claude
fbec36cb67
Scaffold the course repo and author the full curriculum in dependency-chain order, following the settled build decisions in handoff.md. - Scaffold: course README, vendor-neutral AGENTS.md (dogfoods Module 5), _TEMPLATE.md (the fixed 9-section module shape), root .gitignore, ship config. - Modules 1-2: reference exemplars (locked for tone/depth/lab style). - Modules 3-27: full lessons + runnable labs, each following the template, respecting the chain, vendor/model-agnostic, with "feel the pain" labs. - Module 8 hosting comparison web-researched and date-stamped (as of 2026-06-22), not written from memory; expansion-zone modules carry Verify-before-publish. - Capstone: the full loop end to end on the running tasks-app example. Lab code syntax-checked (Python/shell/YAML); every module has the 7 core template sections. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT
94 lines
4.3 KiB
Markdown
94 lines
4.3 KiB
Markdown
# The Workflow
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### The Toolchain Around AI Coding
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A living course for IT professionals who are comfortable in an AI chat window and starting to build
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real software with it — but are still copy-pasting between the chat and their files. The goal is to
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replace that loop with durable engineering workflows: version control, collaboration, CI/CD,
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runners, and the tools that extend AI into real systems.
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> **Thesis:** the model is the cheap, swappable part. The workflow around it is the skill that
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> lasts. This course is deliberately model- and vendor-agnostic — whichever LLM you use, the
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> scaffolding is the same.
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This repo *is* the course, and it also dogfoods the course: it's version-controlled, it commits its
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own AI instructions file ([`AGENTS.md`](AGENTS.md), the subject of Module 5), and each module is
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built on a branch and merged through review — exactly the motion the modules teach.
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---
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## Who this is for
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IT professionals who are fluent in an AI chat window and comfortable with ops concepts — **not
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beginners.** If you already paste code between a chat tab and your editor and feel the friction, you
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are the audience. You will not be taught what a variable is; you will be taught the engineering
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scaffolding that makes AI-assisted work safe, shareable, and repeatable.
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---
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## How the course is built
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It's a **dependency chain, not a topic list.** Every module assumes only what the previous ones
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taught, and nothing references a tool before it's been introduced. The 27 modules group into five
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units, plus a capstone finale.
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| Unit | Modules | Theme |
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|------|---------|-------|
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| **1 — Get out of the chat window** | 1–7 | The local foundation: version control, committing the AI's config, getting the AI editing real files safely. |
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| **2 — Make it shareable, reviewable, recoverable** | 8–12 | The team layer: hosting, issues, review, collaboration, recovery. |
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| **3 — Automate the checking and shipping** | 13–19 | The pipeline: tests, CI, security scanning, containers, secrets, delivery, runners. |
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| **4 — Extend the AI into your systems** | 20–23 | The frontier: MCP, skills, securing them, existing codebases. |
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| **5 — AI in the loop** | 24–27 | Agents inside the pipeline, from assistive to autonomous, plus the evals that make it trustworthy. |
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| **Capstone** | finale | One real feature taken end to end. |
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**Durable core vs. expansion zone.** Modules 1–14 are the stable foundation. From Module 15 onward
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is the expansion zone, where a fast-moving space keeps handing us new lessons. Volatile material
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lives toward the back so the front stays stable as the course grows.
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See [`the-workflow-syllabus.md`](the-workflow-syllabus.md) for the full module-by-module plan and
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the reasoning behind the sequencing.
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---
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## Format and conventions
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- **Written lessons + interactive labs.** Every module is a README you read *and* a lab you run at
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the keyboard. There are no quizzes; there's a "you're done when…" check.
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- **Run labs on your own machine, any OS.** No sandbox or cloud account required. Where a lab needs
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code, it leans on **Python or shell** — picked per lab, kept as small as possible. The *concepts*
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are language-agnostic; the labs just need something concrete to run.
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- **GitHub is the default, not the requirement.** Hosting examples use GitHub because nearly
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everyone will encounter it, but the course is provider-neutral and includes an optional
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**self-hosted-forge track** for on-prem and air-gapped environments.
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- **Self-checks only.** No grading, no certification — each module ends at a concrete done-criterion.
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---
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## Repo layout
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```
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the-workflow-course/
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README.md # this file
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AGENTS.md # committed AI instructions — dogfoods Module 5 (vendor-neutral name)
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the-workflow-syllabus.md # the full course plan (source of truth for structure)
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handoff.md # build-context notes for the authoring sessions
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_TEMPLATE.md # the shape every module follows
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modules/
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01-the-copy-paste-problem/
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README.md
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lab/
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02-version-control-as-a-safety-net/
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README.md
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lab/
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...
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capstone/
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README.md
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assets/ # diagrams, images
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```
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---
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## Status
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Planning is complete (27 modules + capstone). Authoring is in progress, built in dependency-chain
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order. Modules 1–2 are drafted as the reference exemplars; the rest follow.
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