AI capabilities need version, status, baseline, change, release, audit, complete PLM system.

Components, blueprints, specs, patterns, skills, factory patterns cover the complete lifecycle.

1)    Version management: AI capabilities have version numbers, supporting coexistence and rollback.

2)    Status management: clear states (development, testing, release, retirement), controlled transitions.

3)    Baseline management: establish baselines, freezing AI capability configs as change reference.

4)    Change management: changes require application, review, approval, with complete audit logs.

5)    Release management: releases require approval, with version records and rollback support.

Auto-analyze impact scope before changes, dev.change.impact.analyzer.

Not blind changes, but analyzing potential impact and risk before deciding.

1)    Dependency analysis: analyze AI capability dependencies, identify affected downstream capabilities.

2)    Impact scope: identify affected modules, agents, workflows, skills.

3)    Risk assessment: assess change risk level, high-risk changes need stricter approval.

4)    Impact report: generate change impact report for decision reference.

Factory methods for creating components, blueprints, specs, method specs.

Factory pattern provides unified creation interface, ensuring created objects meet specifications.

1)    Component factory: unified creation of development components, ensuring structural compliance.

2)    Blueprint factory: unified creation of blueprints, ensuring complete definitions.

3)    Spec factory: unified creation of specs, ensuring correct parameters.

4)    Method spec factory: unified creation of method specs, ensuring standard definitions.

Blueprint and spec workflow approval, ensuring changes go through proper approval.

Not direct modifications, but through workflow approval processes for controlled changes.

1)    Blueprint approval: blueprint changes require approval flow before taking effect.

2)    Spec approval: spec changes require approval flow before taking effect.

3)    Configurable approval: approval flows can be customized, supporting multi-level, countersign.

4)    Approval records: complete records of approval process, traceable.

PLM knowledge articles, chunks, sources management.

Sediment AI development knowledge into manageable knowledge assets.

1)    Knowledge articles: manage AI development knowledge articles with full-text search.

2)    Knowledge chunks: split knowledge into chunks for reuse.

3)    Knowledge sources: manage knowledge sources, support external import.

4)    Knowledge association: associate knowledge with AI capabilities for easy lookup.

Auto-generate code from specs, dev.code.generator.

Transform AI capability spec definitions into runnable code.

1)    Spec to code: transform AI capability specs into Python code.

2)    Code templates: use predefined templates ensuring generated code meets standards.

3)    Code verification: generated code auto-passes syntax and convention checks.

4)    Code update: regenerate code after spec changes, keeping code and specs in sync.