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Build a Research-Driven Next Training Node

Strong workshop content comes from real product signals, not guesses.

🎯 What You'll Do

In this step, you will turn github/gh-aw research into a concrete training plan update. You will review current gh-aw documentation signals, identify one meaningful learner gap, and draft a new workshop node proposal that is ready to implement. By the end, you will have a repeatable method for deciding what to teach next with confidence.

📋 Before You Start

  • You completed Share and Reuse Your Agentic Workflows.
  • You can open workshop/README.md and identify where new nodes belong in the curriculum table.
  • You can run gh aw compile for workflow validation from earlier steps.

Steps

Review current gh-aw signals

Start by collecting the most current signal from the source repository and its docs references:

for url in \
  "https://raw.githubusercontent.com/github/gh-aw/main/LLMs.txt" \
  "https://raw.githubusercontent.com/github/gh-aw/main/llms.txt" \
  "https://github.github.com/gh-aw/llms.txt"; do
  if curl -fsSL "$url" | head -n 40; then
    break
  fi
done

This gives you a compact index of what the gh-aw project currently emphasizes for model and documentation consumption.

Pick one high-value learner gap

Read your existing workshop path and ask one practical question: what can a learner do now that they could not do before this new node exists? Keep your answer narrow. Good gaps are concrete, such as "how to validate workflow constraints before opening a PR" or "how to select safe outputs for automation."

Draft a node proposal with clear scope

Write a one-paragraph node scope and list the exact artifacts it should change:

  • one new workshop/<step>-<slug>.md file
  • one curriculum row in workshop/README.md
  • optional wording refresh in workshop/00-welcome.md when total step count changes

Capture research metadata in XML comments

Add XML comments to preserve reasoning without interrupting learner flow:

<!--
<research-metadata>
  <focus>safe outputs selection</focus>
  <sources>
    <source>https://raw.githubusercontent.com/github/gh-aw/main/LLMs.txt</source>
    <source>https://github.github.com/gh-aw/reference/safe-outputs/</source>
  </sources>
  <rationale>...</rationale>
</research-metadata>
-->

Keep the comment concise and traceable to real sources you used.

Validate before opening a pull request

Run markdown lint and compile checks so your proposal is production-ready:

npx --yes markdownlint-cli2 "workshop/**/*.md"
gh aw compile --validate

✅ Checkpoint

  • You reviewed current gh-aw direction signals from LLMs.txt
  • You identified one concrete learner gap for a new training node
  • You drafted a bounded node scope tied to specific repository files
  • You captured supporting rationale in XML comments
  • You ran lint and compile validation before preparing a PR

Next: Make Your Workflow Remember Across Runs