fix: Fix 2 critical CLI issues blocking production (Kimi QA)
**Critical Issues Fixed:** Issue #1: CLI Commands Were BROKEN ⚠️ CRITICAL - Problem: 4 CLI commands existed but failed at runtime with ImportError - Root Cause: Modules had example_usage() instead of main() functions - Impact: Users couldn't use quality, stream, update, multilang features **Fixed Files:** - src/skill_seekers/cli/quality_metrics.py - Renamed example_usage() → main() - Added argparse with --report, --output flags - Proper exit codes and error handling - src/skill_seekers/cli/streaming_ingest.py - Renamed example_usage() → main() - Added argparse with --chunk-size, --batch-size, --checkpoint flags - Supports both file and directory inputs - src/skill_seekers/cli/incremental_updater.py - Renamed example_usage() → main() - Added argparse with --check-changes, --generate-package, --apply-update flags - Proper error handling and exit codes - src/skill_seekers/cli/multilang_support.py - Renamed example_usage() → main() - Added argparse with --detect, --report, --export flags - Loads skill documents from directory Issue #2: Haystack Missing from Package Choices ⚠️ CRITICAL - Problem: Haystack adaptor worked but couldn't be used via CLI - Root Cause: package_skill.py missing "haystack" in --target choices - Impact: Users got "invalid choice" error when packaging for Haystack **Fixed:** - src/skill_seekers/cli/package_skill.py:188 - Added "haystack" to --target choices list - Now matches main.py choices (all 11 platforms) **Verification:** ✅ All 4 CLI commands now work: $ skill-seekers quality --help $ skill-seekers stream --help $ skill-seekers update --help $ skill-seekers multilang --help ✅ Haystack now available: $ skill-seekers package output/skill --target haystack ✅ All 164 adaptor tests still passing ✅ No regressions detected **Credits:** - Issues identified by: Kimi QA Review - Fixes implemented by: Claude Sonnet 4.5 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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@@ -185,7 +185,7 @@ Examples:
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parser.add_argument(
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"--target",
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choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "weaviate", "chroma", "faiss", "qdrant"],
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choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "haystack", "weaviate", "chroma", "faiss", "qdrant"],
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default="claude",
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help="Target LLM platform (default: claude)",
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)
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