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>
This commit is contained in:
yusyus
@@ -84,6 +84,7 @@ try:
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# Packaging tools
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package_skill_impl,
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upload_skill_impl,
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enhance_skill_impl,
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install_skill_impl,
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# Splitting tools
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split_config_impl,
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@@ -109,6 +110,7 @@ except ImportError:
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scrape_pdf_impl,
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package_skill_impl,
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upload_skill_impl,
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enhance_skill_impl,
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install_skill_impl,
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split_config_impl,
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generate_router_impl,
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@@ -397,24 +399,27 @@ async def scrape_pdf(
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@safe_tool_decorator(
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description="Package a skill directory into a .zip file ready for Claude upload. Automatically uploads if ANTHROPIC_API_KEY is set."
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description="Package skill directory into platform-specific format (ZIP for Claude/OpenAI/Markdown, tar.gz for Gemini). Supports all platforms: claude, gemini, openai, markdown. Automatically uploads if platform API key is set."
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)
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async def package_skill(
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skill_dir: str,
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target: str = "claude",
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auto_upload: bool = True,
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) -> str:
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"""
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Package a skill directory into a .zip file.
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Package skill directory for target LLM platform.
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Args:
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skill_dir: Path to skill directory (e.g., output/react/)
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auto_upload: Try to upload automatically if API key is available (default: true). If false, only package without upload attempt.
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skill_dir: Path to skill directory to package (e.g., output/react/)
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target: Target platform (default: 'claude'). Options: claude, gemini, openai, markdown
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auto_upload: Auto-upload after packaging if API key is available (default: true). Requires platform-specific API key: ANTHROPIC_API_KEY, GOOGLE_API_KEY, or OPENAI_API_KEY.
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Returns:
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Packaging results with .zip file path and upload status.
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Packaging results with file path and platform info.
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"""
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args = {
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"skill_dir": skill_dir,
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"target": target,
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"auto_upload": auto_upload,
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}
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result = await package_skill_impl(args)
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@@ -424,26 +429,74 @@ async def package_skill(
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@safe_tool_decorator(
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description="Upload a skill .zip file to Claude automatically (requires ANTHROPIC_API_KEY)"
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description="Upload skill package to target LLM platform API. Requires platform-specific API key. Supports: claude (Anthropic Skills API), gemini (Google Files API), openai (Assistants API). Does NOT support markdown."
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)
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async def upload_skill(skill_zip: str) -> str:
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async def upload_skill(
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skill_zip: str,
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target: str = "claude",
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api_key: str | None = None,
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) -> str:
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"""
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Upload a skill .zip file to Claude.
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Upload skill package to target platform.
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Args:
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skill_zip: Path to skill .zip file (e.g., output/react.zip)
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skill_zip: Path to skill package (.zip or .tar.gz, e.g., output/react.zip)
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target: Target platform (default: 'claude'). Options: claude, gemini, openai
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api_key: Optional API key (uses env var if not provided: ANTHROPIC_API_KEY, GOOGLE_API_KEY, or OPENAI_API_KEY)
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Returns:
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Upload results with success/error message.
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Upload results with skill ID and platform URL.
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"""
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result = await upload_skill_impl({"skill_zip": skill_zip})
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args = {
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"skill_zip": skill_zip,
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"target": target,
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}
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if api_key:
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args["api_key"] = api_key
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result = await upload_skill_impl(args)
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if isinstance(result, list) and result:
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return result[0].text if hasattr(result[0], "text") else str(result[0])
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return str(result)
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@safe_tool_decorator(
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description="Complete one-command workflow: fetch config → scrape docs → AI enhance (MANDATORY) → package → upload. Enhancement required for quality (3/10→9/10). Takes 20-45 min depending on config size. Automatically uploads to Claude if ANTHROPIC_API_KEY is set."
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description="Enhance SKILL.md with AI using target platform's model. Local mode uses Claude Code Max (no API key). API mode uses platform API (requires key). Transforms basic templates into comprehensive 500+ line guides with examples."
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)
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async def enhance_skill(
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skill_dir: str,
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target: str = "claude",
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mode: str = "local",
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api_key: str | None = None,
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) -> str:
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"""
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Enhance SKILL.md with AI.
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Args:
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skill_dir: Path to skill directory containing SKILL.md (e.g., output/react/)
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target: Target platform (default: 'claude'). Options: claude, gemini, openai
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mode: Enhancement mode (default: 'local'). Options: local (Claude Code, no API), api (uses platform API)
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api_key: Optional API key for 'api' mode (uses env var if not provided: ANTHROPIC_API_KEY, GOOGLE_API_KEY, or OPENAI_API_KEY)
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Returns:
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Enhancement results with backup location.
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"""
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args = {
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"skill_dir": skill_dir,
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"target": target,
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"mode": mode,
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}
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if api_key:
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args["api_key"] = api_key
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result = await enhance_skill_impl(args)
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if isinstance(result, list) and result:
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return result[0].text if hasattr(result[0], "text") else str(result[0])
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return str(result)
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@safe_tool_decorator(
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description="Complete one-command workflow: fetch config → scrape docs → AI enhance (MANDATORY) → package → upload. Enhancement required for quality (3/10→9/10). Takes 20-45 min depending on config size. Supports multiple LLM platforms: claude (default), gemini, openai, markdown. Auto-uploads if platform API key is set."
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)
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async def install_skill(
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config_name: str | None = None,
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@@ -452,6 +505,7 @@ async def install_skill(
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auto_upload: bool = True,
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unlimited: bool = False,
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dry_run: bool = False,
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target: str = "claude",
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) -> str:
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"""
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Complete one-command workflow to install a skill.
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@@ -460,9 +514,10 @@ async def install_skill(
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config_name: Config name from API (e.g., 'react', 'django'). Mutually exclusive with config_path. Tool will fetch this config from the official API before scraping.
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config_path: Path to existing config JSON file (e.g., 'configs/custom.json'). Mutually exclusive with config_name. Use this if you already have a config file.
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destination: Output directory for skill files (default: 'output')
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auto_upload: Auto-upload to Claude after packaging (requires ANTHROPIC_API_KEY). Default: true. Set to false to skip upload.
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auto_upload: Auto-upload after packaging (requires platform API key). Default: true. Set to false to skip upload.
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unlimited: Remove page limits during scraping (default: false). WARNING: Can take hours for large sites.
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dry_run: Preview workflow without executing (default: false). Shows all phases that would run.
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target: Target LLM platform (default: 'claude'). Options: claude, gemini, openai, markdown. Requires corresponding API key: ANTHROPIC_API_KEY, GOOGLE_API_KEY, or OPENAI_API_KEY.
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Returns:
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Workflow results with all phase statuses.
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@@ -472,6 +527,7 @@ async def install_skill(
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"auto_upload": auto_upload,
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"unlimited": unlimited,
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"dry_run": dry_run,
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"target": target,
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}
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if config_name:
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args["config_name"] = config_name
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@@ -490,7 +546,7 @@ async def install_skill(
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@safe_tool_decorator(
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description="Split large documentation config into multiple focused skills. For 10K+ page documentation."
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description="Split large configs into multiple focused skills. Supports documentation (10K+ pages) and unified multi-source configs. Auto-detects config type and recommends best strategy."
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)
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async def split_config(
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config_path: str,
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@@ -499,12 +555,16 @@ async def split_config(
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dry_run: bool = False,
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) -> str:
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"""
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Split large documentation config into multiple skills.
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Split large configs into multiple skills.
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Supports:
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- Documentation configs: Split by categories, size, or create router skills
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- Unified configs: Split by source type (documentation, github, pdf)
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Args:
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config_path: Path to config JSON file (e.g., configs/godot.json)
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strategy: Split strategy: auto, none, category, router, size (default: auto)
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target_pages: Target pages per skill (default: 5000)
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config_path: Path to config JSON file (e.g., configs/godot.json or configs/react_unified.json)
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strategy: Split strategy: auto, none, source, category, router, size (default: auto). 'source' is for unified configs.
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target_pages: Target pages per skill for doc configs (default: 5000)
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dry_run: Preview without saving files (default: false)
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Returns:
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