Run the Framework End-to-End

In this exercise you run the AI Workflow Framework end-to-end yourself, live in class. Starting from a pre-built Workflow Requirements, you invoke /handsonai:design, /handsonai:build, /handsonai:test, and /handsonai:run on a Competitive Intelligence workflow — shipping a competitor-research skill and competitor-brief agent that produce a structured brief on a real competitor. In a follow-up session, you’ll evolve that workflow into a self-improving system.

Lesson: Ship a Workflow Using the AI Workflow Framework

Run the framework end-to-end yourself with the Hands-on AI plugin installed — in Claude Code (slash commands), Cowork (type / to pick each skill), or Claude Chat (just ask for the step). Starting from a pre-built Workflow Requirements, you invoke /handsonai:design, /handsonai:build, /handsonai:test, and /handsonai:run — generating a competitor-research skill and competitor-brief agent from your approved spec, then running the workflow on a real competitor. Watch a structured context file get produced on the first run.

Hands-on assignment

Starting point: a pre-built Workflow Requirements (download below). Ending point: a shipped skill + agent producing a brief on a real competitor.

1. /handsonai:design — Turn the requirements into an approved Design Spec (plan mode, collaborative). See the Design step docs.
2. /handsonai:build — Generate the competitor-research skill and competitor-brief agent from your spec. See the Build step docs.
3. /handsonai:test — Validate the building blocks before trusting them with real input. See the Test step docs.
4. /handsonai:run — Invoke the workflow on a real competitor; watch knowledge/competitors/{name}.md emerge. See the Run step docs.

Download the Workflow Requirements

The Step 2 (Deconstruct) artifact is pre-built so we can spend class time running the rest of the framework on it.

Download the Competitive Intelligence — Workflow Requirements (.md)

Set up your workspace before class

Do these steps in the folder you have open in Cowork or Claude Code.

Step 1 — get a registry/ folder. Open the folder you’ll work in (any folder — it doesn’t need to be a GitHub repository) in your AI assistant and say “Set up my AI registry.” It creates the registry/ folder for you. If you’d like GitHub to build a dashboard automatically, start from the AI Registry template repository instead — the AI Registry setup guide walks through both.

Step 2 — create the workflow folder and save the download. Inside your workspace, create a folder named outputs, and inside it a folder named competitive-intelligence-brief. Save the download as requirements.md inside that folder, so the full path is outputs/competitive-intelligence-brief/requirements.md. (Rename the downloaded file — the framework skills look for requirements.md.)

Step 3 — hand-type the first Workflow node. In registry/workflows/, create a file named competitive-intelligence-brief.md and paste in exactly this. Easiest path: ask your AI assistant to create the file for you with this exact content. (Or, if your registry lives on GitHub and you save files yourself, open your repository on github.com, click Add file → Create new file, paste registry/workflows/competitive-intelligence-brief.md as the filename, paste the block below as the content, then Commit — same result either way.)

---
type: Workflow
title: "Competitive Intelligence Brief"
description: "Research a competitor's recent moves and produce a structured brief plus an updated knowledge file."
generated: { by: process:deconstruct, at: 2026-07-06 }
status: under-development
definition_type: step-driven
execution_mode: manual
autonomy: guided
trigger: "manual"
stale_after: 2026-10-06
---
# Competitive Intelligence Brief

Research a competitor's recent moves and produce a structured brief plus an
updated knowledge file. Triggered manually on demand, or scheduled per competitor.

# Artifacts

- [Requirements](outputs/competitive-intelligence-brief/requirements.md)

The node is the small file every framework step reads first; your registry/ is the inventory. When class starts, you’ll run /design and it will pick up right where the pre-built requirements leave off.

What you’ll walk away with

• A competitor-research skill and a competitor-brief agent installed in your workspace, ready to invoke any time
• A populated knowledge/competitors/{name}.md file on a real competitor — the seed for the self-improvement work later in the course
• First-hand reps on every framework step as a chainable slash command (/handsonai:analyze, /handsonai:deconstruct, /handsonai:design, /handsonai:build, /handsonai:test, /handsonai:run, /handsonai:improve)

Lesson: Recognize When a Workflow Needs to Improve

A brief, conceptual preview of Step 7 of the framework — Improve — anchored to the workflow you just ran. Identify the three signals that tell you a running workflow needs to evolve: hypotheses that never confirm, edge cases where the schema breaks, and drift as your priorities evolve. Improve is the step that never ends — and in the follow-up session, you’ll actually do the iteration.

Lesson: Evolve Your Workflow Into a Self-Improving System

Take the workflow you just shipped and evolve it into a system that gets sharper every time it runs. Apply Karpathy’s “LLM Wiki” pattern to its structured context file — structure it with Rules / Facts / Hypotheses sections, implement an ingest / query / lint learning loop, and schedule the workflow to run daily without you. By session end, your workflow’s outputs feed a self-improving knowledge base of competitor intelligence — Step 7 (Improve) in action.