# 907 Feature Structure Analyzer 51a3dc46

> Requirements for the PDF structure analysis and content detection module.

- Skill: `tools-only/907-feature-structure-analyzer-51a3dc46` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/907-feature-structure-analyzer-51a3dc46`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/907-feature-structure-analyzer-51a3dc46/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/907-feature-structure-analyzer-51a3dc46

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# Feature Requirements: Structure Analyzer

## Problem Statement
PDF files are essentially "flat" collections of text blocks with position coordinates. They lack semantic structure such as Chapters, Sections, Paragraphs, Lists, and Footnotes. To create a high-quality, navigable EPUB, we must deduce this structure from visual cues (font size, bold weight, spacing) and textual patterns. Without this, the EPUB would be a single continuous stream of text without a Table of Contents or proper formatting.

## Goals & Objectives
- **Automated Structure Detection**: Identify high-level structure (Chapters, Sub-chapters) to generate a Table of Contents (ToC).
- **Semantic Tagging**: Classify text blocks into semantic roles: `Paragraph`, `Heading (H1-H3)`, `List Item`, `Quote`, `Footnote`.
- **Footnote Linking**: Associate footnote markers in the text (e.g., `[1]`, `*`) with their corresponding content at the bottom of the page/chapter.
- **Configurable Heuristics**: Allow users to tweak detection rules (e.g., "Headers must be >14pt bold") via configuration.

### Non-Goals
- **OCR**: We assume the PDF has a text layer (or is pre-processed).
- **Complex Layouts (v1)**: We focus on single/double-column text. Complex magazine-style layouts with floating boxes are best-effort.
- **Image Analysis**: We do not analyze images for content, only their position.

## User Stories
1. **Chapter Detection**: "As a user, I want chapter titles to be automatically recognized so that my EPUB has a working Table of Contents."
2. **Hierarchy Preservation**: "As a reader, I want to see distinct formatting for main headings vs sub-headings to understand the book's structure."
3. **Footnote Navigation**: "As a scholar, I want to click on a footnote number and jump to the definition, then jump back, instead of manually scrolling."
4. **Noise Filtering**: "As a user, I want the system to automatically identify and remove repetitive headers/footers (e.g., 'Page X of Y', 'My Book Title') so they don't interrupt the reading flow."

## Success Criteria
- **Header Recall**: >90% of explicit chapter headers detected correctly in standard fiction/non-fiction layouts.
- **ToC Generation**: Generated Table of Contents matches the visual structure of the PDF.
- **Footnote Linking**: >80% of standard numerical footnotes (`1`, `[1]`) are correctly linked to their definitions.
- **Performance**: Analysis of a 300-page book takes < 30 seconds.

## Constraints & Assumptions
- **Input**: List of `TextBlock` objects from `PDFExtractor` (with font/position data).
- **Heuristics**: Detection relies on the consistency of the PDF styling (e.g., all H1s use the same font).
- **Scope**: Footnotes may range from "bottom of page" to "end of chapter".

## Data Models (Draft)
```python
@dataclass
class StructuredBlock:
    text: str
    role: str  # "p", "h1", "h2", "li", "footnote", "quote"
    level: int = 0
    id: str = None  # ToC anchor or Footnote ID
```

## Questions & Open Items
- How do we handle "Run-in heads" (bold text at start of paragraph)?
- Handling multi-line headers? (Merge logic needed).
- Detecting "drop caps" (large first letter)?