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Daily movie and TV show info aggregator with LLM-generated highlights, multi-source scraping, and smart ranking
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npx skills add https://github.com/Aradotso/hermes-skills --skill moontv-openclaw-skill명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SOC 직업 분류 기준
SKILL.md 표시 중
| name | moontv-openclaw-skill |
| description | Daily movie and TV show info aggregator with LLM-generated highlights, multi-source scraping, and smart ranking |
| triggers | ["set up moontv daily feed","create a movie and TV show recommendation system","scrape CMS movie sources","generate daily movie highlights with LLM","build a watchlist tracker","aggregate multiple video sources","rank movies by douban score and popularity","create automated daily media reports"] |
Skill by ara.so — Hermes Skills collection.
This skill enables AI agents to help developers use MoonTV OpenClaw, a Python-based movie and TV show aggregator that scrapes multiple CMS sources, ranks content by Douban scores and popularity, generates LLM-powered highlights, and produces daily Markdown reports with watchlist tracking.
MoonTV OpenClaw:
vod_name, keeping first occurrencegit clone https://github.com/doane2002cn/moontv-openclaw-skill.git
cd moontv-openclaw-skill
# Copy environment template
cp .env.example .env
Edit .env:
# Required: MoonTV gateway and play URL
MOONTV_GATEWAY=https://moontv-api.12879737.xyz
MOONTV_PLAY_URL=https://moontv.dduan2002cn.xyz/play
# Optional: Watchlist configuration
WATCHLIST_FILE=config/watchlist.json
# Optional: LLM for highlights (auto-fallback if not set)
LLM_GATEWAY=https://api.gptgod.online/v1
LLM_MODEL=gpt-4o-mini
LLM_API_KEY=your-api-key-from-env
Never hardcode API keys. Use environment variables or secret managers.
moontv-openclaw/
├── scripts/
│ ├── moontv_daily.py # Main scraper & orchestrator
│ ├── report_template.py # Markdown report renderer
│ ├── highlight_generator.py # LLM highlight generator
│ └── test_moontv.py # Test suite
├── config/
│ └── watchlist.json # Watchlist configuration
├── output/ # Generated reports (auto-created)
└── .env # Environment variables
cd scripts
python moontv_daily.py
This will:
vod_nameoutput/cd scripts
python -m pytest test_moontv.py -v
moontv_daily.py)Fetch Gateway Sources:
import requests
import os
from dotenv import load_dotenv
load_dotenv()
def fetch_gateway():
"""Fetch available CMS sources from gateway"""
gateway_url = os.getenv("MOONTV_GATEWAY")
response = requests.get(f"{gateway_url}/api/resource/sources", timeout=10)
response.raise_for_status()
data = response.json()
if data["code"] != 0 or not data["data"]:
raise Exception("Gateway returned no sources")
return data["data"] # List of dicts: [{"name": "...", "api": "..."}, ...]
Scrape CMS Source:
def fetch_cms_data(api_url):
"""Fetch today's data from a single CMS source"""
response = requests.get(
f"{api_url}?ac=videolist&t=1,2,3,4,5",
timeout=10
)
response.raise_for_status()
return response.json()
Deduplicate:
def deduplicate(items):
"""Remove duplicates by vod_name, keep first occurrence"""
seen = set()
unique = []
for item in items:
name = item.get("vod_name")
if name and name not in seen:
seen.add(name)
unique.append(item)
return unique
Classify Items:
def classify(items):
"""Classify into 5 categories with priority"""
categories = {
"电影": [], "剧集": [], "综艺": [], "短剧": [], "福利": []
}
for item in items:
type_name = item.get("type_name", "")
# Priority order: 电影 > 剧集 > 综艺 > 短剧 > 福利
if "电影" in type_name:
categories["电影"].append(item)
elif any(x in type_name for x in ["连续", "电视剧", "美剧", "韩剧"]):
categories["剧集"].append(item)
elif "综艺" in type_name:
categories["综艺"].append(item)
elif "短剧" in type_name:
categories["短剧"].append(item)
else:
categories["福利"].append(item)
return categories
Scoring Algorithm:
from datetime import datetime, timedelta
def calculate_score(item, max_hits):
"""Dual-path weighted scoring"""
douban = float(item.get("vod_douban_score", 0))
hits = int(item.get("vod_hits", 0))
time_str = item.get("vod_time", "")
# Normalize popularity (0-10)
normalized_hits = (hits / max_hits * 10) if max_hits > 0 else 0
# Time bonus (10 if within 12 hours, else 0)
time_bonus = 0
try:
vod_time = datetime.fromisoformat(time_str)
if datetime.now() - vod_time < timedelta(hours=12):
time_bonus = 10
except:
pass
# Dual-path scoring
if douban > 0:
score = douban * 0.6 + normalized_hits * 0.3 + time_bonus * 0.1
else:
baseline = 5.0
score = normalized_hits * 0.6 + time_bonus * 0.3 + baseline * 0.1
return round(score, 2)
report_template.py)Format Single Item:
def format_item(item, rank, play_base_url):
"""Format a single movie/TV item as Markdown"""
name = item.get("vod_name", "未知")
score = item.get("综合评分", 0)
douban = item.get("vod_douban_score", 0)
vod_id = item.get("vod_id", "")
episode = extract_episode(item)
# Build play URL
play_url = f"{play_base_url}?id={vod_id}"
# Format output
lines = [
f"**{rank}. [{name}]({play_url})**",
f" - 综合评分：{score}"
]
if douban > 0:
lines.append(f" - 豆瓣评分：{douban}")
if episode:
lines.append(f" - 最新：{episode}")
# Add highlight if available
if "亮点" in item and item["亮点"]:
lines.append(f" - 💡 {item['亮点']}")
return "\n".join(lines)
Render Full Report:
def render_report(top_items, watchlist_updates, date_str):
"""Render complete Markdown report"""
lines = [
f"# 📺 MoonTV 每日精选 ({date_str})",
"",
"---",
""
]
# Categories
categories = ["电影", "剧集", "综艺", "短剧", "福利"]
for cat in categories:
if cat in top_items and top_items[cat]:
lines.append(f"## {cat} Top 5")
lines.append("")
for i, item in enumerate(top_items[cat], 1):
lines.append(format_item(item, i, play_base_url))
lines.append("")
# Watchlist section
if watchlist_updates:
lines.append("## 📌 追剧更新")
lines.append("")
for item in watchlist_updates:
lines.append(format_item(item, "📌", play_base_url))
lines.append("")
return "\n".join(lines)
highlight_generator.py)Build LLM Prompt:
def build_prompt(items):
"""Build batch prompt for LLM highlight generation"""
item_list = []
for item in items:
item_list.append({
"name": item.get("vod_name", ""),
"synopsis": item.get("vod_content", "")[:200] # Truncate long synopses
})
prompt = f"""请为以下影视作品生成简短亮点（15字以内），以JSON数组返回。
作品列表：
{json.dumps(item_list, ensure_ascii=False, indent=2)}
返回格式示例：
[
{{"name": "作品名", "highlight": "亮点描述"}},
...
]
"""
return prompt
Call LLM:
import openai
import os
def generate_highlights(items):
"""Generate highlights via LLM, fallback to synopsis truncation"""
api_key = os.getenv("LLM_API_KEY")
if not api_key:
return fallback_highlights(items)
try:
client = openai.OpenAI(
api_key=api_key,
base_url=os.getenv("LLM_GATEWAY")
)
prompt = build_prompt(items)
response = client.chat.completions.create(
model=os.getenv("LLM_MODEL", "gpt-4o-mini"),
messages=[{"role": "user", "content": prompt}],
temperature=0.7
)
result = response.choices[0].message.content
highlights = json.loads(result)
# Map highlights back to items
highlight_map = {h["name"]: h["highlight"] for h in highlights}
for item in items:
item["亮点"] = highlight_map.get(item.get("vod_name", ""), "")
return items
except Exception as e:
print(f"LLM failed: {e}, using fallback")
return fallback_highlights(items)
Fallback Highlights:
import re
def fallback_highlights(items):
"""Fallback: truncate synopsis to 30 chars"""
for item in items:
synopsis = item.get("vod_content", "")
# Remove punctuation, take first 30 chars
clean = re.sub(r'[，。！？、；：""''（）《》【】]', '', synopsis)
highlight = clean[:30] if clean else "精彩内容，不容错过"
item["亮点"] = highlight
return items
Create config/watchlist.json:
{
"watchlist": [
{"name": "大唐迷雾", "type": "剧集"},
{"name": "认识的哥哥", "type": "综艺"},
{"name": "梦魇绝镇", "type": "剧集"}
]
}
Match Watchlist in Code:
import json
def match_watchlist(all_items, watchlist_file):
"""Match items against watchlist"""
try:
with open(watchlist_file, 'r', encoding='utf-8') as f:
config = json.load(f)
watchlist = config.get("watchlist", [])
except:
return []
matches = []
for watch in watchlist:
for item in all_items:
if item.get("vod_name") == watch["name"]:
matches.append(item)
break
return matches
from concurrent.futures import ThreadPoolExecutor, as_completed
def scrape_all_sources(sources):
"""Scrape all CMS sources concurrently"""
all_items = []
with ThreadPoolExecutor(max_workers=10) as executor:
futures = {
executor.submit(fetch_cms_data, src["api"]): src["name"]
for src in sources
}
for future in as_completed(futures):
source_name = futures[future]
try:
data = future.result()
items = data.get("list", [])
all_items.extend(items)
print(f"✓ {source_name}: {len(items)} items")
except Exception as e:
print(f"✗ {source_name}: {e}")
return all_items
def filter_low_quality(items):
"""Remove items with no name, score, or hits"""
return [
item for item in items
if item.get("vod_name")
and (item.get("vod_douban_score", 0) > 0 or item.get("vod_hits", 0) > 0)
]
import re
def extract_episode(item):
"""Extract episode info from vod_remarks"""
remarks = item.get("vod_remarks", "")
# Match patterns like "更新至12集", "第10集", "EP08"
patterns = [
r'更新至(\d+)集',
r'第(\d+)集',
r'EP(\d+)',
r'(\d+)集全'
]
for pattern in patterns:
match = re.search(pattern, remarks)
if match:
return f"第{match.group(1)}集"
return remarks if remarks else ""
import os
from datetime import datetime, timedelta
from pathlib import Path
def cleanup_old_reports(output_dir, days=7):
"""Delete reports older than N days"""
cutoff = datetime.now() - timedelta(days=days)
for file in Path(output_dir).glob("moontv_*.md"):
if file.stat().st_mtime < cutoff.timestamp():
file.unlink()
print(f"Deleted old report: {file.name}")
Symptom: requests.exceptions.RequestException when calling gateway
Solution: Check MOONTV_GATEWAY in .env, verify network access:
curl https://moontv-api.12879737.xyz/api/resource/sources
Symptom: "Gateway returned no sources"
Solution: Gateway may be rate-limited or down. Wait and retry, or check gateway status.
Symptom: All highlights are truncated synopses
Solution:
LLM_API_KEY is set correctlyLLM_GATEWAY is accessibleSymptom: ModuleNotFoundError: No module named 'openai'
Solution: Install dependencies:
pip install requests openai python-dotenv
Symptom: Watchlist section empty despite configured items
Solution:
WATCHLIST_FILE path is correctname in watchlist.json matches exact vod_name from CMSSymptom: Some Top 5 sections missing
Solution:
classify() logic matches your CMS type_name valuesRun the test suite:
cd scripts
python -m pytest test_moontv.py -v
Key test coverage:
max_workers in ThreadPoolExecutor based on your networkModify weights in calculate_score():
if douban > 0:
# Prioritize Douban score more
score = douban * 0.7 + normalized_hits * 0.2 + time_bonus * 0.1
else:
# Trust popularity more
score = normalized_hits * 0.7 + time_bonus * 0.2 + baseline * 0.1
Extend classify():
categories = {
"电影": [], "剧集": [], "综艺": [], "短剧": [], "福利": [],
"动漫": [] # New category
}
# Add classification logic
elif "动漫" in type_name or "动画" in type_name:
categories["动漫"].append(item)
Modify render_report() in report_template.py:
# Add emoji icons per category
icons = {
"电影": "🎬",
"剧集": "📺",
"综艺": "🎤",
"短剧": "📱",
"福利": "🎁"
}
lines.append(f"## {icons.get(cat, '📌')} {cat} Top 5")
This skill provides everything an AI agent needs to help developers deploy, configure, and customize MoonTV OpenClaw for automated movie/TV content aggregation and recommendation.