which('xclip'):
+        print("\nWarning: 'xclip' not found. Cannot copy to clipboard.")
+        return
+    try:
+        subprocess.run(['xclip', '-selection', 'clipboard'], input=text.encode('utf-8'), check=True)
+        print("Markdown output copied to clipboard")
+    except Exception as e:
+        print(f"\nWarning: Could not copy to clipboard: {e}")
 
 def run_tree_command() -> str:
-    """Runs the 'eza' command to generate a tree view that respects .gitignore."""
-    eza_exec = shutil.which("eza")
-    if not eza_exec:
-        return "Skipping: `eza` command not found."
-    
-    try:
-        result = subprocess.run(
-            [eza_exec, '--tree', '--git-ignore', '--color=never'],
-            capture_output=True,
-            text=True,
-            cwd=REPO_ROOT,
-            check=True
-        )
-        return result.stdout
-    except subprocess.CalledProcessError as e:
-        return f"Error running eza command: {e.stderr}"
-    except Exception as e:
-        return f"An unexpected error occurred while running eza: {str(e)}"
+    """Runs the 'eza' command to generate a tree view that respects .gitignore."""
+    eza_exec = shutil.which("eza")
+    if not eza_exec:
+        return "Skipping: `eza` command not found."
+    
+    try:
+        result = subprocess.run(
+            [eza_exec, '--tree', '--git-ignore', '--color=never'],
+            capture_output=True,
+            text=True,
+            cwd=REPO_ROOT,
+            check=True
+        )
+        return result.stdout
+    except subprocess.CalledProcessError as e:
+        return f"Error running eza command: {e.stderr}"
+    except Exception as e:
+        return f"An unexpected error occurred while running eza: {str(e)}"
 
 def check_dependencies():
-    """Verifies that all required external command-line tools are installed."""
-    print("Checking for required external dependencies...")
-    dependencies = {
-        "pyreverse": "Provided by `pylint`. Install with: pip install pylint",
-        "plantuml": "A Java-based tool. See https://plantuml.com/starting",
-        "eza": "A modern replacement for `ls`. See https://eza.rocks/install",
-        "xclip": "Clipboard utility for Linux. Install with your package manager (e.g., sudo apt-get install xclip)",
-    }
-    missing = []
-    
-    for tool, instructions in dependencies.items():
-        if not shutil.which(tool):
-            missing.append((tool, instructions))
-    
-    if not missing:
-        print("✅ All dependencies found.")
-    else:
-        print("\n❌ Missing dependencies detected:")
-        for tool, instructions in missing:
-            print(f"  - Command not found: `{tool}`")
-            print(f"    ↳ {instructions}")
-        print("\nPlease install the missing tools and ensure they are in your system's PATH.")
-        sys.exit(1)
+    """Verifies that all required external command-line tools are installed."""
+    print("Checking for required external dependencies...")
+    dependencies = {
+        "pyreverse": "Provided by `pylint`. Install with: pip install pylint",
+        "plantuml": "A Java-based tool. See https://plantuml.com/starting",
+        "eza": "A modern replacement for `ls`. See https://eza.rocks/install",
+        "xclip": "Clipboard utility for Linux. Install with your package manager (e.g., sudo apt-get install xclip)",
+    }
+    missing = []
+    
+    for tool, instructions in dependencies.items():
+        if not shutil.which(tool):
+            missing.append((tool, instructions))
+    
+    if not missing:
+        print("✅ All dependencies found.")
+    else:
+        print("\n❌ Missing dependencies detected:")
+        for tool, instructions in missing:
+            print(f"  - Command not found: `{tool}`")
+            print(f"    ↳ {instructions}")
+        print("\nPlease install the missing tools and ensure they are in your system's PATH.")
+        sys.exit(1)
 
 # ============================================================================
 # --- Intelligent PromptBuilder Class ---
 # ============================================================================
 class PromptBuilder:
-    """
-    Builds a complete, structured Markdown prompt including file manifests,
-    auto-generated context, file contents, and the user's final prompt.
-    """
-    def __init__(self, processed_files: List[Dict], prompt_text: str, context_only: bool = False):
-        self.processed_files = processed_files
-        self.prompt_text = prompt_text
-        self.context_only = context_only
-        self.auto_context = {}
-
-    def add_auto_context(self, title: str, content: str):
-        """Adds auto-generated context like UML diagrams to the prompt."""
-        if content and "error" not in content.lower() and "skipping" not in content.lower():
-            self.auto_context[title] = {
-                'content': content,
-                'tokens': count_tokens(content),
-                'words': count_words(content)
-            }
-
-    def _generate_manifest_header(self) -> str:
-        lines = ["# Codebase Context & Manifest", ""]
-        for f in self.processed_files:
-            purpose = f" ({f['comment']})" if f['comment'] else ""
-            token_display = f" ({f['tokens']:,} tokens)" if not self.context_only else ""
-            lines.append(f"- **{f['path']}**{purpose}{token_display}")
-        return "\n".join(lines)
-
-    def _generate_auto_context_section(self) -> str:
-        if not self.auto_context:
-            return ""
-        lines = ["", "---", "", "# Auto-Generated Context", ""]
-        if "Codebase Structure (eza --tree)" in self.auto_context:
-            title = "Codebase Structure (eza --tree)"
-            content = self.auto_context[title]['content']
-            lines.append(f"## {title}")
-            lines.append("```text")
-            lines.append(content.strip())
-            lines.append("```")
-        
-        for title, data in self.auto_context.items():
-            if title != "Codebase Structure (eza --tree)":
-                lines.append(f"## {title}")
-                lines.append("```text")
-                lines.append(data['content'])
-                lines.append("```")
-        return "\n".join(lines)
-
-    def _generate_file_contents(self) -> str:
-        lines = []
-        for f in self.processed_files:
-            lines.append(f"```{f['lang']}:{f['path']}")
-            lines.append(f['content'])
-            lines.append("```")
-            lines.append(f"\n# End of {f['path']}\n")
-        return "\n".join(lines)
-
-    def _generate_ai_checklist(self) -> str:
-        """Creates a standardized checklist for the AI to verify the prompt's context."""
-        return '''
+    """
+    Builds a complete, structured Markdown prompt including file manifests,
+    auto-generated context, file contents, and the user's final prompt.
+    """
+    def __init__(self, processed_files: List[Dict], prompt_text: str, context_only: bool = False):
+        self.processed_files = processed_files
+        self.prompt_text = prompt_text
+        self.context_only = context_only
+        self.auto_context = {}
+
+    def add_auto_context(self, title: str, content: str):
+        """Adds auto-generated context like UML diagrams to the prompt."""
+        if content and "error" not in content.lower() and "skipping" not in content.lower():
+            self.auto_context[title] = {
+                'content': content,
+                'tokens': count_tokens(content),
+                'words': count_words(content)
+            }
+
+    def _generate_manifest_header(self) -> str:
+        lines = ["# Codebase Context & Manifest", ""]
+        for f in self.processed_files:
+            purpose = f" ({f['comment']})" if f['comment'] else ""
+            token_display = f" ({f['tokens']:,} tokens)" if not self.context_only else ""
+            lines.append(f"- **{f['path']}**{purpose}{token_display}")
+        return "\n".join(lines)
+
+    def _generate_auto_context_section(self) -> str:
+        if not self.auto_context:
+            return ""
+        lines = ["", "---", "", "# Auto-Generated Context", ""]
+        
+        # --- NEW: Prioritize printing Narrative Context first ---
+        narrative_title = "Recent Narrative Context"
+        if narrative_title in self.auto_context:
+            content = self.auto_context[narrative_title]['content']
+            lines.append(f"## {narrative_title}")
+            lines.append(content.strip())
+            lines.append("") # Add a blank line for spacing
+
+        if "Codebase Structure (eza --tree)" in self.auto_context:
+            title = "Codebase Structure (eza --tree)"
+            content = self.auto_context[title]['content']
+            lines.append(f"## {title}")
+            lines.append("```text")
+            lines.append(content.strip())
+            lines.append("```")
+        
+        for title, data in self.auto_context.items():
+            if title not in ["Codebase Structure (eza --tree)", narrative_title]:
+                lines.append(f"## {title}")
+                lines.append("```text")
+                lines.append(data['content'])
+                lines.append("```")
+        return "\n".join(lines)
+
+    def _generate_file_contents(self) -> str:
+        lines = []
+        for f in self.processed_files:
+            lines.append(f"```{f['lang']}:{f['path']}")
+            lines.append(f['content'])
+            lines.append("```")
+            lines.append(f"\n# End of {f['path']}\n")
+        return "\n".join(lines)
+
+    def _generate_ai_checklist(self) -> str:
+        """Creates a standardized checklist for the AI to verify the prompt's context."""
+        return '''
 # AI Self-Correction Checklist
 
 Before addressing the user's prompt, perform the following verification steps:
 
-1.  **Review the Manifest vs. Codebase Structure:** Compare the file list in the manifest against the `eza --tree` output. Are there any obviously related, critical files missing from the manifest that would be necessary to understand the user's request? (e.g., if `core.py` is present, is `__init__.py` also present?).
-2.  **Analyze Import Statements:** Briefly scan the `import` statements in the provided Python files. Do they suggest dependencies on local files that were *not* included in the manifest?
-3.  **Check for Incompleteness:** If you determine that critical files are missing, do not proceed. Instead, your **primary task** is to inform me of the missing files and recommend adding them to `foo_files.py` to create a more complete context.
-4.  **Confirm Understanding:** If the context appears complete, state "Context verified." and then proceed with the user's request.
+1.  **Review the Manifest vs. Codebase Structure:** Compare the file list in the manifest against the `eza --tree` output. Are there any obviously related, critical files missing from the manifest that would be necessary to understand the user's request? (e.g., if `core.py` is present, is `__init__.py` also present?).
+2.  **Analyze Import Statements:** Briefly scan the `import` statements in the provided Python files. Do they suggest dependencies on local files that were *not* included in the manifest?
+3.  **Check for Incompleteness:** If you determine that critical files are missing, do not proceed. Instead, your **primary task** is to inform me of the missing files and recommend adding them to `foo_files.py` to create a more complete context.
+4.  **Confirm Understanding:** If the context appears complete, state "Context verified." and then proceed with the user's request.
 '''
 
-    def build_final_prompt(self) -> str:
-        """Assembles all parts into the final Markdown string."""
-        parts = [
-            self._generate_manifest_header(),
-            self._generate_auto_context_section(),
-        ]
-
-        if not self.context_only:
-            parts.extend([
-                "\n---\n\n# File Contents\n",
-                self._generate_file_contents(),
-            ])
-
-        parts.extend([
-            "---\n\n# User Prompt\n",
-            self._generate_ai_checklist(),
-            self.prompt_text
-        ])
-        
-        return "\n".join(filter(None, parts))
-
-    def print_summary(self):
-        """Calculates and prints an accurate, comprehensive summary to the console."""
-        # --- Calculate token counts for all components ---
-        manifest_str = self._generate_manifest_header()
-        manifest_tokens = count_tokens(manifest_str)
-        manifest_words = count_words(manifest_str)
-        
-        prompt_tokens = count_tokens(self.prompt_text)
-        prompt_words = count_words(self.prompt_text)
-
-        checklist_str = self._generate_ai_checklist()
-        checklist_tokens = count_tokens(checklist_str)
-        checklist_words = count_words(checklist_str)
-
-        auto_context_total_tokens = sum(v['tokens'] for v in self.auto_context.values())
-        auto_context_total_words = sum(v['words'] for v in self.auto_context.values())
-
-        file_content_total_tokens = sum(f['tokens'] for f in self.processed_files)
-        file_content_total_words = sum(f['words'] for f in self.processed_files)
-
-        # --- Display the breakdown ---
-        print("--- Files Included ---")
-        for f in self.processed_files:
-            if self.context_only:
-                print(f"• {f['path']} (content omitted)")
-            else:
-                print(f"• {f['path']} ({f['tokens']:,} tokens)")
-        
-        if self.auto_context:
-            print("\n--- Auto-Context Included ---")
-            for title, data in self.auto_context.items():
-                print(f"• {title} ({data['tokens']:,} tokens)")
-
-        # --- Calculate and display the final summary ---
-        print("\n--- Prompt Summary ---")
-        if self.context_only:
-            print("NOTE: Running in --context-only mode. File contents are excluded.")
-            total_tokens = manifest_tokens + auto_context_total_tokens + prompt_tokens + checklist_tokens
-            total_words = manifest_words + auto_context_total_words + prompt_words + checklist_words
-        else:
-            total_tokens = manifest_tokens + auto_context_total_tokens + file_content_total_tokens + prompt_tokens + checklist_tokens
-            total_words = manifest_words + auto_context_total_words + file_content_total_words + prompt_words + checklist_words
-
-        print(f"Total Tokens: {total_tokens:,}")
-        print(f"Total Words:  {total_words:,}")
-
-        ratio = total_tokens / total_words if total_words > 0 else 0
-        perspective = get_literary_perspective(total_words, ratio)
-        print("\n--- Size Perspective ---")
-        print(perspective)
-        print()
+    def build_final_prompt(self) -> str:
+        """Assembles all parts into the final Markdown string."""
+        parts = [
+            self._generate_manifest_header(),
+            self._generate_auto_context_section(),
+        ]
+
+        if not self.context_only:
+            parts.extend([
+                "\n---\n\n# File Contents\n",
+                self._generate_file_contents(),
+            ])
+
+        parts.extend([
+            "---\n\n# User Prompt\n",
+            self._generate_ai_checklist(),
+            self.prompt_text
+        ])
+        
+        return "\n".join(filter(None, parts))
+
+    def print_summary(self):
+        """Calculates and prints an accurate, comprehensive summary to the console."""
+        # --- Calculate token counts for all components ---
+        manifest_str = self._generate_manifest_header()
+        manifest_tokens = count_tokens(manifest_str)
+        manifest_words = count_words(manifest_str)
+        
+        prompt_tokens = count_tokens(self.prompt_text)
+        prompt_words = count_words(self.prompt_text)
+
+        checklist_str = self._generate_ai_checklist()
+        checklist_tokens = count_tokens(checklist_str)
+        checklist_words = count_words(checklist_str)
+
+        auto_context_total_tokens = sum(v['tokens'] for v in self.auto_context.values())
+        auto_context_total_words = sum(v['words'] for v in self.auto_context.values())
+
+        file_content_total_tokens = sum(f['tokens'] for f in self.processed_files)
+        file_content_total_words = sum(f['words'] for f in self.processed_files)
+
+        # --- Display the breakdown ---
+        print("--- Files Included ---")
+        for f in self.processed_files:
+            if self.context_only:
+                print(f"• {f['path']} (content omitted)")
+            else:
+                print(f"• {f['path']} ({f['tokens']:,} tokens)")
+        
+        if self.auto_context:
+            print("\n--- Auto-Context Included ---")
+            for title, data in self.auto_context.items():
+                print(f"• {title} ({data['tokens']:,} tokens)")
+
+        # --- Calculate and display the final summary ---
+        print("\n--- Prompt Summary ---")
+        if self.context_only:
+            print("NOTE: Running in --context-only mode. File contents are excluded.")
+            total_tokens = manifest_tokens + auto_context_total_tokens + prompt_tokens + checklist_tokens
+            total_words = manifest_words + auto_context_total_words + prompt_words + checklist_words
+        else:
+            total_tokens = manifest_tokens + auto_context_total_tokens + file_content_total_tokens + prompt_tokens + checklist_tokens
+            total_words = manifest_words + auto_context_total_words + file_content_total_words + prompt_words + checklist_words
+
+        print(f"Total Tokens: {total_tokens:,}")
+        print(f"Total Words:  {total_words:,}")
+
+        ratio = total_tokens / total_words if total_words > 0 else 0
+        perspective = get_literary_perspective(total_words, ratio)
+        print("\n--- Size Perspective ---")
+        print(perspective)
+        print()
 
 # ============================================================================
 # --- Main Execution Logic ---
 # ============================================================================
 def main():
-    """Main function to parse args, process files, and generate output."""
-    parser = argparse.ArgumentParser(description='Generate a Markdown context file for AI code assistance.')
-    parser.add_argument('prompt', nargs='?', default=None, help='A prompt string or path to a prompt file (e.g., prompt.md).')
-    parser.add_argument('-o', '--output', type=str, help='Optional: Output filename.')
-    parser.add_argument('--no-clipboard', action='store_true', help='Disable copying output to clipboard.')
-    parser.add_argument('--check-dependencies', action='store_true', help='Verify that all required external tools are installed.')
-    parser.add_argument('--context-only', action='store_true', help='Generate a context-only prompt without file contents.')
-    args = parser.parse_args()
-
-    if args.check_dependencies:
-        check_dependencies()
-        sys.exit(0)
-
-    # 1. Handle user prompt
-    prompt_content = "Please review the provided context and assist with the codebase."
-    if args.prompt:
-        if os.path.exists(args.prompt):
-            with open(args.prompt, 'r', encoding='utf-8') as f:
-                prompt_content = f.read()
-        else:
-            prompt_content = args.prompt
-    elif os.path.exists("prompt.md"):
-        with open("prompt.md", 'r', encoding='utf-8') as f:
-            prompt_content = f.read()
-
-    # 2. Process all specified files
-    files_to_process = parse_file_list_from_config()
-    processed_files_data = []
-    print("--- Processing Files ---")
-    for path, comment in files_to_process:
-        full_path = os.path.join(REPO_ROOT, path) if not os.path.isabs(path) else path
-        if not os.path.exists(full_path):
-            print(f"Warning: File not found and will be skipped: {full_path}")
-            continue
-
-        content = ""
-        lang = "text"
-        ext = os.path.splitext(path)[1].lower()
-
-        if ext == '.ipynb':
-            if JUPYTEXT_AVAILABLE:
-                print(f"  -> Converting notebook: {path}")
-                try:
-                    notebook = jupytext.read(full_path)
-                    content = jupytext.writes(notebook, fmt='py:percent')
-                    lang = 'python'
-                except Exception as e:
-                    content = f"# FAILED TO CONVERT NOTEBOOK: {path}\n# ERROR: {e}"
-                    print(f"Warning: Failed to convert {path}: {e}")
-            else:
-                content = f"# SKIPPING NOTEBOOK CONVERSION: jupytext not installed for {path}"
-                print(f"Warning: `jupytext` library not found. Skipping conversion for {path}.")
-        else:
-            try:
-                with open(full_path, 'r', encoding='utf-8') as f:
-                    content = f.read()
-                lang_map = {'.py': 'python', '.js': 'javascript', '.html': 'html', '.css': 'css', '.md': 'markdown', '.json': 'json', '.nix': 'nix', '.sh': 'bash'}
-                lang = lang_map.get(ext, 'text')
-            except Exception as e:
-                print(f"ERROR: Could not read or process {full_path}: {e}")
-                sys.exit(1)
-
-        processed_files_data.append({
-            "path": path, "comment": comment, "content": content,
-            "tokens": count_tokens(content), "words": count_words(content),
-            "lang": lang
-        })
-
-    # 3. Build the prompt and add auto-generated context
-    builder = PromptBuilder(processed_files_data, prompt_content, context_only=args.context_only)
-    
-    # --- Add the Codebase Tree ---
-    print("\n--- Generating Auto-Context ---")
-    print("Generating codebase tree diagram...", end='', flush=True)
-    tree_output = run_tree_command()
-    title = "Codebase Structure (eza --tree)"
-    builder.add_auto_context(title, tree_output)
-    if title in builder.auto_context:
-        token_count = builder.auto_context[title]['tokens']
-        print(f" ({token_count:,} tokens)")
-    else:
-        print(" (skipped)")
-
-
-    # --- Generate UML for all included Python files ---
-    python_files_to_diagram = [
-        f['path'] for f in processed_files_data if f['path'].endswith('.py')
-    ]
-
-    if python_files_to_diagram:
-        print("Python file(s) detected. Generating UML diagrams...")
-        for py_file_path in python_files_to_diagram:
-            print(f"  -> Generating for {py_file_path}...", end='', flush=True)
-            uml_context = generate_uml_and_dot(
-                target_file=py_file_path,
-                project_name=CONFIG["PROJECT_NAME"]
-            )
-            uml_content = uml_context.get("ascii_uml")
-            title = f"UML Class Diagram (ASCII for {py_file_path})"
-            builder.add_auto_context(title, uml_content)
-
-            if title in builder.auto_context:
-                token_count = builder.auto_context[title]['tokens']
-                print(f" ({token_count:,} tokens)")
-            elif uml_content and "note: no classes" in uml_content.lower():
-                print(" (no classes found)")
-            else:
-                print(" (skipped)")
-
-        print("...UML generation complete.\n")
-    
-    # 4. Generate final output and print summary
-    final_output = builder.build_final_prompt()
-    builder.print_summary()
-
-    # 5. Handle output
-    if args.output:
-        with open(args.output, 'w', encoding='utf-8') as f:
-            f.write(final_output)
-        print(f"Output written to '{args.output}'")
-    if not args.no_clipboard:
-        copy_to_clipboard(final_output)
+    """Main function to parse args, process files, and generate output."""
+    parser = argparse.ArgumentParser(description='Generate a Markdown context file for AI code assistance.')
+    parser.add_argument('prompt', nargs='?', default=None, help='A prompt string or path to a prompt file (e.g., prompt.md).')
+    parser.add_argument('-o', '--output', type=str, help='Optional: Output filename.')
+    parser.add_argument('--no-clipboard', action='store_true', help='Disable copying output to clipboard.')
+    parser.add_argument('--check-dependencies', action='store_true', help='Verify that all required external tools are installed.')
+    parser.add_argument('--context-only', action='store_true', help='Generate a context-only prompt without file contents.')
+    # --- NEW: Argument for including narrative context ---
+    parser.add_argument(
+        '-l', '--list',
+        nargs='?',
+        const='[-5:]',
+        default=None,
+        help='Include a list of recent articles. Optionally provide a slice, e.g., "[-10:]". Defaults to "[-5:]".'
+    )
+    args = parser.parse_args()
+
+    if args.check_dependencies:
+        check_dependencies()
+        sys.exit(0)
+
+    # 1. Handle user prompt
+    prompt_content = "Please review the provided context and assist with the codebase."
+    if args.prompt:
+        if os.path.exists(args.prompt):
+            with open(args.prompt, 'r', encoding='utf-8') as f:
+                prompt_content = f.read()
+        else:
+            prompt_content = args.prompt
+    elif os.path.exists("prompt.md"):
+        with open("prompt.md", 'r', encoding='utf-8') as f:
+            prompt_content = f.read()
+
+    # 2. Process all specified files
+    files_to_process = parse_file_list_from_config()
+    processed_files_data = []
+    print("--- Processing Files ---")
+    for path, comment in files_to_process:
+        full_path = os.path.join(REPO_ROOT, path) if not os.path.isabs(path) else path
+        if not os.path.exists(full_path):
+            print(f"Warning: File not found and will be skipped: {full_path}")
+            continue
+
+        content = ""
+        lang = "text"
+        ext = os.path.splitext(path)[1].lower()
+
+        if ext == '.ipynb':
+            if JUPYTEXT_AVAILABLE:
+                print(f"  -> Converting notebook: {path}")
+                try:
+                    notebook = jupytext.read(full_path)
+                    content = jupytext.writes(notebook, fmt='py:percent')
+                    lang = 'python'
+                except Exception as e:
+                    content = f"# FAILED TO CONVERT NOTEBOOK: {path}\n# ERROR: {e}"
+                    print(f"Warning: Failed to convert {path}: {e}")
+            else:
+                content = f"# SKIPPING NOTEBOOK CONVERSION: jupytext not installed for {path}"
+                print(f"Warning: `jupytext` library not found. Skipping conversion for {path}.")
+        else:
+            try:
+                with open(full_path, 'r', encoding='utf-8') as f:
+                    content = f.read()
+                lang_map = {'.py': 'python', '.js': 'javascript', '.html': 'html', '.css': 'css', '.md': 'markdown', '.json': 'json', '.nix': 'nix', '.sh': 'bash'}
+                lang = lang_map.get(ext, 'text')
+            except Exception as e:
+                print(f"ERROR: Could not read or process {full_path}: {e}")
+                sys.exit(1)
+
+        processed_files_data.append({
+            "path": path, "comment": comment, "content": content,
+            "tokens": count_tokens(content), "words": count_words(content),
+            "lang": lang
+        })
+
+    # 3. Build the prompt and add auto-generated context
+    builder = PromptBuilder(processed_files_data, prompt_content, context_only=args.context_only)
+    
+    # --- Add the Codebase Tree ---
+    print("\n--- Generating Auto-Context ---")
+    print("Generating codebase tree diagram...", end='', flush=True)
+    tree_output = run_tree_command()
+    title = "Codebase Structure (eza --tree)"
+    builder.add_auto_context(title, tree_output)
+    if title in builder.auto_context:
+        token_count = builder.auto_context[title]['tokens']
+        print(f" ({token_count:,} tokens)")
+    else:
+        print(" (skipped)")
+
+
+    # --- NEW: Add narrative context if requested ---
+    if args.list:
+        print("Adding narrative context from articles...", end='', flush=True)
+        all_articles = _get_article_list_data()
+        sliced_articles = []
+        try:
+            slice_or_index = parse_slice_arg(args.list)
+            if isinstance(slice_or_index, int):
+                sliced_articles = [all_articles[slice_or_index]]
+            elif isinstance(slice_or_index, slice):
+                sliced_articles = all_articles[slice_or_index]
+        except (ValueError, IndexError):
+            print(f" (invalid slice '{args.list}')")
+            sliced_articles = []
+
+        if sliced_articles:
+            narrative_content = "\n".join(
+                f"### {article['title']} ({article['date']})\n> {article['summary']}\n"
+                for article in sliced_articles
+            )
+            builder.add_auto_context("Recent Narrative Context", narrative_content)
+            print(f" ({len(sliced_articles)} articles)")
+        else:
+            print(" (no articles found or invalid slice)")
+
+
+    # --- Generate UML for all included Python files ---
+    python_files_to_diagram = [
+        f['path'] for f in processed_files_data if f['path'].endswith('.py')
+    ]
+
+    if python_files_to_diagram:
+        print("Python file(s) detected. Generating UML diagrams...")
+        for py_file_path in python_files_to_diagram:
+            print(f"  -> Generating for {py_file_path}...", end='', flush=True)
+            uml_context = generate_uml_and_dot(
+                target_file=py_file_path,
+                project_name=CONFIG["PROJECT_NAME"]
+            )
+            uml_content = uml_context.get("ascii_uml")
+            title = f"UML Class Diagram (ASCII for {py_file_path})"
+            builder.add_auto_context(title, uml_content)
+
+            if title in builder.auto_context:
+                token_count = builder.auto_context[title]['tokens']
+                print(f" ({token_count:,} tokens)")
+            elif uml_content and "note: no classes" in uml_content.lower():
+                print(" (no classes found)")
+            else:
+                print(" (skipped)")
+
+        print("...UML generation complete.\n")
+    
+    # 4. Generate final output and print summary
+    final_output = builder.build_final_prompt()
+    builder.print_summary()
+
+    # 5. Handle output
+    if args.output:
+        with open(args.output, 'w', encoding='utf-8') as f:
+            f.write(final_output)
+        print(f"Output written to '{args.output}'")
+    if not args.no_clipboard:
+        copy_to_clipboard(final_output)
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
\ No newline at end of file

[mike@nixos:~/repos/pipulate]$ 

[mike@nixos:~/repos/pipulate]$ git --no-pager diff
diff --git a/prompt_foo.py b/prompt_foo.py
index 5880cdc4..bf9257d0 100644
--- a/prompt_foo.py
+++ b/prompt_foo.py
@@ -27,644 +27,644 @@ except ImportError:
 # --- Configuration ---
 # ============================================================================
 def find_repo_root(start_path: str) -> str:
-    """Find the git repository root from a starting path."""
-    path = os.path.abspath(start_path)
-    while path != os.path.dirname(path):
-        if os.path.isdir(os.path.join(path, '.git')):
-            return path
-        path = os.path.dirname(path)
-    raise FileNotFoundError("Could not find the repository root (.git directory).")
+    """Find the git repository root from a starting path."""
+    path = os.path.abspath(start_path)
+    while path != os.path.dirname(path):
+        if os.path.isdir(os.path.join(path, '.git')):
+            return path
+        path = os.path.dirname(path)
+    raise FileNotFoundError("Could not find the repository root (.git directory).")
 
 REPO_ROOT = find_repo_root(os.path.dirname(__file__))
 
 # Centralized configuration as recommended in Architectural Analysis (Section VI-B)
 CONFIG = {
-    "PROJECT_NAME": "pipulate",
-    "POSTS_DIRECTORY": "/home/mike/repos/MikeLev.in/_posts" # NEW: For list_articles logic
+    "PROJECT_NAME": "pipulate",
+    "POSTS_DIRECTORY": "/home/mike/repos/MikeLev.in/_posts" # NEW: For list_articles logic
 }
 
 # ============================================================================
 # --- Accurate Literary Size Scale (Word Count Based) ---
 # ============================================================================
 LITERARY_SIZE_SCALE = [
-    (3000, "Short Essay"),
-    (7500, "Short Story"),
-    (20000, "Novelette"),
-    (50000, "Novella or a Master's Dissertation"),
-    (80000, "Average Paperback Novel or a Ph.D. Dissertation"),
-    (120000, "Long Novel"),
-    (200000, "Epic Fantasy Novel"),
-    (500000, "Seriously Long Epic (like 'Infinite Jest')"),
+    (3000, "Short Essay"),
+    (7500, "Short Story"),
+    (20000, "Novelette"),
+    (50000, "Novella or a Master's Dissertation"),
+    (80000, "Average Paperback Novel or a Ph.D. Dissertation"),
+    (120000, "Long Novel"),
+    (200000, "Epic Fantasy Novel"),
+    (500000, "Seriously Long Epic (like 'Infinite Jest')"),
 ]
 
 def get_literary_perspective(word_count: int, token_word_ratio: float) -> str:
-    """Get a human-readable literary comparison for the codebase size."""
-    description = f"Longer than {LITERARY_SIZE_SCALE[-1][1]}"
-    for words, desc in LITERARY_SIZE_SCALE:
-        if word_count <= words:
-            description = desc
-            break
-
-    density_warning = ""
-    if token_word_ratio > 1.8:
-        density_warning = (
-            f" (Note: With a token/word ratio of {token_word_ratio:.2f}, "
-            f"this content is far denser and more complex than typical prose of this length)."
-        )
-
-    return f"📚 Equivalent in length to a **{description}**{density_warning}"
+    """Get a human-readable literary comparison for the codebase size."""
+    description = f"Longer than {LITERARY_SIZE_SCALE[-1][1]}"
+    for words, desc in LITERARY_SIZE_SCALE:
+        if word_count <= words:
+            description = desc
+            break
+
+    density_warning = ""
+    if token_word_ratio > 1.8:
+        density_warning = (
+            f" (Note: With a token/word ratio of {token_word_ratio:.2f}, "
+            f"this content is far denser and more complex than typical prose of this length)."
+        )
+
+    return f"📚 Equivalent in length to a **{description}**{density_warning}"
 
 # ============================================================================
 # --- Restored & Corrected: UML and DOT Context Generation ---
 # ============================================================================
 def generate_uml_and_dot(target_file: str, project_name: str) -> Dict:
-    """Generates a UML ASCII diagram and a DOT dependency graph for a target Python file."""
-    pyreverse_exec = shutil.which("pyreverse")
-    plantuml_exec = shutil.which("plantuml")
-
-    if not pyreverse_exec or not plantuml_exec:
-        msg = []
-        if not pyreverse_exec: msg.append("`pyreverse` (from pylint)")
-        if not plantuml_exec: msg.append("`plantuml`")
-        return {"ascii_uml": f"Skipping: Required command(s) not found: {', '.join(msg)}."}
-
-    target_path = os.path.join(REPO_ROOT, target_file)
-    if not os.path.exists(target_path):
-        return {"ascii_uml": f"Skipping: Target file for UML generation not found: {target_path}"}
-
-    with tempfile.TemporaryDirectory() as temp_dir:
-        dot_file_path = os.path.join(temp_dir, "classes.dot")
-        puml_file_path = os.path.join(temp_dir, "diagram.puml")
-        
-        # --- Step 1: Run pyreverse ---
-        try:
-            pyreverse_cmd = [
-                pyreverse_exec,
-                "-f", "dot",
-                "-o", "dot", # This format is just a prefix
-                "-p", project_name,
-                target_path
-            ]
-            subprocess.run(
-                pyreverse_cmd,
-                check=True,
-                capture_output=True,
-                text=True,
-                cwd=temp_dir
-            )
-            generated_dot_name = f"classes_{project_name}.dot"
-            os.rename(os.path.join(temp_dir, generated_dot_name), dot_file_path)
-
-        except (subprocess.CalledProcessError, FileNotFoundError) as e:
-            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
-            return {"ascii_uml": f"Error: pyreverse failed. {error_msg}", "dot_graph": None}
-
-        # --- Step 2: Convert DOT to PlantUML ---
-        try:
-            graphs = pydot.graph_from_dot_file(dot_file_path)
-            if not graphs:
-                return {"ascii_uml": f"Note: No classes found in {target_file} to generate a diagram.", "dot_graph": None}
-            graph = graphs[0]
-            dot_content = graph.to_string()
-
-            puml_lines = ["@startuml", "skinparam linetype ortho", ""]
-
-            def sanitize_line(line):
-                clean = re.sub(r'<br[^>]*>', '', line)
-                clean = re.sub(r'<[^>]+>', '', clean)
-                return clean.strip()
-
-            for node in graph.get_nodes():
-                label = node.get_label()
-                if not label: continue
-
-                parts = label.strip('<>{} ').split('|')
-                class_name = sanitize_line(parts[0])
-                puml_lines.append(f"class {class_name} {{")
-
-                if len(parts) > 1:
-                    for attr in re.split(r'<br[^>]*>', parts[1]):
-                        clean_attr = sanitize_line(attr).split(':')[0].strip()
-                        if clean_attr:
-                            puml_lines.append(f"  - {clean_attr}")
-
-                if len(parts) > 2:
-                    method_block = parts[2].strip()
-                    for method_line in re.split(r'<br[^>]*>', method_block):
-                        clean_method = sanitize_line(method_line)
-                        if clean_method:
-                            puml_lines.append(f"  + {clean_method}")
-
-                puml_lines.append("}\n")
-
-            for edge in graph.get_edges():
-                source_name = edge.get_source().strip('"').split('.')[-1]
-                dest_name = edge.get_destination().strip('"').split('.')[-1]
-                puml_lines.append(f"{source_name} ..> {dest_name}")
-
-            puml_lines.append("@enduml")
-            with open(puml_file_path, 'w') as f:
-                f.write('\n'.join(puml_lines))
-
-        except Exception as e:
-            with open(dot_file_path, 'r') as f:
-                dot_content_on_error = f.read()
-            return {"ascii_uml": f"Error: DOT to PUML conversion failed. {str(e)}", "dot_graph": dot_content_on_error}
- 
-        # --- Step 3: Run PlantUML ---
-        try:
-            plantuml_cmd = ["plantuml", "-tutxt", puml_file_path]
-            subprocess.run(plantuml_cmd, check=True, capture_output=True, text=True, cwd=temp_dir)
-            
-            utxt_file_path = puml_file_path.replace(".puml", ".utxt")
-            with open(utxt_file_path, 'r') as f:
-                ascii_uml = f.read()
-            
-            # --- Normalize whitespace from plantuml output ---
-            lines = ascii_uml.splitlines()
-            non_empty_lines = [line for line in lines if line.strip()]
-            
-            if non_empty_lines:
-                min_indent = min(len(line) - len(line.lstrip(' ')) for line in non_empty_lines)
-                dedented_lines = [line[min_indent:] for line in lines]
-                stripped_lines = [line.rstrip() for line in dedented_lines]
-                ascii_uml = '\n'.join(stripped_lines)
-                
-                # Prepend a newline to "absorb the chop" from rendering
-                if ascii_uml:
-                    ascii_uml = '\n' + ascii_uml
-
-        except (subprocess.CalledProcessError, FileNotFoundError) as e:
-            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
-            return {"ascii_uml": f"Error: plantuml failed. {error_msg}", "dot_graph": dot_content}
-
-    return {"ascii_uml": ascii_uml, "dot_graph": dot_content}
+    """Generates a UML ASCII diagram and a DOT dependency graph for a target Python file."""
+    pyreverse_exec = shutil.which("pyreverse")
+    plantuml_exec = shutil.which("plantuml")
+
+    if not pyreverse_exec or not plantuml_exec:
+        msg = []
+        if not pyreverse_exec: msg.append("`pyreverse` (from pylint)")
+        if not plantuml_exec: msg.append("`plantuml`")
+        return {"ascii_uml": f"Skipping: Required command(s) not found: {', '.join(msg)}."}
+
+    target_path = os.path.join(REPO_ROOT, target_file)
+    if not os.path.exists(target_path):
+        return {"ascii_uml": f"Skipping: Target file for UML generation not found: {target_path}"}
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        dot_file_path = os.path.join(temp_dir, "classes.dot")
+        puml_file_path = os.path.join(temp_dir, "diagram.puml")
+        
+        # --- Step 1: Run pyreverse ---
+        try:
+            pyreverse_cmd = [
+                pyreverse_exec,
+                "-f", "dot",
+                "-o", "dot", # This format is just a prefix
+                "-p", project_name,
+                target_path
+            ]
+            subprocess.run(
+                pyreverse_cmd,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=temp_dir
+            )
+            generated_dot_name = f"classes_{project_name}.dot"
+            os.rename(os.path.join(temp_dir, generated_dot_name), dot_file_path)
+
+        except (subprocess.CalledProcessError, FileNotFoundError) as e:
+            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
+            return {"ascii_uml": f"Error: pyreverse failed. {error_msg}", "dot_graph": None}
+
+        # --- Step 2: Convert DOT to PlantUML ---
+        try:
+            graphs = pydot.graph_from_dot_file(dot_file_path)
+            if not graphs:
+                return {"ascii_uml": f"Note: No classes found in {target_file} to generate a diagram.", "dot_graph": None}
+            graph = graphs[0]
+            dot_content = graph.to_string()
+
+            puml_lines = ["@startuml", "skinparam linetype ortho", ""]
+
+            def sanitize_line(line):
+                clean = re.sub(r'<br[^>]*>', '', line)
+                clean = re.sub(r'<[^>]+>', '', clean)
+                return clean.strip()
+
+            for node in graph.get_nodes():
+                label = node.get_label()
+                if not label: continue
+
+                parts = label.strip('<>{} ').split('|')
+                class_name = sanitize_line(parts[0])
+                puml_lines.append(f"class {class_name} {{")
+
+                if len(parts) > 1:
+                    for attr in re.split(r'<br[^>]*>', parts[1]):
+                        clean_attr = sanitize_line(attr).split(':')[0].strip()
+                        if clean_attr:
+                            puml_lines.append(f"  - {clean_attr}")
+
+                if len(parts) > 2:
+                    method_block = parts[2].strip()
+                    for method_line in re.split(r'<br[^>]*>', method_block):
+                        clean_method = sanitize_line(method_line)
+                        if clean_method:
+                            puml_lines.append(f"  + {clean_method}")
+
+                puml_lines.append("}\n")
+
+            for edge in graph.get_edges():
+                source_name = edge.get_source().strip('"').split('.')[-1]
+                dest_name = edge.get_destination().strip('"').split('.')[-1]
+                puml_lines.append(f"{source_name} ..> {dest_name}")
+
+            puml_lines.append("@enduml")
+            with open(puml_file_path, 'w') as f:
+                f.write('\n'.join(puml_lines))
+
+        except Exception as e:
+            with open(dot_file_path, 'r') as f:
+                dot_content_on_error = f.read()
+            return {"ascii_uml": f"Error: DOT to PUML conversion failed. {str(e)}", "dot_graph": dot_content_on_error}
+ 
+        # --- Step 3: Run PlantUML ---
+        try:
+            plantuml_cmd = ["plantuml", "-tutxt", puml_file_path]
+            subprocess.run(plantuml_cmd, check=True, capture_output=True, text=True, cwd=temp_dir)
+            
+            utxt_file_path = puml_file_path.replace(".puml", ".utxt")
+            with open(utxt_file_path, 'r') as f:
+                ascii_uml = f.read()
+            
+            # --- Normalize whitespace from plantuml output ---
+            lines = ascii_uml.splitlines()
+            non_empty_lines = [line for line in lines if line.strip()]
+            
+            if non_empty_lines:
+                min_indent = min(len(line) - len(line.lstrip(' ')) for line in non_empty_lines)
+                dedented_lines = [line[min_indent:] for line in lines]
+                stripped_lines = [line.rstrip() for line in dedented_lines]
+                ascii_uml = '\n'.join(stripped_lines)
+                
+                # Prepend a newline to "absorb the chop" from rendering
+                if ascii_uml:
+                    ascii_uml = '\n' + ascii_uml
+
+        except (subprocess.CalledProcessError, FileNotFoundError) as e:
+            error_msg = e.stderr if hasattr(e, 'stderr') else str(e)
+            return {"ascii_uml": f"Error: plantuml failed. {error_msg}", "dot_graph": dot_content}
+
+    return {"ascii_uml": ascii_uml, "dot_graph": dot_content}
 
 
 # ============================================================================
 # --- NEW: Logic ported from list_articles.py for Narrative Context ---
 # ============================================================================
 def _get_article_list_data(posts_dir: str = CONFIG["POSTS_DIRECTORY"]) -> List[Dict]:
-    """
-    Parses Jekyll posts, sorts them chronologically, and returns a list of dictionaries.
-    This is a self-contained version of the logic from the `list_articles.py` script.
-    """
-    posts_data = []
-    if not os.path.isdir(posts_dir):
-        print(f"Warning: Article directory not found at {posts_dir}", file=sys.stderr)
-        return []
-
-    for filename in os.listdir(posts_dir):
-        if not filename.endswith(('.md', '.markdown')):
-            continue
-        filepath = os.path.join(posts_dir, filename)
-        try:
-            date_str = filename[:10]
-            post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
-            with open(filepath, 'r', encoding='utf-8') as f:
-                content = f.read()
-            if content.startswith('---'):
-                parts = content.split('---', 2)
-                front_matter = yaml.safe_load(parts[1]) or {}
-                posts_data.append({
-                    'path': filepath,
-                    'date': post_date,
-                    'sort_order': int(front_matter.get('sort_order', 0)),
-                    'title': front_matter.get('title', 'Untitled'),
-                    'summary': front_matter.get('meta_description', '')
-                })
-        except (ValueError, yaml.YAMLError, IndexError):
-            continue
-
-    sorted_posts = sorted(posts_data, key=lambda p: (p['date'], p['sort_order']))
-    return sorted_posts
+    """
+    Parses Jekyll posts, sorts them chronologically, and returns a list of dictionaries.
+    This is a self-contained version of the logic from the `list_articles.py` script.
+    """
+    posts_data = []
+    if not os.path.isdir(posts_dir):
+        print(f"Warning: Article directory not found at {posts_dir}", file=sys.stderr)
+        return []
+
+    for filename in os.listdir(posts_dir):
+        if not filename.endswith(('.md', '.markdown')):
+            continue
+        filepath = os.path.join(posts_dir, filename)
+        try:
+            date_str = filename[:10]
+            post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
+            with open(filepath, 'r', encoding='utf-8') as f:
+                content = f.read()
+            if content.startswith('---'):
+                parts = content.split('---', 2)
+                front_matter = yaml.safe_load(parts[1]) or {}
+                posts_data.append({
+                    'path': filepath,
+                    'date': post_date,
+                    'sort_order': int(front_matter.get('sort_order', 0)),
+                    'title': front_matter.get('title', 'Untitled'),
+                    'summary': front_matter.get('meta_description', '')
+                })
+        except (ValueError, yaml.YAMLError, IndexError):
+            continue
+
+    sorted_posts = sorted(posts_data, key=lambda p: (p['date'], p['sort_order']))
+    return sorted_posts
 
 def parse_slice_arg(arg_str: str):
-    """Parses a string like '[5:10]' or '[5]' into a slice object or an int."""
-    if not arg_str or not arg_str.startswith('[') or not arg_str.endswith(']'):
-        return None
-    content = arg_str[1:-1].strip()
-    if ':' in content:
-        parts = content.split(':', 1)
-        start = int(parts[0].strip()) if parts[0].strip() else None
-        end = int(parts[1].strip()) if parts[1].strip() else None
-        return slice(start, end)
-    elif content:
-        return int(content)
-    return None
+    """Parses a string like '[5:10]' or '[5]' into a slice object or an int."""
+    if not arg_str or not arg_str.startswith('[') or not arg_str.endswith(']'):
+        return None
+    content = arg_str[1:-1].strip()
+    if ':' in content:
+        parts = content.split(':', 1)
+        start = int(parts[0].strip()) if parts[0].strip() else None
+        end = int(parts[1].strip()) if parts[1].strip() else None
+        return slice(start, end)
+    elif content:
+        return int(content)
+    return None
 
 # ============================================================================
 # --- Helper Functions (Tokenizing, File Parsing, Clipboard) ---
 # ============================================================================
 def count_tokens(text: str, model: str = "gpt-4o") -> int:
-    """Counts tokens in a text string using tiktoken."""
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-        return len(encoding.encode(text))
-    except Exception:
-        return len(text.split())
+    """Counts tokens in a text string using tiktoken."""
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+        return len(encoding.encode(text))
+    except Exception:
+        return len(text.split())
 
 def count_words(text: str) -> int:
-    """Counts words in a text string."""
-    return len(text.split())
+    """Counts words in a text string."""
+    return len(text.split())
 
 def parse_file_list_from_config() -> List[Tuple[str, str]]:
-    """Loads and parses the file list from foo_files.py."""
-    try:
-        import foo_files
-        files_raw = foo_files.FILES_TO_INCLUDE_RAW
-    except (ImportError, AttributeError):
-        print("ERROR: foo_files.py not found or doesn't contain FILES_TO_INCLUDE_RAW.")
-        sys.exit(1)
-
-    lines = files_raw.strip().splitlines()
-    seen_files, parsed_files = set(), []
-
-    for line in lines:
-        line = line.strip()
-        if not line or line.startswith('#'):
-            continue
-        parts = re.split(r'\s*<--\s*|\s*#\s*', line, 1)
-        file_path = parts[0].strip()
-        comment = parts[1].strip() if len(parts) > 1 else ""
-
-        if file_path and file_path not in seen_files:
-            seen_files.add(file_path)
-            parsed_files.append((file_path, comment))
-    return parsed_files
+    """Loads and parses the file list from foo_files.py."""
+    try:
+        import foo_files
+        files_raw = foo_files.FILES_TO_INCLUDE_RAW
+    except (ImportError, AttributeError):
+        print("ERROR: foo_files.py not found or doesn't contain FILES_TO_INCLUDE_RAW.")
+        sys.exit(1)
+
+    lines = files_raw.strip().splitlines()
+    seen_files, parsed_files = set(), []
+
+    for line in lines:
+        line = line.strip()
+        if not line or line.startswith('#'):
+            continue
+        parts = re.split(r'\s*<--\s*|\s*#\s*', line, 1)
+        file_path = parts[0].strip()
+        comment = parts[1].strip() if len(parts) > 1 else ""
+
+        if file_path and file_path not in seen_files:
+            seen_files.add(file_path)
+            parsed_files.append((file_path, comment))
+    return parsed_files
 
 def copy_to_clipboard(text: str):
-    """Copies text to the system clipboard using 'xclip'."""
-    if not shutil.which('xclip'):
-        print("\nWarning: 'xclip' not found. Cannot copy to clipboard.")
-        return
-    try:
-        subprocess.run(['xclip', '-selection', 'clipboard'], input=text.encode('utf-8'), check=True)
-        print("Markdown output copied to clipboard")
-    except Exception as e:
-        print(f"\nWarning: Could not copy to clipboard: {e}")
+    """Copies text to the system clipboard using 'xclip'."""
+    if not shutil.which('xclip'):
+        print("\nWarning: 'xclip' not found. Cannot copy to clipboard.")
+        return
+    try:
+        subprocess.run(['xclip', '-selection', 'clipboard'], input=text.encode('utf-8'), check=True)
+        print("Markdown output copied to clipboard")
+    except Exception as e:
+        print(f"\nWarning: Could not copy to clipboard: {e}")
 
 def run_tree_command() -> str:
-    """Runs the 'eza' command to generate a tree view that respects .gitignore."""
-    eza_exec = shutil.which("eza")
-    if not eza_exec:
-        return "Skipping: `eza` command not found."
-    
-    try:
-        result = subprocess.run(
-            [eza_exec, '--tree', '--git-ignore', '--color=never'],
-            capture_output=True,
-            text=True,
-            cwd=REPO_ROOT,
-            check=True
-        )
-        return result.stdout
-    except subprocess.CalledProcessError as e:
-        return f"Error running eza command: {e.stderr}"
-    except Exception as e:
-        return f"An unexpected error occurred while running eza: {str(e)}"
+    """Runs the 'eza' command to generate a tree view that respects .gitignore."""
+    eza_exec = shutil.which("eza")
+    if not eza_exec:
+        return "Skipping: `eza` command not found."
+    
+    try:
+        result = subprocess.run(
+            [eza_exec, '--tree', '--git-ignore', '--color=never'],
+            capture_output=True,
+            text=True,
+            cwd=REPO_ROOT,
+            check=True
+        )
+        return result.stdout
+    except subprocess.CalledProcessError as e:
+        return f"Error running eza command: {e.stderr}"
+    except Exception as e:
+        return f"An unexpected error occurred while running eza: {str(e)}"
 
 def check_dependencies():
-    """Verifies that all required external command-line tools are installed."""
-    print("Checking for required external dependencies...")
-    dependencies = {
-        "pyreverse": "Provided by `pylint`. Install with: pip install pylint",
-        "plantuml": "A Java-based tool. See https://plantuml.com/starting",
-        "eza": "A modern replacement for `ls`. See https://eza.rocks/install",
-        "xclip": "Clipboard utility for Linux. Install with your package manager (e.g., sudo apt-get install xclip)",
-    }
-    missing = []
-    
-    for tool, instructions in dependencies.items():
-        if not shutil.which(tool):
-            missing.append((tool, instructions))
-    
-    if not missing:
-        print("✅ All dependencies found.")
-    else:
-        print("\n❌ Missing dependencies detected:")
-        for tool, instructions in missing:
-            print(f"  - Command not found: `{tool}`")
-            print(f"    ↳ {instructions}")
-        print("\nPlease install the missing tools and ensure they are in your system's PATH.")
-        sys.exit(1)
+    """Verifies that all required external command-line tools are installed."""
+    print("Checking for required external dependencies...")
+    dependencies = {
+        "pyreverse": "Provided by `pylint`. Install with: pip install pylint",
+        "plantuml": "A Java-based tool. See https://plantuml.com/starting",
+        "eza": "A modern replacement for `ls`. See https://eza.rocks/install",
+        "xclip": "Clipboard utility for Linux. Install with your package manager (e.g., sudo apt-get install xclip)",
+    }
+    missing = []
+    
+    for tool, instructions in dependencies.items():
+        if not shutil.which(tool):
+            missing.append((tool, instructions))
+    
+    if not missing:
+        print("✅ All dependencies found.")
+    else:
+        print("\n❌ Missing dependencies detected:")
+        for tool, instructions in missing:
+            print(f"  - Command not found: `{tool}`")
+            print(f"    ↳ {instructions}")
+        print("\nPlease install the missing tools and ensure they are in your system's PATH.")
+        sys.exit(1)
 
 # ============================================================================
 # --- Intelligent PromptBuilder Class ---
 # ============================================================================
 class PromptBuilder:
-    """
-    Builds a complete, structured Markdown prompt including file manifests,
-    auto-generated context, file contents, and the user's final prompt.
-    """
-    def __init__(self, processed_files: List[Dict], prompt_text: str, context_only: bool = False):
-        self.processed_files = processed_files
-        self.prompt_text = prompt_text
-        self.context_only = context_only
-        self.auto_context = {}
-
-    def add_auto_context(self, title: str, content: str):
-        """Adds auto-generated context like UML diagrams to the prompt."""
-        if content and "error" not in content.lower() and "skipping" not in content.lower():
-            self.auto_context[title] = {
-                'content': content,
-                'tokens': count_tokens(content),
-                'words': count_words(content)
-            }
-
-    def _generate_manifest_header(self) -> str:
-        lines = ["# Codebase Context & Manifest", ""]
-        for f in self.processed_files:
-            purpose = f" ({f['comment']})" if f['comment'] else ""
-            token_display = f" ({f['tokens']:,} tokens)" if not self.context_only else ""
-            lines.append(f"- **{f['path']}**{purpose}{token_display}")
-        return "\n".join(lines)
-
-    def _generate_auto_context_section(self) -> str:
-        if not self.auto_context:
-            return ""
-        lines = ["", "---", "", "# Auto-Generated Context", ""]
-        
-        # --- NEW: Prioritize printing Narrative Context first ---
-        narrative_title = "Recent Narrative Context"
-        if narrative_title in self.auto_context:
-            content = self.auto_context[narrative_title]['content']
-            lines.append(f"## {narrative_title}")
-            lines.append(content.strip())
-            lines.append("") # Add a blank line for spacing
-
-        if "Codebase Structure (eza --tree)" in self.auto_context:
-            title = "Codebase Structure (eza --tree)"
-            content = self.auto_context[title]['content']
-            lines.append(f"## {title}")
-            lines.append("```text")
-            lines.append(content.strip())
-            lines.append("```")
-        
-        for title, data in self.auto_context.items():
-            if title not in ["Codebase Structure (eza --tree)", narrative_title]:
-                lines.append(f"## {title}")
-                lines.append("```text")
-                lines.append(data['content'])
-                lines.append("```")
-        return "\n".join(lines)
-
-    def _generate_file_contents(self) -> str:
-        lines = []
-        for f in self.processed_files:
-            lines.append(f"```{f['lang']}:{f['path']}")
-            lines.append(f['content'])
-            lines.append("```")
-            lines.append(f"\n# End of {f['path']}\n")
-        return "\n".join(lines)
-
-    def _generate_ai_checklist(self) -> str:
-        """Creates a standardized checklist for the AI to verify the prompt's context."""
-        return '''
+    """
+    Builds a complete, structured Markdown prompt including file manifests,
+    auto-generated context, file contents, and the user's final prompt.
+    """
+    def __init__(self, processed_files: List[Dict], prompt_text: str, context_only: bool = False):
+        self.processed_files = processed_files
+        self.prompt_text = prompt_text
+        self.context_only = context_only
+        self.auto_context = {}
+
+    def add_auto_context(self, title: str, content: str):
+        """Adds auto-generated context like UML diagrams to the prompt."""
+        if content and "error" not in content.lower() and "skipping" not in content.lower():
+            self.auto_context[title] = {
+                'content': content,
+                'tokens': count_tokens(content),
+                'words': count_words(content)
+            }
+
+    def _generate_manifest_header(self) -> str:
+        lines = ["# Codebase Context & Manifest", ""]
+        for f in self.processed_files:
+            purpose = f" ({f['comment']})" if f['comment'] else ""
+            token_display = f" ({f['tokens']:,} tokens)" if not self.context_only else ""
+            lines.append(f"- **{f['path']}**{purpose}{token_display}")
+        return "\n".join(lines)
+
+    def _generate_auto_context_section(self) -> str:
+        if not self.auto_context:
+            return ""
+        lines = ["", "---", "", "# Auto-Generated Context", ""]
+        
+        # --- NEW: Prioritize printing Narrative Context first ---
+        narrative_title = "Recent Narrative Context"
+        if narrative_title in self.auto_context:
+            content = self.auto_context[narrative_title]['content']
+            lines.append(f"## {narrative_title}")
+            lines.append(content.strip())
+            lines.append("") # Add a blank line for spacing
+
+        if "Codebase Structure (eza --tree)" in self.auto_context:
+            title = "Codebase Structure (eza --tree)"
+            content = self.auto_context[title]['content']
+            lines.append(f"## {title}")
+            lines.append("```text")
+            lines.append(content.strip())
+            lines.append("```")
+        
+        for title, data in self.auto_context.items():
+            if title not in ["Codebase Structure (eza --tree)", narrative_title]:
+                lines.append(f"## {title}")
+                lines.append("```text")
+                lines.append(data['content'])
+                lines.append("```")
+        return "\n".join(lines)
+
+    def _generate_file_contents(self) -> str:
+        lines = []
+        for f in self.processed_files:
+            lines.append(f"```{f['lang']}:{f['path']}")
+            lines.append(f['content'])
+            lines.append("```")
+            lines.append(f"\n# End of {f['path']}\n")
+        return "\n".join(lines)
+
+    def _generate_ai_checklist(self) -> str:
+        """Creates a standardized checklist for the AI to verify the prompt's context."""
+        return '''
 # AI Self-Correction Checklist
 
 Before addressing the user's prompt, perform the following verification steps:
 
-1.  **Review the Manifest vs. Codebase Structure:** Compare the file list in the manifest against the `eza --tree` output. Are there any obviously related, critical files missing from the manifest that would be necessary to understand the user's request? (e.g., if `core.py` is present, is `__init__.py` also present?).
-2.  **Analyze Import Statements:** Briefly scan the `import` statements in the provided Python files. Do they suggest dependencies on local files that were *not* included in the manifest?
-3.  **Check for Incompleteness:** If you determine that critical files are missing, do not proceed. Instead, your **primary task** is to inform me of the missing files and recommend adding them to `foo_files.py` to create a more complete context.
-4.  **Confirm Understanding:** If the context appears complete, state "Context verified." and then proceed with the user's request.
+1.  **Review the Manifest vs. Codebase Structure:** Compare the file list in the manifest against the `eza --tree` output. Are there any obviously related, critical files missing from the manifest that would be necessary to understand the user's request? (e.g., if `core.py` is present, is `__init__.py` also present?).
+2.  **Analyze Import Statements:** Briefly scan the `import` statements in the provided Python files. Do they suggest dependencies on local files that were *not* included in the manifest?
+3.  **Check for Incompleteness:** If you determine that critical files are missing, do not proceed. Instead, your **primary task** is to inform me of the missing files and recommend adding them to `foo_files.py` to create a more complete context.
+4.  **Confirm Understanding:** If the context appears complete, state "Context verified." and then proceed with the user's request.
 '''
 
-    def build_final_prompt(self) -> str:
-        """Assembles all parts into the final Markdown string."""
-        parts = [
-            self._generate_manifest_header(),
-            self._generate_auto_context_section(),
-        ]
-
-        if not self.context_only:
-            parts.extend([
-                "\n---\n\n# File Contents\n",
-                self._generate_file_contents(),
-            ])
-
-        parts.extend([
-            "---\n\n# User Prompt\n",
-            self._generate_ai_checklist(),
-            self.prompt_text
-        ])
-        
-        return "\n".join(filter(None, parts))
-
-    def print_summary(self):
-        """Calculates and prints an accurate, comprehensive summary to the console."""
-        # --- Calculate token counts for all components ---
-        manifest_str = self._generate_manifest_header()
-        manifest_tokens = count_tokens(manifest_str)
-        manifest_words = count_words(manifest_str)
-        
-        prompt_tokens = count_tokens(self.prompt_text)
-        prompt_words = count_words(self.prompt_text)
-
-        checklist_str = self._generate_ai_checklist()
-        checklist_tokens = count_tokens(checklist_str)
-        checklist_words = count_words(checklist_str)
-
-        auto_context_total_tokens = sum(v['tokens'] for v in self.auto_context.values())
-        auto_context_total_words = sum(v['words'] for v in self.auto_context.values())
-
-        file_content_total_tokens = sum(f['tokens'] for f in self.processed_files)
-        file_content_total_words = sum(f['words'] for f in self.processed_files)
-
-        # --- Display the breakdown ---
-        print("--- Files Included ---")
-        for f in self.processed_files:
-            if self.context_only:
-                print(f"• {f['path']} (content omitted)")
-            else:
-                print(f"• {f['path']} ({f['tokens']:,} tokens)")
-        
-        if self.auto_context:
-            print("\n--- Auto-Context Included ---")
-            for title, data in self.auto_context.items():
-                print(f"• {title} ({data['tokens']:,} tokens)")
-
-        # --- Calculate and display the final summary ---
-        print("\n--- Prompt Summary ---")
-        if self.context_only:
-            print("NOTE: Running in --context-only mode. File contents are excluded.")
-            total_tokens = manifest_tokens + auto_context_total_tokens + prompt_tokens + checklist_tokens
-            total_words = manifest_words + auto_context_total_words + prompt_words + checklist_words
-        else:
-            total_tokens = manifest_tokens + auto_context_total_tokens + file_content_total_tokens + prompt_tokens + checklist_tokens
-            total_words = manifest_words + auto_context_total_words + file_content_total_words + prompt_words + checklist_words
-
-        print(f"Total Tokens: {total_tokens:,}")
-        print(f"Total Words:  {total_words:,}")
-
-        ratio = total_tokens / total_words if total_words > 0 else 0
-        perspective = get_literary_perspective(total_words, ratio)
-        print("\n--- Size Perspective ---")
-        print(perspective)
-        print()
+    def build_final_prompt(self) -> str:
+        """Assembles all parts into the final Markdown string."""
+        parts = [
+            self._generate_manifest_header(),
+            self._generate_auto_context_section(),
+        ]
+
+        if not self.context_only:
+            parts.extend([
+                "\n---\n\n# File Contents\n",
+                self._generate_file_contents(),
+            ])
+
+        parts.extend([
+            "---\n\n# User Prompt\n",
+            self._generate_ai_checklist(),
+            self.prompt_text
+        ])
+        
+        return "\n".join(filter(None, parts))
+
+    def print_summary(self):
+        """Calculates and prints an accurate, comprehensive summary to the console."""
+        # --- Calculate token counts for all components ---
+        manifest_str = self._generate_manifest_header()
+        manifest_tokens = count_tokens(manifest_str)
+        manifest_words = count_words(manifest_str)
+        
+        prompt_tokens = count_tokens(self.prompt_text)
+        prompt_words = count_words(self.prompt_text)
+
+        checklist_str = self._generate_ai_checklist()
+        checklist_tokens = count_tokens(checklist_str)
+        checklist_words = count_words(checklist_str)
+
+        auto_context_total_tokens = sum(v['tokens'] for v in self.auto_context.values())
+        auto_context_total_words = sum(v['words'] for v in self.auto_context.values())
+
+        file_content_total_tokens = sum(f['tokens'] for f in self.processed_files)
+        file_content_total_words = sum(f['words'] for f in self.processed_files)
+
+        # --- Display the breakdown ---
+        print("--- Files Included ---")
+        for f in self.processed_files:
+            if self.context_only:
+                print(f"• {f['path']} (content omitted)")
+            else:
+                print(f"• {f['path']} ({f['tokens']:,} tokens)")
+        
+        if self.auto_context:
+            print("\n--- Auto-Context Included ---")
+            for title, data in self.auto_context.items():
+                print(f"• {title} ({data['tokens']:,} tokens)")
+
+        # --- Calculate and display the final summary ---
+        print("\n--- Prompt Summary ---")
+        if self.context_only:
+            print("NOTE: Running in --context-only mode. File contents are excluded.")
+            total_tokens = manifest_tokens + auto_context_total_tokens + prompt_tokens + checklist_tokens
+            total_words = manifest_words + auto_context_total_words + prompt_words + checklist_words
+        else:
+            total_tokens = manifest_tokens + auto_context_total_tokens + file_content_total_tokens + prompt_tokens + checklist_tokens
+            total_words = manifest_words + auto_context_total_words + file_content_total_words + prompt_words + checklist_words
+
+        print(f"Total Tokens: {total_tokens:,}")
+        print(f"Total Words:  {total_words:,}")
+
+        ratio = total_tokens / total_words if total_words > 0 else 0
+        perspective = get_literary_perspective(total_words, ratio)
+        print("\n--- Size Perspective ---")
+        print(perspective)
+        print()
 
 # ============================================================================
 # --- Main Execution Logic ---
 # ============================================================================
 def main():
-    """Main function to parse args, process files, and generate output."""
-    parser = argparse.ArgumentParser(description='Generate a Markdown context file for AI code assistance.')
-    parser.add_argument('prompt', nargs='?', default=None, help='A prompt string or path to a prompt file (e.g., prompt.md).')
-    parser.add_argument('-o', '--output', type=str, help='Optional: Output filename.')
-    parser.add_argument('--no-clipboard', action='store_true', help='Disable copying output to clipboard.')
-    parser.add_argument('--check-dependencies', action='store_true', help='Verify that all required external tools are installed.')
-    parser.add_argument('--context-only', action='store_true', help='Generate a context-only prompt without file contents.')
-    # --- NEW: Argument for including narrative context ---
-    parser.add_argument(
-        '-l', '--list',
-        nargs='?',
-        const='[-5:]',
-        default=None,
-        help='Include a list of recent articles. Optionally provide a slice, e.g., "[-10:]". Defaults to "[-5:]".'
-    )
-    args = parser.parse_args()
-
-    if args.check_dependencies:
-        check_dependencies()
-        sys.exit(0)
-
-    # 1. Handle user prompt
-    prompt_content = "Please review the provided context and assist with the codebase."
-    if args.prompt:
-        if os.path.exists(args.prompt):
-            with open(args.prompt, 'r', encoding='utf-8') as f:
-                prompt_content = f.read()
-        else:
-            prompt_content = args.prompt
-    elif os.path.exists("prompt.md"):
-        with open("prompt.md", 'r', encoding='utf-8') as f:
-            prompt_content = f.read()
-
-    # 2. Process all specified files
-    files_to_process = parse_file_list_from_config()
-    processed_files_data = []
-    print("--- Processing Files ---")
-    for path, comment in files_to_process:
-        full_path = os.path.join(REPO_ROOT, path) if not os.path.isabs(path) else path
-        if not os.path.exists(full_path):
-            print(f"Warning: File not found and will be skipped: {full_path}")
-            continue
-
-        content = ""
-        lang = "text"
-        ext = os.path.splitext(path)[1].lower()
-
-        if ext == '.ipynb':
-            if JUPYTEXT_AVAILABLE:
-                print(f"  -> Converting notebook: {path}")
-                try:
-                    notebook = jupytext.read(full_path)
-                    content = jupytext.writes(notebook, fmt='py:percent')
-                    lang = 'python'
-                except Exception as e:
-                    content = f"# FAILED TO CONVERT NOTEBOOK: {path}\n# ERROR: {e}"
-                    print(f"Warning: Failed to convert {path}: {e}")
-            else:
-                content = f"# SKIPPING NOTEBOOK CONVERSION: jupytext not installed for {path}"
-                print(f"Warning: `jupytext` library not found. Skipping conversion for {path}.")
-        else:
-            try:
-                with open(full_path, 'r', encoding='utf-8') as f:
-                    content = f.read()
-                lang_map = {'.py': 'python', '.js': 'javascript', '.html': 'html', '.css': 'css', '.md': 'markdown', '.json': 'json', '.nix': 'nix', '.sh': 'bash'}
-                lang = lang_map.get(ext, 'text')
-            except Exception as e:
-                print(f"ERROR: Could not read or process {full_path}: {e}")
-                sys.exit(1)
-
-        processed_files_data.append({
-            "path": path, "comment": comment, "content": content,
-            "tokens": count_tokens(content), "words": count_words(content),
-            "lang": lang
-        })
-
-    # 3. Build the prompt and add auto-generated context
-    builder = PromptBuilder(processed_files_data, prompt_content, context_only=args.context_only)
-    
-    # --- Add the Codebase Tree ---
-    print("\n--- Generating Auto-Context ---")
-    print("Generating codebase tree diagram...", end='', flush=True)
-    tree_output = run_tree_command()
-    title = "Codebase Structure (eza --tree)"
-    builder.add_auto_context(title, tree_output)
-    if title in builder.auto_context:
-        token_count = builder.auto_context[title]['tokens']
-        print(f" ({token_count:,} tokens)")
-    else:
-        print(" (skipped)")
-
-
-    # --- NEW: Add narrative context if requested ---
-    if args.list:
-        print("Adding narrative context from articles...", end='', flush=True)
-        all_articles = _get_article_list_data()
-        sliced_articles = []
-        try:
-            slice_or_index = parse_slice_arg(args.list)
-            if isinstance(slice_or_index, int):
-                sliced_articles = [all_articles[slice_or_index]]
-            elif isinstance(slice_or_index, slice):
-                sliced_articles = all_articles[slice_or_index]
-        except (ValueError, IndexError):
-            print(f" (invalid slice '{args.list}')")
-            sliced_articles = []
-
-        if sliced_articles:
-            narrative_content = "\n".join(
-                f"### {article['title']} ({article['date']})\n> {article['summary']}\n"
-                for article in sliced_articles
-            )
-            builder.add_auto_context("Recent Narrative Context", narrative_content)
-            print(f" ({len(sliced_articles)} articles)")
-        else:
-            print(" (no articles found or invalid slice)")
-
-
-    # --- Generate UML for all included Python files ---
-    python_files_to_diagram = [
-        f['path'] for f in processed_files_data if f['path'].endswith('.py')
-    ]
-
-    if python_files_to_diagram:
-        print("Python file(s) detected. Generating UML diagrams...")
-        for py_file_path in python_files_to_diagram:
-            print(f"  -> Generating for {py_file_path}...", end='', flush=True)
-            uml_context = generate_uml_and_dot(
-                target_file=py_file_path,
-                project_name=CONFIG["PROJECT_NAME"]
-            )
-            uml_content = uml_context.get("ascii_uml")
-            title = f"UML Class Diagram (ASCII for {py_file_path})"
-            builder.add_auto_context(title, uml_content)
-
-            if title in builder.auto_context:
-                token_count = builder.auto_context[title]['tokens']
-                print(f" ({token_count:,} tokens)")
-            elif uml_content and "note: no classes" in uml_content.lower():
-                print(" (no classes found)")
-            else:
-                print(" (skipped)")
-
-        print("...UML generation complete.\n")
-    
-    # 4. Generate final output and print summary
-    final_output = builder.build_final_prompt()
-    builder.print_summary()
-
-    # 5. Handle output
-    if args.output:
-        with open(args.output, 'w', encoding='utf-8') as f:
-            f.write(final_output)
-        print(f"Output written to '{args.output}'")
-    if not args.no_clipboard:
-        copy_to_clipboard(final_output)
+    """Main function to parse args, process files, and generate output."""
+    parser = argparse.ArgumentParser(description='Generate a Markdown context file for AI code assistance.')
+    parser.add_argument('prompt', nargs='?', default=None, help='A prompt string or path to a prompt file (e.g., prompt.md).')
+    parser.add_argument('-o', '--output', type=str, help='Optional: Output filename.')
+    parser.add_argument('--no-clipboard', action='store_true', help='Disable copying output to clipboard.')
+    parser.add_argument('--check-dependencies', action='store_true', help='Verify that all required external tools are installed.')
+    parser.add_argument('--context-only', action='store_true', help='Generate a context-only prompt without file contents.')
+    # --- NEW: Argument for including narrative context ---
+    parser.add_argument(
+        '-l', '--list',
+        nargs='?',
+        const='[-5:]',
+        default=None,
+        help='Include a list of recent articles. Optionally provide a slice, e.g., "[-10:]". Defaults to "[-5:]".'
+    )
+    args = parser.parse_args()
+
+    if args.check_dependencies:
+        check_dependencies()
+        sys.exit(0)
+
+    # 1. Handle user prompt
+    prompt_content = "Please review the provided context and assist with the codebase."
+    if args.prompt:
+        if os.path.exists(args.prompt):
+            with open(args.prompt, 'r', encoding='utf-8') as f:
+                prompt_content = f.read()
+        else:
+            prompt_content = args.prompt
+    elif os.path.exists("prompt.md"):
+        with open("prompt.md", 'r', encoding='utf-8') as f:
+            prompt_content = f.read()
+
+    # 2. Process all specified files
+    files_to_process = parse_file_list_from_config()
+    processed_files_data = []
+    print("--- Processing Files ---")
+    for path, comment in files_to_process:
+        full_path = os.path.join(REPO_ROOT, path) if not os.path.isabs(path) else path
+        if not os.path.exists(full_path):
+            print(f"Warning: File not found and will be skipped: {full_path}")
+            continue
+
+        content = ""
+        lang = "text"
+        ext = os.path.splitext(path)[1].lower()
+
+        if ext == '.ipynb':
+            if JUPYTEXT_AVAILABLE:
+                print(f"  -> Converting notebook: {path}")
+                try:
+                    notebook = jupytext.read(full_path)
+                    content = jupytext.writes(notebook, fmt='py:percent')
+                    lang = 'python'
+                except Exception as e:
+                    content = f"# FAILED TO CONVERT NOTEBOOK: {path}\n# ERROR: {e}"
+                    print(f"Warning: Failed to convert {path}: {e}")
+            else:
+                content = f"# SKIPPING NOTEBOOK CONVERSION: jupytext not installed for {path}"
+                print(f"Warning: `jupytext` library not found. Skipping conversion for {path}.")
+        else:
+            try:
+                with open(full_path, 'r', encoding='utf-8') as f:
+                    content = f.read()
+                lang_map = {'.py': 'python', '.js': 'javascript', '.html': 'html', '.css': 'css', '.md': 'markdown', '.json': 'json', '.nix': 'nix', '.sh': 'bash'}
+                lang = lang_map.get(ext, 'text')
+            except Exception as e:
+                print(f"ERROR: Could not read or process {full_path}: {e}")
+                sys.exit(1)
+
+        processed_files_data.append({
+            "path": path, "comment": comment, "content": content,
+            "tokens": count_tokens(content), "words": count_words(content),
+            "lang": lang
+        })
+
+    # 3. Build the prompt and add auto-generated context
+    builder = PromptBuilder(processed_files_data, prompt_content, context_only=args.context_only)
+    
+    # --- Add the Codebase Tree ---
+    print("\n--- Generating Auto-Context ---")
+    print("Generating codebase tree diagram...", end='', flush=True)
+    tree_output = run_tree_command()
+    title = "Codebase Structure (eza --tree)"
+    builder.add_auto_context(title, tree_output)
+    if title in builder.auto_context:
+        token_count = builder.auto_context[title]['tokens']
+        print(f" ({token_count:,} tokens)")
+    else:
+        print(" (skipped)")
+
+
+    # --- NEW: Add narrative context if requested ---
+    if args.list:
+        print("Adding narrative context from articles...", end='', flush=True)
+        all_articles = _get_article_list_data()
+        sliced_articles = []
+        try:
+            slice_or_index = parse_slice_arg(args.list)
+            if isinstance(slice_or_index, int):
+                sliced_articles = [all_articles[slice_or_index]]
+            elif isinstance(slice_or_index, slice):
+                sliced_articles = all_articles[slice_or_index]
+        except (ValueError, IndexError):
+            print(f" (invalid slice '{args.list}')")
+            sliced_articles = []
+
+        if sliced_articles:
+            narrative_content = "\n".join(
+                f"### {article['title']} ({article['date']})\n> {article['summary']}\n"
+                for article in sliced_articles
+            )
+            builder.add_auto_context("Recent Narrative Context", narrative_content)
+            print(f" ({len(sliced_articles)} articles)")
+        else:
+            print(" (no articles found or invalid slice)")
+
+
+    # --- Generate UML for all included Python files ---
+    python_files_to_diagram = [
+        f['path'] for f in processed_files_data if f['path'].endswith('.py')
+    ]
+
+    if python_files_to_diagram:
+        print("Python file(s) detected. Generating UML diagrams...")
+        for py_file_path in python_files_to_diagram:
+            print(f"  -> Generating for {py_file_path}...", end='', flush=True)
+            uml_context = generate_uml_and_dot(
+                target_file=py_file_path,
+                project_name=CONFIG["PROJECT_NAME"]
+            )
+            uml_content = uml_context.get("ascii_uml")
+            title = f"UML Class Diagram (ASCII for {py_file_path})"
+            builder.add_auto_context(title, uml_content)
+
+            if title in builder.auto_context:
+                token_count = builder.auto_context[title]['tokens']
+                print(f" ({token_count:,} tokens)")
+            elif uml_content and "note: no classes" in uml_content.lower():
+                print(" (no classes found)")
+            else:
+                print(" (skipped)")
+
+        print("...UML generation complete.\n")
+    
+    # 4. Generate final output and print summary
+    final_output = builder.build_final_prompt()
+    builder.print_summary()
+
+    # 5. Handle output
+    if args.output:
+        with open(args.output, 'w', encoding='utf-8') as f:
+            f.write(final_output)
+        print(f"Output written to '{args.output}'")
+    if not args.no_clipboard:
+        copy_to_clipboard(final_output)
 
 
 if __name__ == "__main__":
-    main()
+    main()

[mike@nixos:~/repos/pipulate]$