Create your Claude Agent SDK skill in one prompt, then learn to improve it throughout the chapter

Create your MCP server building skill in one prompt, then learn to improve it throughout the chapter

Build a specification-first Digital FTE that orchestrates accumulated intelligence from Lessons 1-7. Learn to compose execution skills into production-ready agents, validate against specifications, and position for monetization.

Create a skill that orchestrates the write-execute-analyze loop to autonomously process data. Learn to implement error recovery, iterate toward robust solutions, and test your skill across diverse input scenarios. This is where specification-driven development meets real problem-solving.

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

3D web graphics with Three.js (WebGL/WebGPU). Capabilities: scenes, cameras, geometries, materials, lights, animations, model loading (GLTF/FBX), PBR materials, shadows, post-processing (bloom, SSAO, SSR), custom shaders, instancing, LOD, physics, VR/XR. Actions: create, build, animate, render 3D scenes/models. Keywords: Three.js, WebGL, WebGPU, 3D graphics, scene, camera, geometry, material, light, animation, GLTF, FBX, OrbitControls, PBR, shadow mapping, post-processing, bloom, SSAO, shader, instancing, LOD, WebXR, VR, AR, product configurator, data visualization, architectural walkthrough, interactive 3D, canvas. Use when: creating 3D visualizations, building WebGL/WebGPU apps, loading 3D models, adding animations, implementing VR/XR, creating interactive graphics, building product configurators.

Fourth step in building instruction context for codebase

Execute tasks with velocity and quality. Use when ready to implement after clarity and prioritization are complete. This is the fourth system in the 5-system framework.

Coordinator workflow for orchestrating dockeragents through fix-review-iterate-present loop. Use when delegating any task that produces code changes. Ensures agents achieve 10/10 quality before presenting to human.

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

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

Create your OpenAI Apps SDK skill in one prompt, then learn to improve it throughout the chapter

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