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>
This commit is contained in:
Matt Van Horn
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"""xAI API client for X (Twitter) discovery."""
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import json
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import re
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from typing import Any, Dict, List, Optional
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from . import http
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# xAI uses chat completions endpoint
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XAI_CHAT_URL = "https://api.x.ai/v1/chat/completions"
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X_SEARCH_PROMPT = """You have access to real-time X (Twitter) data. Search for posts about: {topic}
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Focus on posts from {from_date} to {to_date}. Find 15-30 high-quality, relevant posts.
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IMPORTANT: Return ONLY valid JSON in this exact format, no other text:
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{{
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"items": [
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{{
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"text": "Post text content (truncated if long)",
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"url": "https://x.com/user/status/...",
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"author_handle": "username",
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"date": "YYYY-MM-DD or null if unknown",
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"engagement": {{
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"likes": 100,
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"reposts": 25,
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"replies": 15,
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"quotes": 5
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}},
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"why_relevant": "Brief explanation of relevance",
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"relevance": 0.85
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}}
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]
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}}
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Rules:
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- relevance is 0.0 to 1.0 (1.0 = highly relevant)
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- date must be YYYY-MM-DD format or null
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- engagement can be null if unknown
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- Include diverse voices/accounts if applicable
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- Prefer posts with substantive content, not just links"""
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def search_x(
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api_key: str,
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model: str,
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topic: str,
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from_date: str,
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to_date: str,
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mock_response: Optional[Dict] = None,
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) -> Dict[str, Any]:
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"""Search X for relevant posts using xAI API with live search.
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Args:
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api_key: xAI API key
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model: Model to use
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topic: Search topic
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from_date: Start date (YYYY-MM-DD)
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to_date: End date (YYYY-MM-DD)
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mock_response: Mock response for testing
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Returns:
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Raw API response
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"""
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if mock_response is not None:
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return mock_response
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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# Use chat completions format with search enabled
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payload = {
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"model": model,
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"messages": [
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{
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"role": "system",
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"content": "You are a research assistant with access to real-time X (Twitter) data. Search X and return structured results."
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},
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{
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"role": "user",
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"content": X_SEARCH_PROMPT.format(
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topic=topic,
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from_date=from_date,
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to_date=to_date,
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),
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}
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],
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"search_parameters": {
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"mode": "auto",
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"sources": [{"type": "x"}],
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"from_date": from_date,
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"to_date": to_date,
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},
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}
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return http.post(XAI_CHAT_URL, payload, headers=headers, timeout=90)
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def parse_x_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Parse xAI response to extract X items.
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Args:
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response: Raw API response
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Returns:
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List of item dicts
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"""
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items = []
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# Try to find the output text
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output_text = ""
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if "output" in response:
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output = response["output"]
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if isinstance(output, str):
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output_text = output
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elif isinstance(output, list):
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for item in output:
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if isinstance(item, dict):
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if item.get("type") == "message":
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content = item.get("content", [])
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for c in content:
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if isinstance(c, dict) and c.get("type") == "output_text":
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output_text = c.get("text", "")
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break
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elif "text" in item:
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output_text = item["text"]
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elif isinstance(item, str):
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output_text = item
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if output_text:
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break
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# Also check for choices (older format)
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if not output_text and "choices" in response:
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for choice in response["choices"]:
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if "message" in choice:
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output_text = choice["message"].get("content", "")
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break
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if not output_text:
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return items
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# Extract JSON from the response
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json_match = re.search(r'\{[\s\S]*"items"[\s\S]*\}', output_text)
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if json_match:
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try:
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data = json.loads(json_match.group())
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items = data.get("items", [])
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except json.JSONDecodeError:
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pass
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# Validate and clean items
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clean_items = []
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for i, item in enumerate(items):
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if not isinstance(item, dict):
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continue
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url = item.get("url", "")
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if not url:
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continue
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# Parse engagement
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engagement = None
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eng_raw = item.get("engagement")
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if isinstance(eng_raw, dict):
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engagement = {
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"likes": int(eng_raw.get("likes", 0)) if eng_raw.get("likes") else None,
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"reposts": int(eng_raw.get("reposts", 0)) if eng_raw.get("reposts") else None,
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"replies": int(eng_raw.get("replies", 0)) if eng_raw.get("replies") else None,
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"quotes": int(eng_raw.get("quotes", 0)) if eng_raw.get("quotes") else None,
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}
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clean_item = {
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"id": f"X{i+1}",
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"text": str(item.get("text", "")).strip()[:500], # Truncate long text
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"url": url,
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"author_handle": str(item.get("author_handle", "")).strip().lstrip("@"),
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"date": item.get("date"),
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"engagement": engagement,
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"why_relevant": str(item.get("why_relevant", "")).strip(),
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"relevance": min(1.0, max(0.0, float(item.get("relevance", 0.5)))),
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}
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# Validate date format
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if clean_item["date"]:
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if not re.match(r'^\d{4}-\d{2}-\d{2}$', str(clean_item["date"])):
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clean_item["date"] = None
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clean_items.append(clean_item)
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return clean_items
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