Add topic/flavor categorization and category browsing

- New taxonomy module: single source of truth for 6 topics x 5 flavors,
  shared by the LLM response schema (enum-constrained) and validation.
- Classifier now assigns one topic + one flavor per article; json_schema
  enums force valid values, with coercion as a safety net.
- article_scores gains topic/flavor columns via an idempotent migration.
- New 'list-category' command to browse by topic and/or flavor, ranked by
  composite score.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

goodnews/taxonomy.py

@@ -0,0 +1,54 @@
+"""Single source of truth for article topic/flavor categories.
+
+Both the LLM response schema (enum constraints) and the post-hoc validation in
+normalize_scores import from here, so the allowed values can never drift apart.
+Adjusting a category here + re-running `classify` is all it takes to reshape the
+browsable feeds.
+"""
+
+from __future__ import annotations
+
+
+# Topical axis: what the story is primarily about.
+TOPICS: dict[str, str] = {
+    "science": "research, discoveries, space, physics, technology",
+    "environment": "conservation, climate solutions, ecosystems, clean energy",
+    "health": "medicine, wellbeing, mental health, public health",
+    "community": "local action, humanitarian work, social progress, kindness, fair work",
+    "culture": "arts, history, heritage, sport, human-interest",
+    "animals": "wildlife, nature discoveries, charming animal stories",
+}
+
+# Tonal axis: why the story is worth surfacing in a calm, uplifting digest.
+FLAVORS: dict[str, str] = {
+    "breakthrough": "a significant advance or innovation with clear public benefit",
+    "discovery": "newly found or learned; calm and fascinating, low on agency",
+    "solution": "people actively repairing, restoring, or solving a problem",
+    "feelgood": "a heartwarming human, community, or kindness story",
+    "perspective": "useful advice, insight, or framing the reader can apply",
+}
+
+DEFAULT_TOPIC = "science"
+DEFAULT_FLAVOR = "discovery"
+
+
+def coerce_topic(value: object) -> str:
+    text = str(value or "").strip().lower()
+    return text if text in TOPICS else DEFAULT_TOPIC
+
+
+def coerce_flavor(value: object) -> str:
+    text = str(value or "").strip().lower()
+    return text if text in FLAVORS else DEFAULT_FLAVOR
+
+
+def _bullet_list(mapping: dict[str, str]) -> str:
+    return "\n".join(f"- {key}: {desc}" for key, desc in mapping.items())
+
+
+def topics_prompt_block() -> str:
+    return _bullet_list(TOPICS)
+
+
+def flavors_prompt_block() -> str:
+    return _bullet_list(FLAVORS)