AI Engineering OS

Skills for AI engineers. One session at a time.

Each session is a folder. Each folder contains SKILL.md files — drop them into your project, tell your AI to read them, and it starts behaving like a senior engineer instead of a code generator.

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Sessions

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F1 — The Prediction Engine ✅

9 skills. Full regression pipeline. From “what are we building?” to “here’s the business impact.”

f1-the-prediction-engine/
├── SESSION.md                     ← Start here
├── problem-framing-skill/         ← Frame the right problem before touching data
├── model-hypothesis-skill/        ← Choose a hypothesis that matches your situation
├── loss-design-skill/             ← Design loss around business costs, not defaults
├── training-diagnostics-skill/    ← Debug training before trusting the output
├── feature-engineering-skill/     ← Domain knowledge beats polynomial noise
├── generalization-skill/          ← Overfitting, regularization, assumption checks
├── data-integrity-skill/          ← Catch leakage before it reaches production
├── evaluation-skill/              ← Report rupee impact, not just RMSE
└── ml-system-skill/               ← The full 7-stage pipeline, gate by gate

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F2 — Cluster Thinking 🔜

K-Means & Unsupervised Learning — when you don’t have labels

f2-cluster-thinking/
└── SESSION.md                     ← Preview: what's coming

Skills planned: unsupervised framing, K-Means intuition, cluster failure modes, validation (elbow + silhouette), segment-to-insight, full unsupervised pipeline.

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F3 — The GenAI Builder 🔜

GenAI Foundations — LLMs, prompting, RAG, API engineering

f3-genai-builder/
└── SESSION.md                     ← Preview: what's coming

Skills planned: LLM intuition, prompt architecture, RAG design, API engineering, GenAI vs traditional ML decision framework, building a real tool.

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F4 — Ship Ready 🔜

EDA, Business Thinking & Sprint Readiness — from model to delivered project

f4-ship-ready/
└── SESSION.md                     ← Preview: what's coming

Skills planned: hypothesis-driven EDA, data quality assessment, insight-to-action, production code habits, Git workflow, sprint tools.

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How to use any skill

1. Find the skill that matches where you are in your project
2. Copy that SKILL.md into your working folder
3. Tell your AI: Read SKILL.md and follow it
4. The AI asks the right questions instead of jumping to code

In Cursor: @SKILL.md — read this and follow it In Claude Code: Read SKILL.md and follow it for this project