Create a Best-Practice SOP Library in Enter for a U.S.-based multi-unit restaurant group. Help operations teams turn store-level practices into reviewed SOPs, publish them to a shared library, and track whether stores actually adopt them.

[TARGET USERS]
- Store Manager / Shift Lead: Submit practices with steps, materials, time required, expected impact, and evidence, and report adoption feedback.
- Operations / Library Manager: Review submissions and AI-generated SOP drafts, decide the approval status, publish SOPs, and run adoption plans.
- Regional Manager: Review practices from their stores and follow adoption in their region.
- Administrator: Manage categories, templates, stores, users, and access.

[CORE WORKFLOW]
1. Collect each practice with title, category, store, owner, problem, context, steps, materials, time required, impact metrics, and evidence files. Flag incomplete submissions as Needs info.
2. Let an AI assistant draft an SOP, checklist, and training outline from the submission and an optional template. Label the draft as AI-generated, list risk notes, and never invent measured results.
3. Let reviewers choose Needs info, Pilot first, Approve & publish, or Reject with comments. Only approved drafts are versioned and published to the library.
4. Create adoption plans for published SOPs with target stores, an owner, checklist completion, and store feedback. Summarize blockers, success stories, and revision signals.

[CORE PAGES]
- Dashboard: Submissions, drafts in the pipeline, published SOPs, checklist completion, practice inbox, review pipeline, and active adoption plans.
- Submit Practice: Structured submission form with evidence upload.
- SOP Drafts and Review & Approval: Draft detail with SOP steps, checklist, training outline, risk notes, approval status, and review thread.
- Approved Library: Published SOPs with version, category, and adoption count.
- Adoption Tracker and Feedback Summary: Plan progress by store, report coverage, ratings, blockers, and suggested revisions.
- Data Management: Imports, templates, categories, users, and access.

[DATA AND BUSINESS RULES]
- Use stable IDs to link submissions, drafts, revisions, reviews, published versions, adoption plans, stores, and feedback.
- Keep every draft linked to its source submission; editing creates a new revision instead of overwriting history.
- AI-generated durations and impacts are suggestions until a pilot records measured results; attached files are not treated as read unless they are processed.
- Only reviewers with approval rights can publish. Pilot first keeps a draft out of the library until adoption is proven.
- Report coverage counts stores that submitted feedback; stores without reports are unknown, not successful.
- Include a demo case: "Lunch prep station readiness" submitted by Ava Torres from Austin Domain on 09/16/2026. Its AI-generated draft (version 1) is In review; the risk notes say no measured outcomes exist and the 15-minute duration is untested, so they recommend Pilot first with at least one week of shift data. A separate published SOP, "Two-stage lunch prep board" v1.0, has an active adoption plan in 3 stores with 22% checklist completion and 0 of 3 store reports.

[VISUAL DIRECTION AND DELIVERABLES]
- Use a calm knowledge-workspace style with readable SOP steps, clear status badges, and visible links between source, draft, and library.
- Support desktop and mobile so store teams can submit practices and follow checklists on the floor.
- Use labelled demo data with consistent IDs and dates and U.S. date conventions.
- Deliver a publishable demo with one traceable path from store submission to review decision.

Before building, provide a requirements summary, missing information, page architecture, data model, review and publishing rules, role permissions, state transitions, and acceptance criteria. Do not begin generating pages yet.