Clarify how IDKMesh should be used to develop a separate application in a collaborative environment. The requested path starts from creating a directory and GitHub repository, then connects LLMs, agents, humans, and tools; decomposes work into small chunks; routes work to appropriate participants; verifies results; and explains best practices in a machine- and agent-friendly way.
The repository already contained the core pieces needed for this explanation:
The missing element was a clear external-project onboarding path that connected these pieces from an empty project through day-to-day development.
Added docs/PROJECT_ADOPTION_GUIDE.md with:
Updated the repository front doors so the guide is discoverable from:
README.md;docs/GETTING_STARTED.md;docs/README.md;docs/WHAT_IS_IDKMESH.md.The clearest adoption model is:
GitHub project state
-> bounded Work Unit
-> capability/risk routing
-> human or agent candidate
-> exact-revision evidence
-> independent verification
-> protected integration decision
-> observed outcome
-> next Work Unit
Provider/model selection comes after project policy, capability requirements, and risk classification. Larger models do not gain additional authority.
The guide intentionally does not claim a finished external-project bootstrap or universal live model dispatcher. ProjectManifest/DomainPack validation and adapter infrastructure exist, but a polished idkmesh init + multi-provider Verified Swarm Runner remains future product work.
The change reduces the amount of architecture knowledge required for a new maintainer or contributor to answer “how do I use IDKMesh on my own project?” and gives humans and agents a shared operational vocabulary without requiring private project context.