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 GRASP, reusable skills, projects, model selection, model-neutral files, backups, and enterprise governance. GRASP defines the work before the model begins. The operating system makes that discipline repeatable.
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.
Projects versus skills
A project holds the changing context for one body of work. A skill holds a stable method that should run consistently across projects.
Portable files and model routing
Keep durable knowledge in lightweight, model-neutral files. Choose the model for the task, quality requirement, and cost instead of forcing every step through one model.
What carried forward
- Stable instructions should become reusable skills with owners.
- Knowledge should remain portable outside one model interface.
- Projects and skills solve different context problems.
- Model choice should follow the work and quality requirement.
- Enterprise adoption requires orchestration, governance, and ownership.
- 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