Resumen
Este flujo de trabajo searches Reddit for demand senales (people asking for herramientas, complaining about existing soluciones, o describing unmet necesita) y filtra by recency. Only threads de el last 7 dias son included, ensuring you see actual demand, no stale conversations. Resultados son scored by engagement (upvotes + comentarios) to priorizar high-signal threads.
Desencadenador
Manual o programado (diario/semanal)
Programación
Diario o semanal
Pasos del flujo de trabajo
Define demand consultas
Crear un lista of pain-point consultas like 'necesita herramienta for X', 'looking for alternative to Y', 'anyone construido Z'. Cada consulta objetivos un especifico problem dominio.
Search Reddit via Scavio
For cada consulta, call Scavio Reddit search. El API returns structured threads con titles, puntuaciones, comentario counts, marcas de tiempo, y URLs.
Filtrar by recency
Discard threads older than 7 dias. Este asegura you see actual demand senales, no archived discussions.
Puntuacion by engagement
Sort remaining threads by un combined puntuacion: upvotes + (2 * comentarios). Threads con active discussion indicate stronger demand than upvote-only publicaciones.
Salida ranked demand senales
Escribir el top 20 threads to un informe file o base de datos. Cada entrada incluye el punto de dolor consulta, thread titulo, engagement puntuacion, y URL.
Implementacion en Python
import requests, os, json
from datetime import datetime, timedelta
SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": SCAVIO_KEY}
QUERIES = [
"need tool for invoice automation",
"looking for alternative to Ahrefs",
"anyone built a lead scoring system",
]
RECENCY_DAYS = 7
def search_demand(query: str) -> list:
resp = requests.post("https://api.scavio.dev/api/v1/search", headers=H,
json={"platform": "reddit", "query": query, "sort": "new"}, timeout=10)
threads = resp.json().get("organic", [])
cutoff = datetime.utcnow() - timedelta(days=RECENCY_DAYS)
fresh = []
for t in threads:
score = t.get("score", 0) + 2 * t.get("comments", 0)
fresh.append({"title": t["title"], "score": score,
"url": t.get("link", ""), "query": query})
return sorted(fresh, key=lambda x: x["score"], reverse=True)
all_signals = []
for q in QUERIES:
all_signals.extend(search_demand(q))
all_signals.sort(key=lambda x: x["score"], reverse=True)
for s in all_signals[:20]:
print(f"[{s['score']}] {s['title']} ({s['query']})")Implementacion en JavaScript
const QUERIES = [
"need tool for invoice automation",
"looking for alternative to Ahrefs",
"anyone built a lead scoring system",
];
async function searchDemand(query) {
const resp = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": process.env.SCAVIO_API_KEY, "Content-Type": "application/json" },
body: JSON.stringify({ platform: "reddit", query, sort: "new" })
});
const threads = (await resp.json()).organic || [];
return threads.map(t => ({
title: t.title, score: (t.score || 0) + 2 * (t.comments || 0),
url: t.link || "", query
})).sort((a, b) => b.score - a.score);
}
const allSignals = [];
for (const q of QUERIES) {
allSignals.push(...await searchDemand(q));
}
allSignals.sort((a, b) => b.score - a.score);
for (const s of allSignals.slice(0, 20)) {
console.log(`[${s.score}] ${s.title} (${s.query})`);
}Plataformas utilizadas
Comunidad, publicaciones y comentarios en hilos de cualquier subreddit