TL;DR
The Hands-on AI plugin went from 7.0 to 8.1.1 between September 21 and 29. Same install, four new capabilities.
Build your knowledge graph from one sentence and a short interview.
The AI Registry comes first. Analyze adds its candidates there, and every later step reads it.
Every framework step was rewritten. Each opens with an agenda and a time, and closes with the next step.
Testing is a report card. Run the workflow, say test this, and every criterion is graded Met or Not met with evidence.
Next step: update the plugin and take one workflow through a Test round.
If you took a Maven cohort course with me this year, you installed the Hands-on AI plugin in the first session and used it in every session after. It has changed a great deal since then, and the course materials on the shared Google Drive have been updated to match. This edition covers what shipped in the last two weeks and what each change lets you do.
What the plugin is
The plugin exists to help you take one piece of your work and turn it into an AI workflow that runs reliably. It follows the AI Workflow Framework, the seven-step method I teach in every cohort: Analyze where AI would help most, Deconstruct the work into its steps and rules, Design how AI will do it, Build it, Test it against real inputs, Run it for real, and Improve it over time.
Each step is a skill you invoke inside your AI tool. Say analyze my workflows or design this in Claude, ChatGPT, or Claude Code. The skill walks you through that step, asks the questions, and writes the result to a file you keep. The plugin also includes the skills for your knowledge graph and your AI registry, covered below. It’s free.
It didn’t exist at the start of this year. The first skills shipped in February, and since then they have given the people I teach a standard way to build. Because every cohort uses the same seven steps, a workflow built by one person can be read, tested, and improved by another.
Everything below applies whether you’ve taken a course or not. Full details and install steps: handsonai.info/use-the-playbook/build/handsonai.
1. Build your knowledge graph with one sentence
In August I wrote about giving your AI a memory of your business: a folder of Markdown pages about your clients, offerings, processes, and people that the AI maintains. In the courses, building one meant pasting a long prompt.
That prompt is now a skill. Open a folder in Claude Code, Cowork, or the ChatGPT desktop app and say build my knowledge graph. The skill reads what’s already in the folder and interviews you about the work. Then it proposes the four to seven kinds of things your business runs on, plus the sentences that connect them (”a Client buys an Offering”). Nothing is written until you approve the list and send the word build.
What the build writes:
A
knowledge/folder in the Open Knowledge Format, with one page per real thing and aSCHEMA.mdthat holds your types and rules.An
overview.mdyou would hand a new hire, plus an index and a log.Two local skills,
ingestandlint, that keep the graph current as you drop in new documents.Provenance on every page. Who wrote it, when, and a footnote from each claim back to the document it came from.
It needs a tool that writes files on your computer. A browser chat window can’t build one, and the skill says so. Setup page: Knowledge Graph Setup.
2. The AI Registry comes first
The registry and the knowledge graph are now two distinct things, side by side in your folder. The knowledge graph records what your business knows. The registry records what you build with AI: your workflows, the processes they belong to, and the skills and agents that run them.
Three skills own it:
scaffolding-registrystands up theregistry/folder with a schema and nodes for your business, lines of business, functions, and processes. Say set up my registry.analyze(Step 1 of the framework) now reads the registry and adds each candidate workflow to your backlog there, so every later step knows what’s next.indexing-registrychecks the registry for errors and regenerates aREGISTRY.mddashboard and a visual HTML version. Say what have I built and get a real answer.
The registry is what I check when someone asks how many workflows I’ve automated. With Analyze filing into it, it also answers the question the framework starts with: where does AI create the most value, and what have you already done about it? Setup page: AI Registry Setup.
3. Every framework step rewritten for the person running it
Version 8 rewrote all seven skills. Each one opens with an agenda and a time estimate, signposts its phases as it goes, and closes by naming the next step. Plan on about a working day, across three or four sessions, to take one workflow from Analyze to Run.
The changes that alter how you build:
Two mechanisms. A workflow runs as a Skill or an Agent. Design asks the question in plain terms and reuses the skills you already have before proposing new ones.
Draft, then approve. Design writes the Design Spec as a draft you read and sign off. Build refuses a spec you haven’t approved.
Build by intent. Build states what it wants built and lets your platform’s own model create the skill or agent its own way.
Every framework term is now defined in plain language in the Framework Glossary.
4. Test grades the real run
This is the change I’d point alumni to first. Testing was a score. It’s now a report card, and it grades the run where the run happened.
How a round works:
Deconstruct proposes the test inputs. You give one or two real inputs you’ve actually handled. The skill proposes the rest, each aimed at something that could go wrong: a hard case, an empty input, a must-never rule, an embedded instruction in content the workflow reads. You correct the list, and each input is saved as a file.
Every built workflow ends with a “What I did” list. The steps it took, where it paused for you, what it did with your tools, and where the deliverable is. That’s the evidence Test grades against.
Run it, then say test this. Open the round in one chat. Run each input in a fresh chat and say test this there. The skill grades every criterion Met or Not met, quoting the What I did list, and the verdict lands in the last chat.
A guard refuses to grade any run that saw your design before it started. Ready means every line met. Build won’t rebuild while a round is open, and Improve’s regression check uses the same run-then-test this pattern. Full walkthrough: Test and the worked example.
A workflow you haven’t graded against real inputs is a demo. The report card is what turns “it worked when I tried it” into something you can hand to a colleague.
How to update
If you already have the plugin, you need version 8.1.1. Checking for updates and installing them are two separate steps on Claude, so do both.
Claude (claude.ai, Desktop, Cowork). Customize → Plugins. Open the … menu on the
handsonai-pluginsmarketplace and choose Check for updates. Then open the handsonai plugin and click Update. No restart needed.Claude Code. One command:
/plugin update handsonai@handsonaiChatGPT. Plugins → find the
handsonai-pluginsmarketplace → Check for updates (or Sync) → update Hands-on AI.Uploaded ZIPs instead of the plugin? Re-download the skills you use from the downloads table and upload them again. Each one replaces the skill of the same name.
Screenshots for every step are in the Update the Skills section of the setup page. Installing for the first time? Start at handsonai.info/use-the-playbook/build/handsonai.
Your next step
Update the plugin, then take one workflow you already run through a Test round. Pick the input most likely to break it. If you haven’t built your knowledge graph yet, that’s the other place to start, and it takes one session.
I hope the framework skills have changed the way you build. Reply and tell me how they’ve helped. I’d like to hear it.
Coming Up. If you have a friend or colleague who would be a good fit for one of my cohorts, two start Monday, October 5: Hands-on Agentic AI for Leaders and Agentic AI for Claude Builders. Forward this along, and they’ll use the same plugin from the first session.
Stay curious. Stay hands-on.
James