docs: add comprehensive notification system documentation

- Add GLOSSARY.md with core terminology and cross-references
- Add implementation-roadmap.md with 3-phase development plan
- Add notification-system.md with Native-First architecture spec
- Update ios/Plugin/README.md to reflect actual vs planned implementation status

This establishes the foundation for implementing shared SQLite storage,
TTL-at-fire enforcement, rolling window safety, and platform completion
as outlined in the phased roadmap.

Files: 4 changed, 807 insertions(+), 13 deletions(-)

.cursor/rules/docs/documentation_references_model_agents.mdc

@@ -0,0 +1,96 @@
+---
+title: Documentation, References, and Model Agent Use
+version: 1.1
+alwaysApply: true
+scope: code, project-plans
+---
+
+# Directive on Documentation, References, and Model Agent Use in Code and Project Plans
+
+To ensure clarity, efficiency, and high-value documentation within code and project plans—and to leverage **model agents** (AI- or automation-based assistants) effectively—contributors must follow these rules:
+
+---
+
+## 1. Documentation and References Must Add Clear Value
+
+- Only include documentation, comments, or reference links when they provide _new, meaningful information_ that assists understanding or decision-making.  
+- Avoid duplicating content already obvious in the codebase, version history, or linked project documents.
+
+---
+
+## 2. Eliminate Redundant or Noisy References
+
+- Remove references that serve no purpose beyond filling space.  
+- Model agents may automatically flag and suggest removal of trivial references (e.g., links to unchanged boilerplate or self-evident context).
+
+---
+
+## 3. Explicit Role of Model Agents
+
+Model agents are **active participants** in documentation quality control. Their tasks include:  
+
+- **Relevance Evaluation**: Automatically analyze references for their substantive contribution before inclusion.  
+- **Redundancy Detection**: Flag duplicate or trivial references across commits, files, or tasks.  
+- **Context Linking**: Suggest appropriate higher-level docs (designs, ADRs, meeting notes) when a code change touches multi-stage or cross-team items.  
+- **Placement Optimization**: Recommend centralization of references (e.g., in plan overviews, ADRs, or merge commit messages) rather than scattered low-value inline references.  
+- **Consistency Monitoring**: Ensure references align with team standards (e.g., ADR template, architecture repo, or external policy documents).  
+
+Contributors must treat agent recommendations as **first-pass reviews** but remain accountable for final human judgment.
+
+---
+
+## 4. Contextual References for Complex Items
+
+- Use **centralized references** for multi-stage features (e.g., architectural docs, research threads).  
+- Keep inline code comments light; push broader context into centralized documents.  
+- Model agents may auto-summarize complex chains of discussion and attach them as a single reference point.
+
+---
+
+## 5. Centralization of Broader Context
+
+- Store overarching context (design docs, proposals, workflows) in accessible, well-indexed places.  
+- Model agents should assist by **generating reference maps** that track where docs are cited across the codebase.
+
+---
+
+## 6. Focused Documentation
+
+- Documentation should explain **why** and **how** decisions are made, not just what was changed.  
+- Model agents can auto-generate first-pass explanations from commit metadata, diffs, and linked issues—but humans must refine them for accuracy and intent.
+
+---
+
+## 7. Review and Accountability
+
+- Reviewers and team leads must reject submissions containing unnecessary or low-quality documentation.  
+- Model agent outputs are aids, not replacements—contributors remain responsible for **final clarity and relevance**.
+
+---
+
+## 8. Continuous Improvement and Agent Feedback Loops
+
+- Encourage iterative development of model agents so their evaluations become more precise over time.  
+- Contributions should include **feedback on agent suggestions** (e.g., accepted, rejected, or corrected) to train better future outputs.  
+- Agents should log patterns of “rejected” suggestions for refinement.  
+
+---
+
+## 9. Workflow Overview (Mermaid Diagram)
+
+```mermaid
+flowchart TD
+    A[Contributor] -->|Writes Code & Draft Docs| B[Model Agent]
+    B -->|Evaluates References| C{Relevant?}
+    C -->|Yes| D[Suggest Placement & Context Links]
+    C -->|No| E[Flag Redundancy / Noise]
+    D --> F[Contributor Refines Docs]
+    E --> F
+    F --> G[Reviewer]
+    G -->|Approves / Requests Revisions| H[Final Documentation]
+    G -->|Feedback on Agent Suggestions| B
+```
+
+---
+
+✅ **Outcome:** By integrating disciplined contributor standards with **model agent augmentation**, the team achieves documentation that is consistently _relevant, concise, centralized, and decision-focused_. AI ensures coverage and noise reduction, while humans ensure precision and judgment.