docs: Complete Framework-Hooks documentation overhaul

Major documentation update focused on technical accuracy and developer clarity:

Documentation Changes:
- Rewrote README.md with focus on hooks system architecture
- Updated all core docs (Overview, Integration, Performance) to match implementation
- Created 6 missing configuration docs for undocumented YAML files
- Updated all 7 hook docs to reflect actual Python implementations
- Created docs for 2 missing shared modules (intelligence_engine, validate_system)
- Updated all 5 pattern docs with real YAML examples
- Added 4 essential operational docs (INSTALLATION, TROUBLESHOOTING, CONFIGURATION, QUICK_REFERENCE)

Key Improvements:
- Removed all marketing language in favor of humble technical documentation
- Fixed critical configuration discrepancies (logging defaults, performance targets)
- Used actual code examples and configuration from implementation
- Complete coverage: 15 configs, 10 modules, 7 hooks, 3 pattern tiers
- Based all documentation on actual file review and code analysis

Technical Accuracy:
- Corrected performance targets to match performance.yaml
- Fixed timeout values from settings.json (10-15 seconds)
- Updated module count and descriptions to match actual shared/ directory
- Aligned all examples with actual YAML and Python implementations

The documentation now provides accurate, practical information for developers
working with the Framework-Hooks system, focusing on what it actually does
rather than aspirational features.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Framework-Hooks/docs/Hooks/subagent_stop.md

@@ -2,31 +2,26 @@
 
 ## Purpose
 
-The `subagent_stop` hook implements **MODE_Task_Management delegation coordination and analytics** by analyzing subagent task completion performance and providing comprehensive delegation effectiveness measurement. This hook specializes in **task delegation analytics and coordination**, measuring multi-agent collaboration effectiveness and optimizing wave orchestration strategies.
+The `subagent_stop` hook analyzes subagent task completion and provides delegation effectiveness measurement after subagent operations. It implements MODE_Task_Management delegation coordination analytics for multi-agent collaboration optimization.
 
-**Core Responsibilities:**
-- Analyze subagent task completion and performance metrics
-- Measure delegation effectiveness and coordination success
-- Learn from parallel execution patterns and cross-agent coordination
-- Optimize wave orchestration strategies for multi-agent operations
-- Coordinate cross-agent knowledge sharing and learning
-- Track task management framework effectiveness across delegated operations
+**Core Implementation**: Measures delegation effectiveness, analyzes cross-agent coordination patterns, and optimizes wave orchestration strategies with a target execution time of <150ms.
 
 ## Execution Context
 
-The `subagent_stop` hook executes **after subagent operations complete** in Claude Code, specifically when:
+The subagent_stop hook runs after subagent operations complete in Claude Code. According to `settings.json`, it has a 15-second timeout and executes via: `python3 ~/.claude/hooks/subagent_stop.py`
 
-- **Subagent Task Completion**: When individual subagents finish their delegated tasks
-- **Multi-Agent Coordination End**: After parallel task execution completes
-- **Wave Orchestration Completion**: When wave-based task coordination finishes
-- **Delegation Strategy Assessment**: For analyzing effectiveness of different delegation approaches
-- **Cross-Agent Learning**: When coordination patterns need to be captured for future optimization
+**Execution Triggers:**
+- Individual subagent task completion
+- Multi-agent coordination end
+- Wave orchestration completion  
+- Delegation strategy assessment
 
-**Integration Points:**
-- Integrates with Claude Code's subagent delegation system
-- Coordinates with MODE_Task_Management for delegation analytics
-- Synchronizes with wave orchestration for multi-agent coordination
-- Links with learning engine for continuous delegation improvement
+**Actual Processing:**
+1. Receives subagent completion data via stdin (JSON)
+2. Analyzes delegation effectiveness and coordination patterns
+3. Measures multi-agent collaboration success
+4. Records learning events for delegation optimization
+5. Outputs coordination analytics via stdout (JSON)
 
 ## Performance Target