Format Resume Skill

Purpose

Format CV/resume content with intelligent style application based on semantic context. Uses a clean template with 19 semantic styles and learns from your corrections over time.

Usage

Format new content:

Format this CV: [paste content]

Format from file:

Format my-cv-draft.txt as a resume

How It Works

Key Principle: Intelligence lives in the skill (Claude analyzes text). Python code is just helpers.

Semantic Analysis: I (Claude) analyze your raw text to understand what each element represents

• I don't use regex patterns - I understand semantic context

• "Anthony Byrnes" at top → CV Name

• "EDUCATION" in caps → Section Header

• "2023 - Present California State University" → Timeline Entry + Institution

• "Romeo & Juliet" in productions → Play Title (not Job Title, even though both are bold italic!)

Create JSON Structure: Convert raw text to structured format with style assignments

User Confirmation: Show you my analysis, get confirmation or corrections

Apply Learned Patterns: Python helpers apply previously learned preferences

Generate Document: Create formatted .docx using template with page headers

Visual Preview: Show PDF preview images for review

Learn from Corrections: If you correct a style choice, I save it to learned-preferences.yaml

The 19 Styles

Paragraph Styles:

• CV Name - Your name at top

• Contact Name - Bold, 10pt (cover letter header)

• Contact Info - Regular, 10pt (phone/email)

• Recipient Address - Regular, 11pt (organization address)

• RE Line - Bold, 13pt, ORANGE (position/subject line)

• Date Line - Right-aligned, 11pt (document date)

• Section Header - Bold, ORANGE (11pt for CV, 13pt for cover letter)

• Body Text - Standard paragraphs, 11pt

• Timeline Entry - Date + institution with hanging indent (CV only)

• Page Header - Bold, 10pt (page 2+ headers)

• Bullet Standard, Bullet Gray, Bullet Emphasis - List variations

Character Styles (inline):

• Play Title - Bold italic for productions (most common!)

• Institution - Bold for school/company names

• Job Title - Bold italic for positions

• Orange Emphasis - Highlight text

• Gray Text - Dates, secondary info

Note: Shares template with format-cover-letter skill (career-documents-template.docx)

Workflow

Step 1: You Provide Raw Text

User: "Format this CV: [pastes raw text]"

Step 2: I (Claude) Analyze and Create JSON

I analyze your content using semantic understanding (not regex):

I see:

• Name: "ANTHONY BYRNES" → CV Name style
• Contact: "T: 213.305.3132" → Contact Info style
• Section: "EDUCATION" → Section Header (orange, 11pt)
• Timeline: "2020-2024 California State University, Long Beach" → Timeline Entry
  • inline: "California State University, Long Beach" → Institution (bold)
• Position: "Interim Associate Dean, College of the Arts" → Body Text
  • inline: "Interim Associate Dean" → Job Title (bold italic)
• Bullet: "Led $29M budget..." → Bullet Standard

Here's my JSON interpretation: { "document_metadata": { "type": "cv", "author_name": "ANTHONY BYRNES", "document_title": "Curriculum Vitae", "page_header": { "enabled": true, "left": "ANTHONY BYRNES - Curriculum Vitae", "right": "page" } }, "content": [ {"text": "ANTHONY BYRNES", "style": "CV Name"}, {"text": "T: 213.305.3132", "style": "Contact Info"}, ... ] }

Looks correct? (yes/show json/corrections)

Step 3: You Confirm or Correct

• Say "yes" → I proceed to formatting

• Say "Line 5 should be Gray Text" → I update JSON

• Say "show json" → I display full JSON for manual editing

Step 4: Apply Learned Patterns

Python helpers load learned-preferences.yaml and apply any previously learned style rules:

from cv_formatting.learning_system import LearningSystem

learning = LearningSystem() content = learning.apply_learned_patterns(content)

This is simple pattern matching - the intelligence came from your previous corrections.

Step 5: Generate Document

I save the JSON and call Python helpers to create formatted .docx:

import json import subprocess from pathlib import Path

Save JSON structure

mapping_file = "/tmp/cv_mapping.json" with open(mapping_file, 'w') as f: json.dump(content_mapping, f, indent=2)

Format document (Python helper just applies styles from JSON)

result = subprocess.run([ "python3", "format_cv.py", mapping_file, output_path, "--document-type", "cv", "--preview" ], capture_output=True, text=True, cwd=str(Path.home() / "PycharmProjects/career-lexicon-builder"))

Step 6: Visual Preview

I convert to PDF and show you page images.

You can request changes: "That committee role should be gray text"

Step 7: Learn (if corrections made)

If you make corrections, I save them to learned-preferences.yaml:

learning.learn_correction( text="Graduate Studies Advisory Committee", context="service section", preferred_style="Gray Text" )

Next time I analyze a CV, I'll automatically suggest Gray Text for committee roles.

Step 8: Finalize

Once you're happy with the preview, I save the final document (e.g., "anthony-byrnes-cv.docx").

LLM-Based Analysis vs. Regex Patterns

Why LLM analysis (me, Claude) is better:

• ✅ Understands semantic context (not just patterns)

• ✅ Handles variations naturally ("PhD" vs "Ph.D." vs "Doctor of Philosophy")

• ✅ Distinguishes context: "Dean" in experience → Job Title, "Dean" in service → Gray Text

• ✅ Can explain reasoning to you

• ✅ Learns from corrections through conversation

Python helpers only:

• Load defaults from YAML

• Apply previously learned patterns (simple matching)

• Find files, generate dates

• NO semantic analysis in Python code

Example: Bold Italic Context Discrimination

I distinguish based on semantic context, not just formatting:

• "Interim Associate Dean" after "2023 - Present CSULB" → Job Title (follows employer)

• "Romeo & Juliet" in productions list → Play Title (in artistic works context)

Traditional regex can't do this because both are bold italic. I understand the context.

Learning System

Three-layer learning stored in learned-preferences.yaml :

Layer 1: Style Corrections

style_rules:

• pattern: "committee" context: "service section" preferred_style: "Gray Text" learned_date: "2025-11-11" example: "Graduate Studies Advisory Committee"

Layer 2: Metadata Patterns

metadata_defaults: document_title: "Curriculum Vitae" # not "Resume" page_header_enabled: true date_format: "MMMM YYYY"

Layer 3: Section Patterns

section_patterns: EDUCATION: order: ["Timeline Entry", "Body Text", "Bullet Standard"] inline_institution: true

Corrections are automatically saved. No "save preferences" button needed.

You: "That should be gray text, it's a committee role"

Me: "✓ Updated formatting rules: committee roles → Gray Text"

Next CV: Automatically applies gray to committee roles without asking.

Error Handling

If template missing:

• "Template not found. Run generate_cv_template.py first"

If LibreOffice/Poppler unavailable:

• "PDF preview not available (LibreOffice not installed)"

• "Continuing with .docx only..."

If style mapping unclear:

• "I'm not sure if this is a Job Title or Play Title - can you clarify?"

Files

• career-documents-template.docx

• Shared template with 19 semantic styles

• format_cv.py

• Main formatting script

• style-mappings.yaml

• Base semantic inference rules

• learned-preferences.yaml

• Your accumulated corrections

Page Headers for Multi-Page CVs

CVs spanning multiple pages automatically get professional headers on page 2+:

• Format: "ANTHONY BYRNES - Curriculum Vitae page 2"

• Style: Helvetica 10pt, bold, gray (RGB 128, 128, 128)

• Alignment: Left text aligns with body, right text at 6.5 inches

• Clean first page: No header on page 1

• Same implementation as cover letters (already working!)

Metadata Inference

Python helpers load from defaults.yaml and generate page header config:

from cv_formatting.metadata_inference import MetadataHelper

helper = MetadataHelper() metadata = helper.infer_cv_metadata(content)

Returns:

{ "type": "cv", "author_name": "ANTHONY BYRNES", "document_title": "Curriculum Vitae", "last_updated": "November 2025", "version": "Academic", "page_header": { "enabled": true, "left": "ANTHONY BYRNES - Curriculum Vitae", "right": "page" } }

Detects:

• Contact Information: From defaults.yaml

• Document Title: "Curriculum Vitae", "Resume", or "CV" (from defaults or learned)

• Version: Academic, Industry, Arts, or General (simple keyword detection)

• Page Header Config: Generated automatically from template

Configuration Files

defaults.yaml - Your contact info and preferences:

contact: name: "ANTHONY BYRNES" phone: "213.305.3132" email: "anthonybyrnes@mac.com"

cv_defaults: document_title: "Curriculum Vitae" page_header: enabled: true format: "{name} - {title}"

preferences: version: "Academic"

learned-preferences.yaml - Three-layer learning system:

style_rules: [] # Layer 1: Style corrections metadata_defaults: {} # Layer 2: Metadata patterns section_patterns: {} # Layer 3: Section structures

JSON Export for Wrapper Application

After successfully formatting the resume and creating the output files, export structured metadata.

Write to: resume-formatted-v1.json (increment version if exists)

Implementation:

• Check if resume-formatted-v1.json already exists in output directory

• If exists, increment version number: resume-formatted-v2.json , resume-formatted-v3.json , etc.

• Extract formatting metadata from the JSON structure and output files

• Write JSON file with proper formatting (2-space indentation)

• Save to same directory as formatted resume

Structure:

{ "metadata": { "created_at": "YYYY-MM-DDTHH:MM:SSZ", "version": 1, "skill": "format-resume", "input_file": "resume-content.md", "output_file": "resume-formatted.pdf" }, "formatting_metadata": { "template": "career-documents-template.docx", "document_type": "cv", "font": "Helvetica", "font_size": 11, "margins": { "top": 0.5, "bottom": 0.5, "left": 0.5, "right": 0.5 }, "line_spacing": 1.15, "section_spacing": 0.15, "page_header": { "enabled": true, "left": "ANTHONY BYRNES - Curriculum Vitae", "right": "page" } }, "sections": [ { "name": "Contact Information", "content_preview": "ANTHONY BYRNES\nT: 213.305.3132\nE: anthonybyrnes@mac.com", "styles_used": ["CV Name", "Contact Info"], "validation": "passed", "issues": [] }, { "name": "Professional Experience", "entries": 5, "styles_used": ["Section Header", "Timeline Entry", "Body Text", "Bullet Standard"], "validation": "passed", "issues": [] }, { "name": "Education", "entries": 3, "styles_used": ["Section Header", "Timeline Entry", "Body Text"], "validation": "passed", "issues": [] } ], "validation_results": { "total_checks": 15, "passed": 14, "failed": 0, "warnings": 1, "issues": [ { "type": "warning", "section": "Professional Summary", "message": "Summary slightly long (95 words, recommend <90)", "severity": "low" } ] }, "metrics": { "page_count": 2, "word_count": 542, "section_count": 7, "estimated_read_time_seconds": 130, "ats_compatibility_score": 0.92, "readability_score": 0.88, "visual_appeal_score": 0.90 }, "style_analysis": { "total_styles_used": 12, "paragraph_styles": ["CV Name", "Contact Info", "Section Header", "Timeline Entry", "Body Text", "Bullet Standard", "Bullet Gray"], "character_styles": ["Institution", "Job Title", "Play Title", "Gray Text"], "learned_corrections_applied": 3, "manual_overrides": 0 }, "output_files": [ { "type": "pdf", "path": "anthony-byrnes-cv.pdf", "size_bytes": 245632, "created_at": "YYYY-MM-DDTHH:MM:SSZ" }, { "type": "docx", "path": "anthony-byrnes-cv.docx", "size_bytes": 123456, "created_at": "YYYY-MM-DDTHH:MM:SSZ" } ] }

Present to user:

Formatting complete! Saved to:

• anthony-byrnes-cv.docx (formatted document)
• anthony-byrnes-cv.pdf (preview)
• resume-formatted-v1.json (formatting metadata)

Formatting summary:

• Pages: 2
• Sections: 7
• Styles used: 12 (7 paragraph, 5 character)
• Validation: 14/15 checks passed (1 warning)
• ATS compatibility: 92%

Would you like to:

1. Review the PDF preview
2. Make style corrections
3. Export a different format

Related

• Design: docs/plans/2025-11-09-cv-style-extraction-and-formatting-design.md

• CV Enhancements: docs/plans/2025-11-11-cv-formatting-enhancements-design.md

• Template generation: python generate_cv_template.py

• Validation: python validate_template.py