July 10, 2026
GRASP: Build an AI operating system
GRASP turns a vague AI request into a governed operating model with a goal, people, assets, sequence, and proof.
This was the core operating-model session. The room connected reusable skills, durable knowledge, model selection, project folders, and a five-part method for starting work well.
Questions the room worked
How do you give AI enough context without rebuilding it every time?
Which instructions should become reusable skills?
How do you define done before a model begins?
Methods you can use
G: Goal
Name the result. A deliverable is only the container for the outcome.
R: Roles
Name the audience, reviewer, and approver. The final yes belongs to a person.
A: Assets
Gather tools, data, brand guidance, prior work, and examples before kickoff.
S: Sequence
Define the steps before any tool opens. One instruction should move the work to the next stage.
P: Proof
Set the quality bar and name the likely failure point. Decide how the result will be checked.
The five-step setup
Turn GRASP into an operating workspace.
- 1.Requirements: answer the five GRASP questions.
- 2.Materials: collect brand, voice, audience, references, and examples.
- 3.Folders: create one project folder before work starts.
- 4.Claude setup: connect skills, sources, and folders.
- 5.Execution: define done, issue one instruction, then review.
What carried forward
- Stable instructions should become reusable skills with owners.
- Knowledge should remain portable outside one model interface.
- Model choice should follow the work and quality requirement.
- Clear proof reduces endless iteration.
What to test next
- Run GRASP before the next meaningful AI project.
- Convert one repeated prompt sequence into a named skill.
- Create a tracker with deliverable, location, status, and owner.
Take the work with you