Add CareerOps Comprehensive Technical Report (82 pages, A4 PDF)

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Generates an 80-page A4 technical reference document covering the full CareerOps system architecture, components, and implementation details — requested by the issue.

What's added

• docs/CareerOps-Technical-Report.html — Source document with A4 CSS layout, running headers/footers, professional typography (Space Grotesk + DM Sans via self-hosted WOFF2), code blocks, architecture diagrams, and structured tables
• docs/CareerOps-Technical-Report.pdf — Rendered output (82 pages, 621 KB) produced by the existing generate-pdf.mjs Playwright pipeline with ATS Unicode normalization (89 em-dash replacements)

Report structure

Chapter	Coverage
Front Matter	Cover, Executive Summary, TOC, Abbreviations
1 — Introduction	System overview, use cases, architecture diagram, tech stack
2 — Core Components	All 14 skill modes with token estimates, batch processing with orchestrator/worker flow, Go TUI dashboard internals
3 — Data Management	Schema specs, data contract (user vs system layer), merge/dedup mechanics
4 — Processing & Generation	PDF/ATS pipeline (normalizeTextForATS masking algorithm), pattern analysis, all npm run scripts
5 — Monitoring & Health	Liveness detection cascade, CV sync, 7-rule pipeline verification
6 — Configuration & Deployment	Profile/portals/states YAML schemas, Nix flake, dependency surface (2 prod deps)
7 — Output & Reporting	Report header metadata format, file naming conventions, CV template design system
8 — Documentation & Community	AGENTS.md API spec, CLAUDE.md session init logic, contribution/governance model
9 — Implementation Guide	Quick start sequence, best practices, troubleshooting table, roadmap

No existing files were modified.

Original prompt

CareerOps Comprehensive Technical Report - 80 Pages A4 Format

Create a comprehensive technical report document in PDF format (80 pages, A4 size) that provides a complete overview of the CareerOps system according to the following structure:

Report Structure (80 Pages):

Front Matter (Pages 1-5)

• Cover Page
• Executive Summary
• Table of Contents
• Document Metadata

1. Introduction (Pages 6-12)

• 1.1 System Overview
• 1.2 Purpose & Use Cases
• 1.3 Key Features
• 1.4 Architecture Overview
• 1.5 Technology Stack

2. Core Components (Pages 13-32)

• 2.1 Agent System (/modes) - 14 Skill Modes
  • Overview of skill-based architecture
  • All 14 modes explained: apply, auto-pipeline, batch, contacto, deep, followup, interview-prep, oferta, ofertas, patterns, pdf, pipeline, project, scan, tracker, training
  • Language support (EN, ES, DE, FR, JA, PT-BR, RU)
• 2.2 Batch Processing (/batch)
  • Parallel evaluation engine
  • Worker orchestration
  • State management and resumability
• 2.3 Dashboard (/dashboard) - Go-based visualization
  • TUI architecture
  • Real-time metrics
  • Interactive pipeline management

3. Data Management (Pages 33-42)

• 3.1 Data Schema and Structures
• 3.2 Data Contract (User vs System layer)
• 3.3 Tracking & Deduplication
• 3.4 Normalization and Integrity

4. Processing & Generation (Pages 43-50)

• 4.1 PDF Generation with ATS Optimization
• 4.2 Pattern Analysis
• 4.3 System Utilities and Maintenance

5. Monitoring & Health (Pages 51-56)

• 5.1 Liveness Checks
• 5.2 CV Sync Verification
• 5.3 Pipeline Verification
• 5.4 Cadence Management

6. Configuration & Deployment (Pages 57-64)

• 6.1 Environment Setup
• 6.2 Configuration Files
• 6.3 Nix Flake Setup
• 6.4 Package Dependencies and Installation

7. Output & Reporting (Pages 65-70)

• 7.1 Generated Reports Format
• 7.2 Output Files Structure
• 7.3 PDF Templates and Design
• 7.4 Font Resources

8. Documentation & Community (Pages 71-76)

• 8.1 API Specification
• 8.2 Claude Integration
• 8.3 Development Guide
• 8.4 Community and Governance

9. Implementation Guide (Pages 77-80)

• 9.1 Quick Start Setup
• 9.2 Best Practices
• 9.3 Troubleshooting
• 9.4 Roadmap and Future Work

Report Requirements:

• Professional academic/technical report format
• 80 pages, A4 size (210x297mm)
• Proper pagination with headers/footers
• Table of contents with page numbers
• Detailed paragraphs (not bullet points) for main sections
• Code examples in code blocks where appropriate
• Tables for structured information
• Figures and diagrams descriptions
• Bibliography and references
• Professional typography and formatting
• Approximately 1500-2000 words per major section

Generate this as a formatted document that can be exported as PDF.

The following is the prior conversation context from the user's chat exploration (may be truncated):

User: Abstract
Acknowledgements
Contents
List of Figures
List of Abbreviations

1. Introduction
   1.1 Background
   1.2 Motivation
   1.3 Problem Statement
   1.4 Objectives
   1.5 Existed System
   1.6 Future Scope & Pros and Cons
2. Literature Survey
3. Preprocessing
4. Methodology
   4.1 Dataset Collection
   4.2 Used Model Architecture
   4.2.1 Convolutional Neural Network
   4.2.2 Working of Convolutional Neural Network
   4.2.3 Convolutional Layer
   4.2.4 Pooling Layer
   4.2.5 Fully Connected Layer
   4.3 Pre-trained CNN Model
   4.3.1 VGGNet Architecture
   4.4 Preprocessing
   4.5 Labelling
   4.6 Data Augmentation
   4.7 Feature Extraction
   4.8 Neural Network Training
   4.9 Activation Function Used
   4.10 Loss Function Used
   4.11 Accuracy Calculation
5. Design
   5.1 Design Goals
   5.2 Design the System Architecture
   5.3 Architecture Diagram
   5.4 CNN Uses Cases
   6 Implementation
   6.1 Implementation Strategy
   6.2 Hardware Platform Used
   6.3 Libraries & Software Used
6. Result & Discussion
7. Conclusion
8. Future Work & Social Impact
   10 References

chnage table according to codebase

Assistant: I'd be happy to help you update the table of contents to match your codebase structure. However, I need to first explore the repository to understand what files and components actually exist in your dev0jha/CareerOps project.

Let me examine the repository structure:

Assistant: Based on the CareerOps repository structure, here's an updated table of contents that aligns with the actual codebase rather than the academic research paper format you provided:

# CareerOps - AI-Powered Job Search System

## Table of Contents

1. **Introduction**
   - 1.1 System Overview
   - 1.2 Purpose & Use Cases
   - 1.3 Key Features
   - 1.4 Architecture

2. **Core Components**
   - 2.1 Agent System (`/modes`) - 14 Skill Modes
   - 2.2 Batch Processing (`/batch`)
   - 2.3 Dashboard (`/dashboard`) - Go-based visualization
   - 2.4 Job Description Processing (`/jds`)
   - 2.5 Interview Preparation (`/interview-prep`)

3. **Data Management**
   - 3.1 Data Schema (`/data`)
   - 3.2 Data Contract (`DATA_CONTRACT.md`)
   - 3.3 Tracking & Dedu...

</details>



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