feat(extractor): implement multi-engine article content extractor

- Added scripts/extract_article_contents.py for batch scraping with stealth Foxcape and triple extraction (Trafilatura, Newspaper4k, Readability)
- Created unit, integration, and E2E test suite in tests/test_extract_article_contents.py (90/90 passing)
- Updated specs/003-article-content-extractor and README.md with usage documentation and CLI contracts
- Passed ruff linting/formatting and mypy type checking cleanly

src/parser.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 import re
-from typing import List, Tuple
 
 
 def strip_markdown(markdown_text: str) -> str:
@@ -40,7 +39,7 @@ def strip_markdown(markdown_text: str) -> str:
     return text
 
 
-def extract_sentences(text: str) -> List[str]:
+def extract_sentences(text: str) -> list[str]:
     """Split text into individual sentences."""
     # Split by period, exclamation, question mark followed by space or newline
     raw_sentences = re.split(r"(?<=[.!?])\s+", text.strip())
@@ -48,7 +47,9 @@ def extract_sentences(text: str) -> List[str]:
     return sentences
 
 
-def extract_evidence_snippets(markdown_text: str, match_terms: List[str], max_snippets: int = 3) -> List[str]:
+def extract_evidence_snippets(
+    markdown_text: str, match_terms: list[str], max_snippets: int = 3
+) -> list[str]:
     """
     Extract relevant sentence excerpts from Markdown text that contain any of the given match terms.
     Preserves original phrasing and formats as clean evidence.
@@ -62,7 +63,7 @@ def extract_evidence_snippets(markdown_text: str, match_terms: List[str], max_sn
         sentences = [plain_text]
 
     lower_terms = [t.lower() for t in match_terms if t]
-    evidence: List[str] = []
+    evidence: list[str] = []
 
     for sentence in sentences:
         lower_sent = sentence.lower()