Open vs Closed AI
Source-grounded pack with Audit AI Openness, Choose AI Stack Boundaries, and Design Open AI Distribution, plus 1 more.
Get this packGrounded in the complete Open vs Closed AI series.
Open the pack, choose a workflow, and follow its setup and input instructions. You can also open each workflow below.
10 source essays · 4 usable skills · reviewed Jul 2026
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.
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 specification
Return an ai_openness_audit with layers, rights, practical_access, controlled_surfaces, license_constraints, hosting, version_control, portability_test, operating_burden, dependency_risks, and claim_classification.
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 specification
Return an ai_stack_boundary_decision with workflow, loop_to_own, layers, required_properties, options, tradeoffs, choice, owners, operating_cost, risks, fallbacks, migration_path, and reassessment_triggers.
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 specification
Return an open_ai_distribution_strategy with objective, target, open_layer, rights, governance, adoption_path, ecosystem_value, capture_layer, portability_effect, support_boundary, economics, risks, and success_evidence.
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 specification
Return an ai_improvement_loop_control with loop_surfaces, owners, learning_assets, data_rights, vendor_dependencies, abstraction_boundaries, eval_control, observability, continuity_path, open_ops_owners, contract_controls, and review_triggers.
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.