Resumen
Este flujo de trabajo feeds live search datos en contenido creation pipelines to asegura cada articulo es grounded in verified fuentes. Before contenido es drafted, el pipeline consultas Google for actual datos, Reddit for discussion context, y Amazon/Walmart for precios verification. El salida es un research brief ese contenido writers o AI generators usar to produce accurate, source-backed contenido.
Desencadenador
Activado per contenido brief, o batched diario for el contenido calendar
Programación
Activado per contenido brief o batched diario
Pasos del flujo de trabajo
Cargar contenido brief topics
Leer el lista of topics programado for contenido creation de el contenido calendar o CMS.
Search Google for fuente datos
Consulta Scavio Google search for cada topic to gather actual resultados organicos, AI Overview contenido, y fragmentos destacados.
Search Reddit for discussion context
Find relevante Reddit discussions to understand usuario puntos de dolor y questions alrededor de cada topic.
Verify precios claims
For cualquier topic involving productos o precios, consulta Amazon y Walmart to obtener actual, verified prices.
Compile research brief
Combine todos datos en un structured research brief con fuentes, discussion context, y verified claims.
Implementacion en Python
import requests
import json
from pathlib import Path
from datetime import datetime
API_KEY = "your_scavio_api_key"
def search(query: str, platform: str) -> dict:
res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": platform, "query": query},
timeout=15,
)
res.raise_for_status()
return res.json()
def build_research_brief(topic: str, needs_pricing: bool = False) -> dict:
# Google: authoritative sources
google = search(topic, "google")
sources = [{"title": r.get("title", ""), "url": r.get("link", ""), "snippet": r.get("snippet", "")} for r in google.get("organic", [])[:5]]
ai_overview = google.get("ai_overview", {})
# Reddit: real user perspectives
reddit = search(topic, "reddit")
discussions = [{"title": r.get("title", ""), "subreddit": r.get("subreddit", ""), "score": r.get("score", 0), "link": r.get("link", "")} for r in reddit.get("organic", [])[:5]]
brief = {
"topic": topic,
"researched_at": datetime.utcnow().isoformat(),
"google_sources": sources,
"ai_overview_text": (ai_overview or {}).get("text", ""),
"reddit_discussions": discussions,
"people_also_ask": [q.get("question", "") for q in google.get("people_also_ask", [])],
}
# Pricing verification if needed
if needs_pricing:
amazon = search(topic, "amazon")
prices = [{"title": r.get("title", ""), "price": r.get("price"), "link": r.get("link", "")} for r in amazon.get("organic", [])[:5] if r.get("price")]
brief["verified_prices"] = prices
return brief
def run(topics: list[dict]):
date = datetime.utcnow().strftime("%Y-%m-%d")
briefs = []
for topic_config in topics:
brief = build_research_brief(topic_config["topic"], topic_config.get("needs_pricing", False))
briefs.append(brief)
Path(f"content_research_{date}.json").write_text(json.dumps(briefs, indent=2))
print(f"Built {len(briefs)} research briefs")
for b in briefs:
print(f" {b['topic']}: {len(b['google_sources'])} sources, {len(b['reddit_discussions'])} discussions")
topics = [
{"topic": "best SERP API for AI agents 2026", "needs_pricing": False},
{"topic": "wireless noise cancelling headphones", "needs_pricing": True},
]
run(topics)Implementacion en JavaScript
const API_KEY = "your_scavio_api_key";
async function search(query, platform) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform, query }),
});
if (!res.ok) throw new Error(`scavio ${res.status}`);
return res.json();
}
async function buildBrief(topic, needsPricing = false) {
const google = await search(topic, "google");
const reddit = await search(topic, "reddit");
const brief = {
topic,
sources: (google.organic ?? []).slice(0, 5).map((r) => ({ title: r.title ?? "", url: r.link ?? "", snippet: r.snippet ?? "" })),
discussions: (reddit.organic ?? []).slice(0, 5).map((r) => ({ title: r.title ?? "", subreddit: r.subreddit ?? "", score: r.score ?? 0 })),
paa: (google.people_also_ask ?? []).map((q) => q.question ?? ""),
};
if (needsPricing) {
const amazon = await search(topic, "amazon");
brief.prices = (amazon.organic ?? []).filter((r) => r.price).slice(0, 5).map((r) => ({ title: r.title ?? "", price: r.price, link: r.link ?? "" }));
}
return brief;
}
const topics = [{ topic: "best SERP API 2026" }, { topic: "wireless headphones", needsPricing: true }];
for (const t of topics) {
const brief = await buildBrief(t.topic, t.needsPricing);
console.log(`${brief.topic}: ${brief.sources.length} sources, ${brief.discussions.length} discussions`);
}Plataformas utilizadas
Búsqueda web con grafo de conocimiento, PAA y resúmenes de IA
Amazon
Búsqueda de productos con precios, calificaciones y reseñas
Comunidad, publicaciones y comentarios en hilos de cualquier subreddit