SkillSphere

SkillSphere ⚡️

Graph-powered talent intelligence in < 10 min

Turn scattered career notes into a live knowledge graph — and one-click, ATS-ready résumés.

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🌟 Why you'll care

Problem	SkillSphere's answer
Career data everywhere — LinkedIn, slides, docs	Markdown → Neo4j hypergraph (one source of truth)
Generic CVs don't win roles	Graph-query → Job-specific résumé PDF
LLM privacy & cost nerves	Runs fully local on Ollama, no API keys
Need proof of my graph/AI chops	This repo is the demo — explore the live graph or read the code

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🚀 30‑second taste

git clone https://github.com/bprager/SkillSphere.git
cd SkillSphere && ./scripts/quick_start.sh   # builds graph + sample résumé
open output/resume_google.pdf

Full install & config instructions live in docs/installation.md.

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🔍 See it in action

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🧩 Inside the box

• Hypergraph‑of‑Thought model → Neo4j + Node2Vec embeddings
• Gleaning loop wrings 25 % extra facts from each chunk
• Graph→Markdown→PDF pipeline for recruiter‑ready résumés
• 100 % unit‑tested core modules

Deep‑dive architecture and research notes are in docs/architecture.md.

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📋 Skill Extraction & Documentation Guide

For LLM-Assisted Repository Analysis

This repository is structured to systematically capture and organize technical skills from GitHub repositories. Use these instructions to analyze repositories and extract valuable skill information.

🎯 Analysis Objectives

When analyzing a GitHub repository, your goal is to:

1. Identify Technical Skills: Extract concrete technical competencies demonstrated in the codebase
2. Categorize by Domain: Organize skills into appropriate categories (software, hardware, creative, research)
3. Document Systematically: Create consistent, comprehensive skill records
4. Maintain Quality: Ensure all records follow the established template structure

📂 Repository Structure

SkillSphere/
├── ingestion_docs/
│   ├── skills/                    # All skill documentation
│   │   ├── software/             # Programming & technical skills
│   │   ├── hardware/             # Physical systems & electronics
│   │   ├── creative/             # Design & multimedia skills
│   │   └── research/             # Self-directed learning & experiments
│   ├── jobs/                     # Professional experience records
│   ├── certs/                    # Certification documentation
│   └── extras/                   # Additional context files
├── templates/
│   ├── complementary_skills_template.md     # Main skill documentation template
│   └── complementary_skills_quick_reference.md  # YAML metadata reference
└── docs/                         # Architecture & system documentation

🔍 Skill Extraction Process

Step 1: Repository Analysis

• Analyze README.md, architecture documentation, and pyproject.toml/package.json
• Identify programming languages, frameworks, databases, and deployment technologies
• Look for unique or advanced technical implementations
• Note any custom solutions or sophisticated integrations

Step 2: Skill Categorization

Use this decision matrix:

Category	Criteria	Examples
software/	Programming, APIs, databases, cloud, DevOps	Python, FastAPI, Neo4j, Docker, Kubernetes
hardware/	Physical systems, electronics, embedded	PCB design, 3D printing, IoT, embedded systems
creative/	Design, multimedia, content creation	Video editing, graphic design, documentation
research/	Experiments, prototypes, learning projects	ML research, proof-of-concepts, explorations

Step 3: Prioritization Framework

• High Priority: Core technologies central to the project's functionality
• Medium Priority: Supporting technologies that enable the main features
• Low Priority: Configuration tools and standard development utilities

Step 4: Documentation Creation

For each identified skill, create a comprehensive record using the template structure.

📝 Template Usage

Primary Template: templates/complementary_skills_template.md

• Use this for full skill documentation
• Include all sections: Overview, Learning Journey, Projects, Competencies
• Focus on transferable skills and professional relevance

Quick Reference: templates/complementary_skills_quick_reference.md

• Contains YAML metadata structure for each skill
• Use for consistent categorization and tagging
• Update when adding new skills

📋 File Naming Convention

Use descriptive filenames that include the main technology:

Software Skills:

• neo4j-hypergraphs.md (database + specific application)
• fastapi-web-services.md (framework + application type)
• docker-orchestration.md (tool + specific use case)

Hardware Skills:

• pcb-design-kicad.md (skill + primary tool)
• 3d-printing-bambulab.md (process + specific equipment)

Creative Skills:

• video-editing-davinci.md (skill + software)
• technical-writing-documentation.md (skill + application)

🔧 Quality Standards

Required Information:

• Technical depth and complexity level
• Specific tools and technologies used
• Practical projects or implementations
• Professional relevance and transferability
• Learning progression and current proficiency

Metadata Requirements:

title: "Clear, Descriptive Skill Name"
type: "software_skill|hardware_skill|creative_skill|research_skill"
category: "specific_domain"
entity_id: "skill_unique_identifier"
primary_tools: ["Tool1", "Tool2", "Tool3"]
technologies: ["Tech1", "Tech2", "Tech3"]
competencies: ["skill1", "skill2", "skill3"]

🎯 LLM Analysis Prompt Template

Use this prompt structure for repository analysis:

Analyze this GitHub repository and extract technical skills for documentation:

1. **Repository Assessment**: Examine the codebase, documentation, and dependencies
2. **Skill Identification**: Identify 3-5 high-priority technical skills demonstrated
3. **Categorization**: Determine the appropriate category (software/hardware/creative/research)
4. **Documentation**: Create comprehensive skill records using the provided template
5. **Integration**: Update the quick reference with new skill metadata

Focus on:
- Advanced or specialized technical implementations
- Technologies central to the project's core functionality
- Skills that demonstrate professional-level competency
- Unique combinations of technologies or innovative approaches

Create detailed documentation that showcases technical depth and practical application.

📊 Integration with SkillSphere System

Documented skills are processed by the hypergraph system to:

• Create semantic connections between related competencies
• Generate job-specific résumé content
• Identify skill gaps and learning opportunities
• Demonstrate continuous learning and technical growth

The hypergraph processes markdown files to extract entities, relationships, and competency mappings for intelligent career intelligence.

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🤝 Work with me

I design & build graph‑driven AI solutions that make talent, knowledge and content searchable & actionable. If that sparks ideas for your team:

• Book a 30‑min chat
• Connect on LinkedIn
• Say hi via email: bernd@prager.ws

Let's turn your data into an unfair advantage.

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© 2025 Bernd Prager — Apache 2.0 Clone it, fork it, improve it — and tell me what you build!