# MLAD.ai > Engineering skills for AI-assisted development, built from production > experience and applied research. Clean patterns that make any feature a pivot, not a re-write. Get more from leading AI coding platforms. Founded by Greg Ruthenbeck PhD. IEEE Senior Member. Former Lead, Australian Institute for Machine Learning. 13+ years university teaching. ## The MLAD AI Skills Schema 69 practitioner skills for AI-assisted development across 11 competency clusters, organized by 7 themes and 4 proficiency bands (Foundation, Practice, Mastery, Frontier). Derived from documented expert workflows in production AI coding sessions. Clusters: ai-self-verification (6), verification-tooling (6), model-fluency (5), context-engineering (6), session-convention (6), interruption-and-control (7), parallel-orchestration (8), context-persistence-and-handoff (6), composability-and-automation (7), recovery-and-git-discipline (6), planning-and-decomposition (6) - https://mlad.ai/ledger - https://mlad.ai/ledger/llms.txt ## The MLAD 5-Axis Prompt Taxonomy 5399 curated AI coding prompts from 34 professional sources, classified along 5 independent axes: - Type: System (behavioral rules defining ai identity and persona), Task (immediate work request to complete), Skill (capability with explicit trigger pattern), Reference (documentation, cheatsheets, setup guides), Meta (prompts about prompting conventions) - Activation: Manual (manually placed / persistent), Invoked (called by name -- slash commands, named tools), Triggered (activates on context match -- file patterns, topics, working state), Reactive (fires on agent lifecycle events -- hooks, guardrails, interceptors) - Activity: Create (generate or transform), Fix (correct or validate), Understand (explain or analyze) - Constraint: Open (ai chooses approach freely), Guided (soft preferences given), Bounded (hard rules, some flexibility), Scripted (exact steps prescribed) - Scope: Global (all ai interactions), Project (this codebase), Session (this conversation), Atomic (single use) - https://mlad.ai/prompts - https://mlad.ai/prompts/about - https://mlad.ai/prompts/llms.txt --- ## Articles 7 articles on AI-assisted development. - Simple Skill, Full API: Replacing an MCP with 218 Lines: An MCP server silently dropped 9 Ghost API fields. We replaced it with a 218-line Claude Code skill that passes calls straight through the client library. https://mlad.ai/articles/simple-skill-full-api-replacing-an-mcp-with-218-lines - Skills Are More Context-Efficient Than MCP: MCP servers can consume 55,000 tokens of Claude Code's context window before you type. Skills achieve the same automation at 3x lower cost. Token comparison for Playwright and a framework for choosing the right approach. https://mlad.ai/articles/skills-are-more-context-efficient-than-mcp - Securing Your Vibe-Coded App: Nearly half of AI-generated code contains security flaws. Five checks every vibe-coded app needs: server-side auth, secrets management, access control, XSS prevention, input validation. Platform fixes for MoltBot, Lovable, Codex, and Cursor. https://mlad.ai/articles/securing-your-vibe-coded-app - A Taxonomy for AI Prompts: A classification system for AI coding prompts, built from 1,751 real prompts across 12 sources. Three types and four axes turn ad-hoc prompting into deliberate practice. Browse the full collection in the interactive Prompt Explorer. https://mlad.ai/articles/understanding-ai-prompts-a-taxonomy-for-the-age-of-coding-assistants - The Proposal-Driven Cycle: A lightweight workflow for AI coding that captures intent alongside code. Proposal, Plan, Execute, Record. Version Docs complement git history so you can audit decisions, seed new contexts, and scale documentation to match task complexity. https://mlad.ai/articles/the-proposal-driven-cycle - Understanding System Prompts: How system prompts shape AI behavior and why they matter for consistent tooling. https://mlad.ai/articles/understanding-system-prompts - Advanced Prompt Engineering Patterns: Chain-of-thought, few-shot, and self-consistency patterns for production AI systems. https://mlad.ai/articles/advanced-prompt-engineering-patterns - https://mlad.ai/articles/llms.txt ## The Codex 2 codexes, 30 entries with AI session replays. - Reddit Discovery & Analysis with Claude (13 entries): Build a working information pipeline: discover content, score it, cluster it, extract what matters. All on your GPU. https://mlad.ai/codex/reddit-discovery-and-analysis - Intro to AI App Development (17 entries): Build a production-quality app using AI as your coding partner. Learn 11 transferable techniques for AI-assisted development through real coding sessions. https://mlad.ai/codex/intro-to-ai-app-development - https://mlad.ai/codex/llms.txt ## Quests 1 quests tracking 8 stories. - Reddit Discovery & Analysis (8 stories): Build a complete Reddit analysis pipeline using AI-assisted development. Watch real engineering sessions, answer challenges at key moments, build judgment across six pattern families. Themes: decomposition, verification, context-engineering, recovery, session-architecture, hitl-review https://mlad.ai/quests/rdac - https://mlad.ai/quests/llms.txt ## About - https://mlad.ai/about