feat: Complete multi-platform feature parity implementation

This commit implements full feature parity across all platforms (Claude, Gemini, OpenAI, Markdown) and all skill modes (Docs, GitHub, PDF, Unified, Local Repo).

## Core Changes

### Phase 1: MCP Package Tool Multi-Platform Support
- Added `target` parameter to `package_skill_tool()` in packaging_tools.py
- Updated MCP server definition to expose `target` parameter
- Platform-specific packaging: ZIP for Claude/OpenAI/Markdown, tar.gz for Gemini
- Platform-specific output messages and instructions

### Phase 2: MCP Upload Tool Multi-Platform Support
- Added `target` parameter to `upload_skill_tool()` in packaging_tools.py
- Added optional `api_key` parameter for API key override
- Updated MCP server definition with platform selection
- Platform-specific API key validation (ANTHROPIC_API_KEY, GOOGLE_API_KEY, OPENAI_API_KEY)
- Graceful handling of Markdown (upload not supported)

### Phase 3: Standalone MCP Enhancement Tool
- Created new `enhance_skill_tool()` function (140+ lines)
- Supports both 'local' mode (Claude Code Max) and 'api' mode (platform APIs)
- Added MCP server definition for `enhance_skill`
- Works with Claude, Gemini, and OpenAI
- Integrated into MCP tools exports

### Phase 4: Unified Config Splitting Support
- Added `is_unified_config()` method to detect multi-source configs
- Implemented `split_by_source()` method to split by source type (docs, github, pdf)
- Updated auto-detection to recommend 'source' strategy for unified configs
- Added 'source' to valid CLI strategy choices
- Updated MCP tool documentation for unified support

### Phase 5: Comprehensive Feature Matrix Documentation
- Created `docs/FEATURE_MATRIX.md` (~400 lines)
- Complete platform comparison tables
- Skill mode support matrix
- CLI and MCP tool coverage matrices
- Platform-specific notes and FAQs
- Workflow examples for each combination
- Updated README.md with feature matrix section

## Files Modified

**Core Implementation:**
- src/skill_seekers/mcp/tools/packaging_tools.py
- src/skill_seekers/mcp/server_fastmcp.py
- src/skill_seekers/mcp/tools/__init__.py
- src/skill_seekers/cli/split_config.py
- src/skill_seekers/mcp/tools/splitting_tools.py

**Documentation:**
- docs/FEATURE_MATRIX.md (NEW)
- README.md

**Tests:**
- tests/test_install_multiplatform.py (already existed)

## Test Results
- ✅ 699 tests passing
- ✅ All multiplatform install tests passing (6/6)
- ✅ No regressions introduced
- ✅ All syntax checks passed
- ✅ Import tests successful

## Breaking Changes
None - all changes are backward compatible with default `target='claude'`

## Migration Guide
Existing MCP calls without `target` parameter will continue to work (defaults to 'claude').

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

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

src/skill_seekers/cli/install_skill.py

@@ -60,17 +60,24 @@ Examples:
   # Preview workflow (dry run)
   skill-seekers install --config react --dry-run
 
+  # Install for Gemini instead of Claude
+  skill-seekers install --config react --target gemini
+
+  # Install for OpenAI ChatGPT
+  skill-seekers install --config fastapi --target openai
+
 Important:
   - Enhancement is MANDATORY (30-60 sec) for quality (3/10→9/10)
   - Total time: 20-45 minutes (mostly scraping)
-  - Auto-uploads to Claude if ANTHROPIC_API_KEY is set
+  - Multi-platform support: claude (default), gemini, openai, markdown
+  - Auto-uploads if API key is set (ANTHROPIC_API_KEY, GOOGLE_API_KEY, or OPENAI_API_KEY)
 
 Phases:
   1. Fetch config (if config name provided)
   2. Scrape documentation
   3. AI Enhancement (MANDATORY - no skip option)
-  4. Package to .zip
-  5. Upload to Claude (optional)
+  4. Package for target platform (ZIP or tar.gz)
+  5. Upload to target platform (optional)
 """
     )
 
@@ -104,6 +111,13 @@ Phases:
         help="Preview workflow without executing"
     )
 
+    parser.add_argument(
+        "--target",
+        choices=['claude', 'gemini', 'openai', 'markdown'],
+        default='claude',
+        help="Target LLM platform (default: claude)"
+    )
+
     args = parser.parse_args()
 
     # Determine if config is a name or path
@@ -124,7 +138,8 @@ Phases:
         "destination": args.destination,
         "auto_upload": not args.no_upload,
         "unlimited": args.unlimited,
-        "dry_run": args.dry_run
+        "dry_run": args.dry_run,
+        "target": args.target
     }
 
     # Run async tool