Restructure marketplace for isolated plugin architecture

- Organize 62 plugins into isolated directories under plugins/
- Consolidate tools and workflows into commands/ following Anthropic conventions
- Update marketplace.json with isolated source paths for each plugin
- Revise README to reflect plugin-based structure and token efficiency
- Remove shared resource directories (agents/, tools/, workflows/)

Each plugin now contains only its specific agents and commands, enabling
granular installation and minimal token usage. Installing a single plugin
loads only its resources rather than the entire marketplace.

Structure: plugins/{plugin-name}/{agents/,commands/}

plugins/context-management/commands/context-save.md

@@ -0,0 +1,155 @@
+# Context Save Tool: Intelligent Context Management Specialist
+
+## Role and Purpose
+An elite context engineering specialist focused on comprehensive, semantic, and dynamically adaptable context preservation across AI workflows. This tool orchestrates advanced context capture, serialization, and retrieval strategies to maintain institutional knowledge and enable seamless multi-session collaboration.
+
+## Context Management Overview
+The Context Save Tool is a sophisticated context engineering solution designed to:
+- Capture comprehensive project state and knowledge
+- Enable semantic context retrieval
+- Support multi-agent workflow coordination
+- Preserve architectural decisions and project evolution
+- Facilitate intelligent knowledge transfer
+
+## Requirements and Argument Handling
+
+### Input Parameters
+- `$PROJECT_ROOT`: Absolute path to project root
+- `$CONTEXT_TYPE`: Granularity of context capture (minimal, standard, comprehensive)
+- `$STORAGE_FORMAT`: Preferred storage format (json, markdown, vector)
+- `$TAGS`: Optional semantic tags for context categorization
+
+## Context Extraction Strategies
+
+### 1. Semantic Information Identification
+- Extract high-level architectural patterns
+- Capture decision-making rationales
+- Identify cross-cutting concerns and dependencies
+- Map implicit knowledge structures
+
+### 2. State Serialization Patterns
+- Use JSON Schema for structured representation
+- Support nested, hierarchical context models
+- Implement type-safe serialization
+- Enable lossless context reconstruction
+
+### 3. Multi-Session Context Management
+- Generate unique context fingerprints
+- Support version control for context artifacts
+- Implement context drift detection
+- Create semantic diff capabilities
+
+### 4. Context Compression Techniques
+- Use advanced compression algorithms
+- Support lossy and lossless compression modes
+- Implement semantic token reduction
+- Optimize storage efficiency
+
+### 5. Vector Database Integration
+Supported Vector Databases:
+- Pinecone
+- Weaviate
+- Qdrant
+
+Integration Features:
+- Semantic embedding generation
+- Vector index construction
+- Similarity-based context retrieval
+- Multi-dimensional knowledge mapping
+
+### 6. Knowledge Graph Construction
+- Extract relational metadata
+- Create ontological representations
+- Support cross-domain knowledge linking
+- Enable inference-based context expansion
+
+### 7. Storage Format Selection
+Supported Formats:
+- Structured JSON
+- Markdown with frontmatter
+- Protocol Buffers
+- MessagePack
+- YAML with semantic annotations
+
+## Code Examples
+
+### 1. Context Extraction
+```python
+def extract_project_context(project_root, context_type='standard'):
+    context = {
+        'project_metadata': extract_project_metadata(project_root),
+        'architectural_decisions': analyze_architecture(project_root),
+        'dependency_graph': build_dependency_graph(project_root),
+        'semantic_tags': generate_semantic_tags(project_root)
+    }
+    return context
+```
+
+### 2. State Serialization Schema
+```json
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "type": "object",
+  "properties": {
+    "project_name": {"type": "string"},
+    "version": {"type": "string"},
+    "context_fingerprint": {"type": "string"},
+    "captured_at": {"type": "string", "format": "date-time"},
+    "architectural_decisions": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "properties": {
+          "decision_type": {"type": "string"},
+          "rationale": {"type": "string"},
+          "impact_score": {"type": "number"}
+        }
+      }
+    }
+  }
+}
+```
+
+### 3. Context Compression Algorithm
+```python
+def compress_context(context, compression_level='standard'):
+    strategies = {
+        'minimal': remove_redundant_tokens,
+        'standard': semantic_compression,
+        'comprehensive': advanced_vector_compression
+    }
+    compressor = strategies.get(compression_level, semantic_compression)
+    return compressor(context)
+```
+
+## Reference Workflows
+
+### Workflow 1: Project Onboarding Context Capture
+1. Analyze project structure
+2. Extract architectural decisions
+3. Generate semantic embeddings
+4. Store in vector database
+5. Create markdown summary
+
+### Workflow 2: Long-Running Session Context Management
+1. Periodically capture context snapshots
+2. Detect significant architectural changes
+3. Version and archive context
+4. Enable selective context restoration
+
+## Advanced Integration Capabilities
+- Real-time context synchronization
+- Cross-platform context portability
+- Compliance with enterprise knowledge management standards
+- Support for multi-modal context representation
+
+## Limitations and Considerations
+- Sensitive information must be explicitly excluded
+- Context capture has computational overhead
+- Requires careful configuration for optimal performance
+
+## Future Roadmap
+- Improved ML-driven context compression
+- Enhanced cross-domain knowledge transfer
+- Real-time collaborative context editing
+- Predictive context recommendation systems