Open vs Closed AI
Source-grounded pack with Audit AI Openness, Choose AI Stack Boundaries, and Design Open AI Distribution, plus 1 more.
Use this pack when
Choose this pack when the work touches Open vs Closed AI and you need a bounded workflow with explicit inputs, a defined output, and human review gates.
Included workflows
Choose the job in front of you.
4 source-grounded skills. Load one workflow at a time.
Audit AI Openness
Use this workflow when evaluating an AI model, platform, framework, or vendor that claims to be open and operators need precise evidence about weights, code, data, evaluations, licenses, hosting, auditability, version control, portability, and remaining vendor control.
Output Return an aiopennessaudit with layers, rights, practicalaccess, controlledsurfaces, licenseconstraints, hosting, versioncontrol, portabilitytest, operatingburden, dependencyrisks, and claimclassification.
Choose AI Stack Boundaries
Use this workflow when choosing open, closed, or hybrid components for an AI product or enterprise workflow layer by layer based on outcome quality, accountability, sovereignty, customization, portability, operating capacity, cost, latency, support, and migration risk.
Output Return an aistackboundarydecision with workflow, looptoown, layers, requiredproperties, options, tradeoffs, choice, owners, operatingcost, risks, fallbacks, migrationpath, and reassessmenttriggers.
Design Open AI Distribution
Use this workflow when deciding whether to open an AI model, framework, interface, evaluation, or runtime layer as a distribution strategy while preserving a coherent commercial value-capture layer and honest user portability.
Output Return an openaidistributionstrategy with objective, target, openlayer, rights, governance, adoptionpath, ecosystemvalue, capturelayer, portabilityeffect, supportboundary, economics, risks, and successevidence.
Protect an AI Improvement Loop
Use this workflow when protecting ownership of an AI workflow's user surface, context, feedback, corrections, evaluations, release process, deployment, and migration path so external models can change without surrendering compounding learning or business continuity.
Output Return an aiimprovementloopcontrol with loopsurfaces, owners, learningassets, datarights, vendordependencies, abstractionboundaries, evalcontrol, observability, continuitypath, openopsowners, contractcontrols, and reviewtriggers.
Human review
Keep consequential decisions explicit.
These workflows structure analysis and artifacts; they do not authorize autonomous external, personnel, financial, legal, security, or other high-impact action.
Source and files
Grounded in the complete Open vs Closed AI series.
10 source essays · 4 usable skills · reviewed Jul 2026