Reddit Market Weekly Scanner

import requests
import json
from pathlib import Path
from datetime import datetime
from collections import Counter

API_KEY = "your_scavio_api_key"

SUBREDDITS_AND_QUERIES = [
    {"subreddit": "SaaS", "queries": ["frustrated with", "looking for alternative"]},
    {"subreddit": "startups", "queries": ["tool recommendation", "what do you use for"]},
    {"subreddit": "webdev", "queries": ["best API for", "switching from"]},
    {"subreddit": "SEO", "queries": ["rank tracking tool", "SERP API recommendation"]},
    {"subreddit": "artificial", "queries": ["AI agent tools", "search grounding"]},
]

PAIN_WORDS = ["frustrated", "hate", "terrible", "broken", "expensive", "slow", "unreliable"]
TOOL_WORDS = ["recommend", "alternative", "switched to", "using", "tried"]

def search_subreddit(subreddit: str, query: str) -> list[dict]:
    res = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": "reddit", "query": f"{query} {subreddit}"},
        timeout=15,
    )
    res.raise_for_status()
    return res.json().get("organic", [])

def classify_post(title: str) -> str:
    lower = title.lower()
    if any(w in lower for w in PAIN_WORDS):
        return "pain_point"
    if any(w in lower for w in TOOL_WORDS):
        return "tool_mention"
    if "vs" in lower or "comparison" in lower or "versus" in lower:
        return "comparison"
    return "general"

def run():
    date = datetime.utcnow().strftime("%Y-%m-%d")
    all_posts = []
    tool_mentions = Counter()
    post_types = Counter()

    for config in SUBREDDITS_AND_QUERIES:
        for query in config["queries"]:
            posts = search_subreddit(config["subreddit"], query)
            for post in posts:
                title = post.get("title", "")
                ptype = classify_post(title)
                post_types[ptype] += 1
                all_posts.append({
                    "subreddit": config["subreddit"],
                    "title": title[:120],
                    "score": post.get("score", 0),
                    "type": ptype,
                    "link": post.get("link", ""),
                })

    report = {
        "date": date,
        "subreddits_scanned": len(SUBREDDITS_AND_QUERIES),
        "total_posts": len(all_posts),
        "post_types": dict(post_types),
        "pain_points": [p for p in all_posts if p["type"] == "pain_point"][:10],
        "tool_mentions": [p for p in all_posts if p["type"] == "tool_mention"][:10],
        "top_posts_by_score": sorted(all_posts, key=lambda x: x["score"], reverse=True)[:10],
    }

    Path(f"reddit_market_{date}.json").write_text(json.dumps(report, indent=2))
    print(f"Reddit Market Scan {date}: {len(all_posts)} posts from {len(SUBREDDITS_AND_QUERIES)} subreddits")
    for ptype, count in post_types.most_common():
        print(f"  {ptype}: {count}")

if __name__ == "__main__":
    run()

const API_KEY = "your_scavio_api_key";
const TARGETS = [
  { sub: "SaaS", queries: ["frustrated with", "looking for alternative"] },
  { sub: "startups", queries: ["tool recommendation"] },
];

async function searchReddit(query) {
  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: "reddit", query }),
  });
  if (!res.ok) throw new Error(`scavio ${res.status}`);
  return (await res.json()).organic ?? [];
}

const allPosts = [];
for (const target of TARGETS) {
  for (const q of target.queries) {
    const posts = await searchReddit(`${q} ${target.sub}`);
    allPosts.push(...posts.map((p) => ({ sub: target.sub, title: (p.title ?? "").slice(0, 100), score: p.score ?? 0 })));
  }
}
allPosts.sort((a, b) => b.score - a.score);
console.log(`Scanned ${TARGETS.length} subreddits, found ${allPosts.length} posts`);
for (const p of allPosts.slice(0, 5)) console.log(`  [${p.sub}] ${p.title} (score: ${p.score})`);