Create your RAG skill in one prompt, then learn to improve it throughout the chapter

Create a relational-db-agent skill that knows SQLModel async patterns

Create your agent-tdd skill in one prompt, then learn to improve it throughout the chapter

Create your agent-evals skill from official documentation before learning evaluation concepts. This skill-first approach ensures you build production-ready evaluation capabilities grounded in authoritative sources.

Validate your agent-evals skill by testing it on a completely different agent. A skill proves its worth when it transfers beyond the context where you learned it.

Transform Docker knowledge from Lessons 1-6 into a reusable AI skill for consistent, production-ready containerization

Create your Docker deployment skill in one prompt, then learn to improve it throughout the chapter

World-class #1 expert 4PL and supply chain director specializing in AI-powered logistics optimization, digital transformation, warehouse automation, transportation management systems (TMS), inventory optimization algorithms, 3PL/4PL strategic partnerships, supply chain analytics, and global logistics operations. Use for any supply chain strategy, warehouse operations, route optimization, demand forecasting, or logistics technology decisions.

Reset after failures, blockers, or when stuck. Use when execution hits a wall, something breaks, or we need to step back and reassess. This is the fifth system in the 5-system framework.

[50] EXECUTE. Execute plans step-by-step with confirmation gates. Each step requires user approval before proceeding. Includes change management lifecycle (Pre-Change → During → Post-Change → Rollback). Use when implementing approved plans, deploying changes, or any multi-step execution requiring control and reversibility.

Create your Kubernetes deployment skill in one prompt, then learn to improve it throughout the chapter

You built your Kubernetes skill in Lesson 0 and refined it through Lessons 1-14. Now validate that it actually transfers to new projects.

**Core Principle:** Commit to excellence. Work at 150% — 100% core delivery + 50% strengthening through verification, alternatives, and risk awareness.

Create your Helm chart skill in one prompt, then learn to improve it throughout the chapter

Create your Kafka event schema skill in one prompt, then learn to improve it throughout the chapter

Create your Dapr deployment skill in one prompt, then learn to improve it throughout the chapter

Create your GitOps deployment skill in one prompt, then learn to improve it throughout the chapter

Create your observability and cost engineering skill in one prompt, then learn to improve it throughout the chapter

Create your traffic engineering skill in one prompt, then learn to improve it throughout the chapter

Extend your existing Dapr skill with actor and workflow patterns using official documentation

Complete your dapr-deployment skill with actor and workflow patterns, validate code generation, and document your Digital FTE component

Create your cloud security skill in one prompt, then learn to improve it throughout the chapter

Create your operational excellence skill in one prompt, then learn to improve it throughout the chapter

Complete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check responsive design, validate UX, test login flows, check links, automate any browser task. Use when user wants to test websites, automate browser interactions, validate web functionality, or perform any browser-based testing.

5W1H Decision Framework Tool. Use for: (1) Systematic decision-making before creating todos, (2) Preventing duplicate implementation, (3) Detecting avoidance behavior, (4) Ensuring agile refactor compliance with executor/dispatcher separation

Guides consistent, correct implementation of Stripe payment processing including payment flows, webhooks, subscriptions, and customer management. Use when integrating Stripe payments, setting up subscriptions, implementing webhooks, or managing customer billing.

Test features before users find bugs. Use when feature is built, before deploying, or when bugs reported. Covers manual testing, edge cases, cross-browser testing, and testing checklists for non-technical founders.

Create your multi-cloud-deployer skill in one prompt, then learn to improve it throughout the chapter

You've spent this chapter building mental models: the LLMOps lifecycle, training taxonomy, economic analysis, use case specification. All valuable knowledge—but knowledge fades. Six months from now, y

[61] VALIDATE. Comprehensive code quality check combining ESLint, TypeScript compilation, and unused code detection. Runs full lint suite with detailed error reporting and fix suggestions. Use before commits, after major changes, or when ensuring code quality standards.

[62] VALIDATE. Final self-check before delivery. Verify goal alignment, completeness, correctness, and identify residual risks. Produces quality score (0-100) and delivery status. Use when completing any significant work, before handoff, or when you need confidence that work is ready.

Create your LLMOps data engineering skill in one prompt, then learn to improve it throughout the chapter

Create your llmops-fine-tuner skill from Unsloth documentation before learning fine-tuning theory

You're about to learn persona fine-tuning. But here's the pattern that will make this knowledge truly yours: **build the skill BEFORE you learn the content**.

Before diving into tool-calling patterns, structured outputs, and JSON accuracy metrics, you'll build the skill that will guide your learning throughout this chapter. This isn't just preparation—it's

You've trained two specialized adapters: a TaskMaster persona adapter (Chapter 65) and an agentic tool-calling adapter (Chapter 66). Now you need to combine them. But before you learn the theory of mo

Create your model-alignment skill from TRL documentation before learning DPO theory

Create a reusable skill for evaluating fine-tuned models, benchmarking performance, and detecting quality regressions

[70] CLOSE. Manage task lifecycle with correct statuses and no duplicates. Ensure each task has one status, artifacts match claims, and no task appears in multiple places. Use when managing task lists, updating progress, or ensuring task tracking integrity across systems.

Create your model-serving skill from Ollama documentation before learning deployment theory

Create your agent-integration skill from OpenAI SDK and LiteLLM documentation before learning framework integration

[71] CLOSE. Quick, safe cleanup after completing a milestone. Fix objective issues only (syntax errors, dead code, poor naming). Must be <5% of main task time, <30 seconds per fix, and reversible. Use after key points, not after every small change.

**Core Principle:** Never lose progress. Save context before ending, restore context when starting.

[73] CLOSE. Record and maintain Lessons in MEMORY.md after a problem is solved or the user confirms success. Use when capturing a new lesson, moving lessons through the pipeline, or enhancing Project Architecture Quick Reference with new insights.

[74] CLOSE. Quick checkpoint during active work when context is running low. Use multiple times per development cycle to preserve progress and lessons. Lighter than close-session — no full handoff needed. Triggers on 'save progress', 'checkpoint', 'context low', or automatically when nearing token limits.

Create documentation with gaming-specific examples, retro styling, and 8-bit terminology. Apply when documenting gaming blocks, RPG components, or retro-styled UI elements.

Generate web assets including favicons, app icons (PWA), and social media meta images (Open Graph) for Facebook, Twitter, WhatsApp, and LinkedIn. Use when users need icons, favicons, social sharing images, or Open Graph images from logos or text slogans. Handles image resizing, text-to-image generation, and provides proper HTML meta tags.

9D product development framework

AI Manipulation Defense System implementation with Midstream, AgentDB, and lean-agentic

AgentObservability