docs: merge notification system docs into single Native-First guide

- Consolidate 5 notification-system-* files into doc/notification-system.md
- Add web-push cleanup guide and Start-on-Login glossary entry
- Configure markdownlint for consistent formatting
- Remove web-push references, focus on native OS scheduling

Reduces maintenance overhead while preserving all essential information
in a single, well-formatted reference document.

.cursor/rules/docs/documentation_references_model_agents.mdc

@@ -12,19 +12,23 @@ To ensure clarity, efficiency, and high-value documentation within code and proj
 ---
 
 ## 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.  
+
+- 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.  
@@ -36,6 +40,7 @@ Contributors must treat agent recommendations as **first-pass reviews** but rema
 ---
 
 ## 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.
@@ -43,24 +48,28 @@ Contributors must treat agent recommendations as **first-pass reviews** but rema
 ---
 
 ## 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.  
@@ -84,4 +93,4 @@ flowchart TD
 
 ---
 
-✅ **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.  
+✅ **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.