Autonomous AI agent team for one-man companies. Context engineering + harness engineering drive a pipeline that brainstorms, builds, reviews, and ships.

Implementation of contextual engineering pipeline with LangChain and LangGraph Agents

🚀 Advanced Data & AI Engineering Portfolio: Real-world projects and production-ready patterns to level up your AI skills—from building clean data pipelines to deploying RAG systems, AI agents, and intelligent dashboards.

A multi-model autonomous agentic engineering pipeline driven by linear. Claude + Codex + Linear

AGENT-FORGE is a Context Engineering Toolkit that generates GitHub Copilot customization files for your VS Code project. Instead of manually authoring .github/ configuration, you describe what you need and a multi-agent AI pipeline plans, generates, validates, and installs everything.

(: SMILE! The positive prompt language for structured prompt engineering — used for complex prompts, multi-turn pipelines, agentic engineering. Modular & maintainable prompts for powerful Large Language Model responses.

Unofficial, Google‑free reimplementation of MLE‑STAR: a lightweight, local‑friendly multi‑agent ML engineering pipeline that uses OpenAI‑compatible LLMs (OpenRouter/Ollama) and DuckDuckGo search to generate, debug, refine, ensemble, and submit Kaggle‑ready solutions—AutoML for tabular tasks with code generation and iterative refinement.

FeDQN is a federated pipeline for training reinforcement learning agent of atari game, the Pong, developed during my first semester at Istanbul Technical University Computer Engineering MSc Program

Skill engineering methodology and publishing pipeline for AI agent skills. Validates structure, scans for security, audits entire projects, and publishes to GitHub. Skills are code — engineer them like it.

Mapr is a Bun-native CLI/TUI designed for deep frontend reverse-engineering. It crawls target sites to collect artifacts, processes chunked code through a multi-agent AI pipeline, and generates comprehensive Markdown reports—mapping initialization flows, restoring names, and inferring call graphs to simplify complex build outputs.

Context engineering IDE for AI agents. Design knowledge pipelines, not just prompts.

An agentic pipeline following context engineering best practices, themed in a cyberpunk era

The Full-Stack LLM Engineering Playbook. Architectural patterns for Agents (MCP) & RAG, coupled with advanced Post-Training recipes (SFT, DPO, QLoRA) for domain adaptation. Covers Data Pipelines, Evaluation Frameworks, and System Design.

VibeDataBot is a web-based app designed to reimagine the experience of AI data engineering. It combines the aesthetic of a modern "Dark Mode" IDE with the power of an intelligent agent that can plan, visualize, and execute data pipelines on a Ray cluster.

Advanced Claude Code slash commands and subagents for agentic engineering workflows - complete task-to-implementation pipeline with intelligent delegation

BRIDGE Development Pipeline. From business requirements to delivered solutions using AI agents. Built for development agencies, consultancies, and engineering teams.

Using RAG pipelines, and agentic AI, alongside prompt engineering to build out a Graph Database that allows us to search and visualise the relationships between organisms on pubmed. For my undergraduate thesis @ UTS

OpenBot is an ​open-source AI agent development platform that combines low-code agility with professional-grade AI engineering capabilities. By integrating ​visual agent orchestration, ​multimodal model management, and ​enterprise-ready RAG pipelines.

It analyzes your resume, extracts skills, identifies gaps, retrieves learning resources, and generates a personalized study plan powered by an LLM agent, RAG system, and a modern data engineering pipeline.

DriftGuard is an AI Security Drift Detection and Enforcement Platform that secures the entire AI ecosystem by continuously monitoring and remediating security, compliance, and governance drift across LLMs, agents, RAG pipelines, MCP tools, and context-engineering layers.

MetaPromptFramework is a collection of advanced meta-prompt engineering templates and design patterns built for LLMs like GPT-4, Claude, Gemini, Mistral, and beyond. This framework provides a structured, reusable, and scalable way to craft high-performance prompts across automation, agents, RAG pipelines, and AI-first applications.

Fast, exact diff coverage gating via CLI for CI pipelines and agentic engineering.

Reliability simulator and chaos engineering toolkit for AI agent pipelines

Production-ready Agentic AI Data Pipeline built with LangGraph for cleaning and segmentation. Bridging the gap between RAG and Deterministic AI Architecture for Enterprise Data Engineering.

A comprehensive foundation in AI Engineering & Data Science. Covering Python fundamentals, SQL, EDA, and end-to-end data pipelines. Building the path toward Agentic AI and Machine Learning.

This project implements a multi-agent LinkedIn content generation pipeline (Research → Alignment → Writing → Critique) for generating high-quality LinkedIn posts). Structured JSON parsing, prompt engineering, and agent routing has been implemented to improve output reliability and reasoning depth.

This Data-Ops monorepo contains production-ready Agentic Workflows (n8n, Gemini, BQ) for B2B RevOps. We replace linear LLM spam with autonomous, event-driven pipelines featuring strict data sanitization, API routing, and failure audit loops. Hardcore engineering, zero bullshit. Fork it and build.

A rigorous, Spec-First framework for autonomous software engineering. Solves the Specification Alignment Problem using a serialized Multi-Dimensional Hypergraph (YAML) and a multi-agent adversarial pipeline. Engineered for Google Antigravity, Claude Code, and Cursor.

AI-powered quantitative trading research platform for alpha discovery, machine learning experimentation, and realistic strategy backtesting. Includes data pipelines, feature engineering, reinforcement learning agents, signal fusion, risk management, and a research dashboard for evaluating trading strategies.

Engineered a data ingestion pipeline using the SEC API to extract, classify, and curate a decade's worth of SEC filings (earnings calls, 10-Ks, 10-Qs, 8-Ks) for US30 stocks.At the San Francisco AI AGENT Hackathon, I utilized GPT-4 to generate Q&A pairs from datasets and fine-tuned the GPT-4 LLM, bolstered by prompt engineering.