High-performance Python code flow analysis with NLP query processing - CFG, DFG, call graphs, and intelligent code queries

Goal - Automated git push with enterprise-grade commit intelligence, smart conventional commit generation based on deep code analysis, and interactive release workflow management.

curllm - Intelligent browser automation with local LLMs (Qwen, Llama, Mistral) for data extraction and form automation using a dynamic LLM-DSL architecture without hardcoded selectors.

Toonic — Universal TOON Format Platform for LLM-optimized file representations

NLP2CMD - Transforms natural language into domain-specific commands (SQL, Shell, Docker, Kubernetes) using a multi-layered detection pipeline and thermodynamic optimization.

Zero-config AI cost calculator per commit/model with liteLLM

Closed-loop automation across semcod/ repositories.

     

Regression Index — detect and measure code quality regressions between git versions

Pyqual Demo

Intelligent LLM model router driven by real code metrics — successor to preLLM

WUP (What's Up) - Intelligent file watcher for regression testing in large projects

High-performance Python code flow analysis with optimized TOON format - CFG, DFG, call graphs, and intelligent code queries

Declarative quality gate loops for AI-assisted development

Generate the next 10 project tasks from project state, git history and an LLM prompt.

Progressive algorithmization toolchain — from LLM to deterministic code, from proxy to tickets

Visual Intent Contract Orchestrator: freeze project slices, evolve capsules, verify intent contracts.

Generate environment maps (services, artifacts, env vars) for LLM decision-making

Analyze test value, coverage, duplication, and generate LLM planfiles

A complete toolkit for validating LLM-generated code