GitFoundry 🦀 (Determinism Meets Talent Intelligence)

GitFoundry is a high-performance talent intelligence engine built entirely in C. It bypasses the "Black Box" HR AI by evaluating engineering candidates using verified GitHub engineering signals and a Deterministic Rule Engine.

Instead of parsing keywords from a resume, SignalC analyzes depth of contribution, commitment patterns, and architectural complexity to classify developers by their true engineering value.

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🧠 Core Philosophy

"Deterministic logic for evaluation. AI models for interpretation."

Most talent platforms fail because they treat candidate evaluation as a fuzzy AI problem. SignalC makes every core decision through a transparent, rule-based scoring pipeline, using LLMs only to translate these outputs into human-readable narratives.

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⚙️ Key Features

• 📈 GitHub Intelligence Pipeline: Analyzing commit_depth and contribution_patterns to identify hard-to-fake technical talent.
• 🏗️ Behavioral Classification: Automatic labeling of developers as Builder, Explorer, or Maintainer based on historical patterns.
• 🗺️ Skill Adjacency Graph: A graph-based proximity model (~70 relationships) that estimates a candidate's Time-to-Productivity for a new stack.
• 🗣️ Interpreted AI Summaries: Translates deterministic metrics into a qualitative profile summary.

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📂 Architecture

SignalC/
├── include/
│   └── talent_engine.h      # Core Data Structures & Interfaces
├── src/
│   ├── main.c              # Test Harness & Entry Point
│   ├── scoring_logic.c     # Deterministic Rule Engine
│   ├── skill_graph.c       # Adjacency Matrix & Proximity Logic
│   └── ai_interpreter.c    # AI Narrative Generator
└── Makefile                # High-performance Build System

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🛠️ Installation & Build

Prerequisites

• GCC compiler
• libcurl (for GitHub API integration)

Quick Start

# Clone the repository (once uploaded)
git clone https://github.com/aadit/SignalC.git
cd SignalC

# Build the system
make

# Run the test harness
./bin/talent_intel your_github_username

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🧪 How it Works

1. GitHub Analysis: Scans repository frequency and commit "depth" (lines of code per commit).
2. Scoring Logic: If commit_depth > 100, the candidate is flagged as an Architectural Builder.
3. Graph Traversal: If a candidate knows C, the graph calculates the distance to Rust or Systems to estimate learning curves.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.