Pipeline de ideas de contenido multiplataforma en n8n

import requests

API_KEY = "your_scavio_api_key"
BASE = "https://api.scavio.dev/api/v1"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

# Pick the topics and hashtag you want fresh ideas around.
SUBREDDIT_QUERY = "ai agents"
HASHTAG = "aiagents"
YOUTUBE_QUERY = "ai agents tutorial"


def reddit_topics(query, count=15):
    res = requests.post(f"{BASE}/reddit/search",
        headers=HEADERS, json={"query": query, "count": count}, timeout=30)
    res.raise_for_status()
    posts = res.json().get("data", {}).get("posts", [])
    return [{"title": p.get("title"), "score": p.get("score"),
             "comments": p.get("num_comments"), "url": p.get("url")} for p in posts]


def tiktok_trending(hashtag, count=15):
    # Step 1: resolve the hashtag name to a challenge_id.
    meta = requests.post(f"{BASE}/tiktok/hashtag",
        headers=HEADERS, json={"hashtag": hashtag}, timeout=30)
    meta.raise_for_status()
    challenge = meta.json().get("data", {})
    # Step 2: pull the trending videos for that hashtag.
    vids = requests.post(f"{BASE}/tiktok/hashtag/videos",
        headers=HEADERS, json={"hashtag": hashtag, "count": count}, timeout=30)
    vids.raise_for_status()
    videos = vids.json().get("data", {}).get("videos", [])
    return {"challenge_id": challenge.get("challenge_id"),
            "videos": [{"desc": v.get("desc"), "plays": v.get("play_count")}
                       for v in videos]}


def youtube_results(query, count=15):
    # Wire body uses "search", not "query".
    res = requests.post(f"{BASE}/youtube/search",
        headers=HEADERS, json={"search": query, "count": count}, timeout=30)
    res.raise_for_status()
    items = res.json().get("data", {}).get("videos", [])
    return [{"title": v.get("title"), "channel": v.get("channel"),
             "views": v.get("view_count")} for v in items]


def collect():
    return {
        "reddit": reddit_topics(SUBREDDIT_QUERY),
        "tiktok": tiktok_trending(HASHTAG),
        "youtube": youtube_results(YOUTUBE_QUERY),
    }


if __name__ == "__main__":
    payload = collect()
    # Hand `payload` to your LLM node (n8n OpenAI/Anthropic node) for idea synthesis.
    print(json.dumps(payload, indent=2))

const API_KEY = "your_scavio_api_key";
const BASE = "https://api.scavio.dev/api/v1";
const HEADERS = {
  Authorization: `Bearer ${API_KEY}`,
  "Content-Type": "application/json",
};

const SUBREDDIT_QUERY = "ai agents";
const HASHTAG = "aiagents";
const YOUTUBE_QUERY = "ai agents tutorial";

async function post(path, body) {
  const res = await fetch(`${BASE}${path}`, {
    method: "POST",
    headers: HEADERS,
    body: JSON.stringify(body),
  });
  if (!res.ok) throw new Error(`scavio ${path} ${res.status}`);
  return res.json();
}

async function redditTopics(query, count = 15) {
  const json = await post("/reddit/search", { query, count });
  const posts = json.data?.posts ?? [];
  return posts.map((p) => ({
    title: p.title,
    score: p.score,
    comments: p.num_comments,
    url: p.url,
  }));
}

async function tiktokTrending(hashtag, count = 15) {
  // Step 1: resolve hashtag name to a challenge_id.
  const meta = await post("/tiktok/hashtag", { hashtag });
  const challenge = meta.data ?? {};
  // Step 2: pull trending videos for that hashtag.
  const vids = await post("/tiktok/hashtag/videos", { hashtag, count });
  const videos = vids.data?.videos ?? [];
  return {
    challenge_id: challenge.challenge_id,
    videos: videos.map((v) => ({ desc: v.desc, plays: v.play_count })),
  };
}

async function youtubeResults(query, count = 15) {
  // Wire body uses "search", not "query".
  const json = await post("/youtube/search", { search: query, count });
  const items = json.data?.videos ?? [];
  return items.map((v) => ({
    title: v.title,
    channel: v.channel,
    views: v.view_count,
  }));
}

async function collect() {
  const [reddit, tiktok, youtube] = await Promise.all([
    redditTopics(SUBREDDIT_QUERY),
    tiktokTrending(HASHTAG),
    youtubeResults(YOUTUBE_QUERY),
  ]);
  return { reddit, tiktok, youtube };
}

collect()
  // Hand `payload` to your LLM node (n8n OpenAI/Anthropic node) for synthesis.
  .then((payload) => console.log(JSON.stringify(payload, null, 2)))
  .catch((err) => console.error(err));