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/llm.py

@@ -7,6 +7,15 @@ import urllib.error
 import urllib.request
 from dataclasses import dataclass
 
+from .taxonomy import (
+    FLAVORS,
+    TOPICS,
+    coerce_flavor,
+    coerce_topic,
+    flavors_prompt_block,
+    topics_prompt_block,
+)
+
 
 DEFAULT_BASE_URL = "http://127.0.0.1:1234/v1"
 DEFAULT_MODEL = "gpt-oss"
@@ -29,6 +38,8 @@ CLASSIFICATION_SCHEMA = {
         "novelty_score",
         "pr_risk_score",
         "accepted",
+        "topic",
+        "flavor",
         "reason_code",
         "reason_text",
     ],
@@ -41,6 +52,8 @@ CLASSIFICATION_SCHEMA = {
         "novelty_score": _SCORE_FIELD,
         "pr_risk_score": _SCORE_FIELD,
         "accepted": {"type": "boolean"},
+        "topic": {"type": "string", "enum": list(TOPICS)},
+        "flavor": {"type": "string", "enum": list(FLAVORS)},
         "reason_code": {"type": "string"},
         "reason_text": {"type": "string"},
     },
@@ -61,8 +74,16 @@ Judge emotional aftertaste, not simple positivity. Accept stories that leave a r
 
 Reject stories centered on fear, outrage, partisan conflict, crime, tragedy, disaster repetition, celebrity drama, market panic, or corporate PR without clear public benefit.
 
+Also assign one topic and one flavor, choosing the single best fit.
+
+Topic (what the story is about):
+{topics}
+
+Flavor (why it belongs in a calm, uplifting digest):
+{flavors}
+
 Return only JSON with this exact shape:
-{
+{{
   "constructive_score": 0,
   "cortisol_score": 0,
   "ragebait_score": 0,
@@ -71,10 +92,12 @@ Return only JSON with this exact shape:
   "novelty_score": 0,
   "pr_risk_score": 0,
   "accepted": false,
+  "topic": "one_of_the_allowed_topics",
+  "flavor": "one_of_the_allowed_flavors",
   "reason_code": "short_snake_case",
   "reason_text": "one concise sentence"
-}
-"""
+}}
+""".format(topics=topics_prompt_block(), flavors=flavors_prompt_block())
 
 
 @dataclass
@@ -218,6 +241,8 @@ def normalize_scores(data: dict, model_name: str) -> dict:
         "novelty_score": _bounded_int(data.get("novelty_score")),
         "pr_risk_score": _bounded_int(data.get("pr_risk_score")),
         "accepted": 1 if bool(data.get("accepted")) else 0,
+        "topic": coerce_topic(data.get("topic")),
+        "flavor": coerce_flavor(data.get("flavor")),
         "reason_code": str(data.get("reason_code") or "model_no_reason")[:120],
         "reason_text": str(data.get("reason_text") or "")[:1000],
         "model_name": model_name,
@@ -230,9 +255,9 @@ def upsert_article_score(conn: sqlite3.Connection, article_id: int, scores: dict
         INSERT INTO article_scores (
             article_id, constructive_score, cortisol_score, ragebait_score,
             agency_score, human_benefit_score, novelty_score, pr_risk_score,
-            accepted, reason_code, reason_text, model_name, scored_at
+            accepted, topic, flavor, reason_code, reason_text, model_name, scored_at
         )
-        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
         ON CONFLICT(article_id) DO UPDATE SET
             constructive_score = excluded.constructive_score,
             cortisol_score = excluded.cortisol_score,
@@ -242,6 +267,8 @@ def upsert_article_score(conn: sqlite3.Connection, article_id: int, scores: dict
             novelty_score = excluded.novelty_score,
             pr_risk_score = excluded.pr_risk_score,
             accepted = excluded.accepted,
+            topic = excluded.topic,
+            flavor = excluded.flavor,
             reason_code = excluded.reason_code,
             reason_text = excluded.reason_text,
             model_name = excluded.model_name,
@@ -257,6 +284,8 @@ def upsert_article_score(conn: sqlite3.Connection, article_id: int, scores: dict
             scores["novelty_score"],
             scores["pr_risk_score"],
             scores["accepted"],
+            scores["topic"],
+            scores["flavor"],
             scores["reason_code"],
             scores["reason_text"],
             scores["model_name"],