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Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.
Last updated: May 24, 2026.
This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.
Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.
A Claude Code Agent Skill built from Dex Horthy's (HumanLayer) playbook on David Ondrej's podcast.
"Once the model has written thousands of lines of code, it is harder to change. The sessions that generate design docs are context-light — you get the most model intelligence when you do the hard thinking early."
By default, agents build horizontally: all the backend, then all the frontend, then a 2,000-line diff lands in your lap and reviewing it is your problem. This skill flips that. Every decision that matters gets made before the code exists — where changing your mind costs a sentence, not a rewrite.
cd /d %ProgramFiles(x86)%\Microsoft Office\Office16
cd /d %ProgramFiles%\Microsoft Office\Office16
Composite actions allow you to group multiple steps into a single action, simplifying your workflow files. This is useful for encapsulating common sequences of steps that are repeated across multiple workflows, reducing redundancy and making workflows easier to maintain.
Composite action definitions are metadata files, not workflows, and are therefore not placed in the “workflows” directory. Instead, they are typically found in the “actions” directory or another specified location. In our case, the composite action is defined in .github/actions/composite-action–definition/action.yml.
| { | |
| "$schema": "https://raw.githubusercontent.com/code-yeongyu/oh-my-openagent/dev/assets/oh-my-opencode.schema.json", | |
| "model_fallback": true, | |
| "runtime_fallback": { | |
| "enabled": true, | |
| "retry_on_errors": [400, 429, 503, 529], | |
| "max_fallback_attempts": 3, | |
| "cooldown_seconds": 60, | |
| "timeout_seconds": 30, | |
| "notify_on_fallback": true |
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
This is a list of C++ operators that can be overloaded and their normal signatures(a.k.a what an int would do). The order is the preffered order to use them(The first one listed is often preffered)
T operator+( T const & lhs, T const & rhs )T operator+( T const & rhs ) constT operator+( ) const| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |