Initial commit: last30days skill

Research topics across Reddit + X from the last 30 days using OpenAI and xAI APIs. Features: - Auto model selection (GPT-5.x, Grok-3) - Popularity-aware scoring (relevance + recency + engagement) - Reddit thread enrichment with real metrics - Near-duplicate detection - Multiple emit modes (compact, json, context, path) - 24h caching with --refresh bypass - NUX for API key setup - 87 passing unit tests Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-23 12:37:31 -08:00
commit 5ca4829be4
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+"""OpenAI Responses API client for Reddit discovery."""
+
+import json
+import re
+from typing import Any, Dict, List, Optional
+
+from . import http
+
+OPENAI_RESPONSES_URL = "https://api.openai.com/v1/responses"
+
+REDDIT_SEARCH_PROMPT = """Search Reddit for discussions about: {topic}
+
+Focus on threads from the last 30 days. Find 15-30 high-quality, relevant threads.
+
+IMPORTANT: Return ONLY valid JSON in this exact format, no other text:
+{{
+  "items": [
+    {{
+      "title": "Thread title",
+      "url": "https://reddit.com/r/...",
+      "subreddit": "subreddit_name",
+      "date": "YYYY-MM-DD or null if unknown",
+      "why_relevant": "Brief explanation of relevance",
+      "relevance": 0.85
+    }}
+  ]
+}}
+
+Rules:
+- relevance is 0.0 to 1.0 (1.0 = highly relevant)
+- date must be YYYY-MM-DD format or null
+- Include diverse subreddits if applicable
+- Prefer threads with substantive discussions
+- Do NOT include engagement metrics (upvotes, comments) - those will be fetched separately"""
+
+
+def search_reddit(
+    api_key: str,
+    model: str,
+    topic: str,
+    mock_response: Optional[Dict] = None,
+) -> Dict[str, Any]:
+    """Search Reddit for relevant threads using OpenAI Responses API.
+
+    Args:
+        api_key: OpenAI API key
+        model: Model to use
+        topic: Search topic
+        mock_response: Mock response for testing
+
+    Returns:
+        Raw API response
+    """
+    if mock_response is not None:
+        return mock_response
+
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json",
+    }
+
+    payload = {
+        "model": model,
+        "tools": [
+            {
+                "type": "web_search",
+                "filters": {
+                    "allowed_domains": ["reddit.com"]
+                }
+            }
+        ],
+        "include": ["web_search_call.action.sources"],
+        "input": REDDIT_SEARCH_PROMPT.format(topic=topic),
+    }
+
+    return http.post(OPENAI_RESPONSES_URL, payload, headers=headers, timeout=60)
+
+
+def parse_reddit_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Parse OpenAI response to extract Reddit items.
+
+    Args:
+        response: Raw API response
+
+    Returns:
+        List of item dicts
+    """
+    items = []
+
+    # Try to find the output text
+    output_text = ""
+    if "output" in response:
+        output = response["output"]
+        if isinstance(output, str):
+            output_text = output
+        elif isinstance(output, list):
+            for item in output:
+                if isinstance(item, dict):
+                    if item.get("type") == "message":
+                        content = item.get("content", [])
+                        for c in content:
+                            if isinstance(c, dict) and c.get("type") == "output_text":
+                                output_text = c.get("text", "")
+                                break
+                    elif "text" in item:
+                        output_text = item["text"]
+                elif isinstance(item, str):
+                    output_text = item
+                if output_text:
+                    break
+
+    # Also check for choices (older format)
+    if not output_text and "choices" in response:
+        for choice in response["choices"]:
+            if "message" in choice:
+                output_text = choice["message"].get("content", "")
+                break
+
+    if not output_text:
+        return items
+
+    # Extract JSON from the response
+    json_match = re.search(r'\{[\s\S]*"items"[\s\S]*\}', output_text)
+    if json_match:
+        try:
+            data = json.loads(json_match.group())
+            items = data.get("items", [])
+        except json.JSONDecodeError:
+            pass
+
+    # Validate and clean items
+    clean_items = []
+    for i, item in enumerate(items):
+        if not isinstance(item, dict):
+            continue
+
+        url = item.get("url", "")
+        if not url or "reddit.com" not in url:
+            continue
+
+        clean_item = {
+            "id": f"R{i+1}",
+            "title": str(item.get("title", "")).strip(),
+            "url": url,
+            "subreddit": str(item.get("subreddit", "")).strip().lstrip("r/"),
+            "date": item.get("date"),
+            "why_relevant": str(item.get("why_relevant", "")).strip(),
+            "relevance": min(1.0, max(0.0, float(item.get("relevance", 0.5)))),
+        }
+
+        # Validate date format
+        if clean_item["date"]:
+            if not re.match(r'^\d{4}-\d{2}-\d{2}$', str(clean_item["date"])):
+                clean_item["date"] = None
+
+        clean_items.append(clean_item)
+
+    return clean_items