What problem does it solve? Starting a project for AI-agent-assisted development requires consistent scaffolding: a git repository, agent instruction files, a defined technology stack, documented architecture decisions, and a minimum quality gate. Doing this manually is error-prone and inconsistent, especially across single-repo and multi-repo solutions. ## Core Features & Use Cases - Project bootstrap from any starting point: Detects whether a project has no code, scaffolded code, existing implementation, or is specs-only, and completes whatever harness pieces are missing. - Harness file creation: Generates AGENTS.md, .agents/MEMORY.md, .sdd-devkit/settings.json, ADR and standards indexes, and a root README description from fixed templates, with idempotent re-runs. - Stack setup and quality gate: Researches or suggests a technology stack, scaffolds it, and configures a minimum test gate (unit tests plus relevant layers like E2E or API testing). - Multi-repo support: Creates a specifications repository that aggregates other repos as git submodules, each with its own AGENTS.md and architecture indexes. - Use Case: You have an empty folder for a new product. Invoke the skill and it asks what you want to build, suggests a stack, scaffolds it, creates all agent instruction files, configures a test framework, and documents the initial decisions as ADRs. ## Quick Start Ask the agent to initialize the harness for this project with arch-init and answer its questions about topology, stack, and testing layers.