What problem does it solve? Setting up a new project for AI-driven development requires manually creating rules, agent definitions, verification scripts, and tool bindings. This Skill automates that entire bootstrap so a fresh repository can run the 7-agent pipeline (PM → Analyst → Architect → Gate → Developer → Reviewer → QA) immediately. ## Core Features & Use Cases - Template-based scaffolding: Copies common, fullstack, backend, or generic template overlays into the target directory, including .harness/ rules, playbooks, scripts, and docs. - Interactive configuration: Asks six questions (project type, stack, hooks, partitioning, output language, AI customization) and substitutes placeholders like {{PROJECT_NAME}} and {{STACK}} across all .tmpl files. - Optional AI-native customization: Drafts a tailored .harness/rules/50-<slug>.md grounded in your manifests (package.json, go.mod, etc.) with source citations, validated against four invariants with static-stub fallback. - Use Case: You create an empty repo for a new FastAPI backend. Run the skill, answer the six questions, and it produces the full .harness/ tree, CLAUDE.md stub, verify_all gate, safety hooks, and git pre-commit hook — ready for /harness pipeline work. ## Quick Start Ask the AI to initialize Harness Engineering in the current empty project directory and answer the setup questions about project type and stack.