Content Research: Live Data, Not Slop

import requests, os, json

def research_topic(topic: str) -> dict:
    """Gather live data for content creation."""
    research = {
        "topic": topic,
        "serp_data": None,
        "reddit_threads": None,
        "related_questions": [],
    }

    # Step 1: Get current SERP landscape
    serp_resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": os.environ["SCAVIO_API_KEY"]},
        json={"query": topic, "platform": "google", "country_code": "us"},
        timeout=15,
    )
    serp = serp_resp.json()
    research["serp_data"] = {
        "top_results": [
            {"title": r["title"], "url": r.get("link", ""), "snippet": r.get("snippet", "")}
            for r in serp.get("organic_results", [])[:5]
        ],
        "has_ai_overview": bool(serp.get("ai_overview")),
        "paa_questions": [q.get("question", "") for q in serp.get("people_also_ask", [])],
    }
    research["related_questions"] = research["serp_data"]["paa_questions"]

    # Step 2: Get Reddit discussions for real user opinions
    reddit_resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": os.environ["SCAVIO_API_KEY"]},
        json={"query": topic, "platform": "reddit"},
        timeout=15,
    )
    reddit = reddit_resp.json()
    research["reddit_threads"] = [
        {"title": r["title"], "url": r.get("link", ""), "snippet": r.get("snippet", "")}
        for r in reddit.get("organic_results", [])[:5]
    ]

    return research

# 2 credits per topic: 1 Google + 1 Reddit
data = research_topic("best serp api for agents 2026")

def extract_verifiable_claims(research: dict) -> list:
    """Pull specific, citable facts from research data."""
    claims = []

    # From SERP snippets: extract pricing, features, dates
    for result in research["serp_data"]["top_results"]:
        snippet = result["snippet"]
        # Look for specific numbers, prices, dates
        if any(c in snippet for c in ["$", "%", "2026", "2025"]):
            claims.append({
                "claim": snippet[:200],
                "source": result["url"],
                "type": "serp_snippet",
            })

    # From Reddit: extract real user experiences
    for thread in research.get("reddit_threads", []):
        snippet = thread.get("snippet", "")
        if any(word in snippet.lower() for word in
               ["switched", "using", "tried", "compared", "costs", "pricing"]):
            claims.append({
                "claim": snippet[:200],
                "source": thread["url"],
                "type": "user_experience",
            })

    return claims

claims = extract_verifiable_claims(data)
for c in claims:
    print(f"[{c['type']}] {c['claim'][:80]}...")
    print(f"  Source: {c['source']}")

def generate_content_brief(topic: str) -> dict:
    """Create a content brief enriched with live data."""
    research = research_topic(topic)
    claims = extract_verifiable_claims(research)

    brief = {
        "topic": topic,
        "target_answer": None,  # First paragraph should answer this
        "existing_coverage": [],
        "content_gaps": [],
        "verifiable_data_points": claims,
        "questions_to_answer": research["related_questions"],
    }

    # Analyze existing coverage
    for r in research["serp_data"]["top_results"]:
        brief["existing_coverage"].append({
            "title": r["title"],
            "url": r["url"],
            "angle": r["snippet"][:100],
        })

    # Content gap: questions in PAA not covered by top results
    covered_topics = " ".join(r["snippet"] for r in research["serp_data"]["top_results"])
    for q in research["related_questions"]:
        q_words = set(q.lower().split())
        if len(q_words.intersection(covered_topics.lower().split())) < 3:
            brief["content_gaps"].append(q)

    return brief

brief = generate_content_brief("n8n seo automation workflow 2026")
print(json.dumps(brief, indent=2))

# SLOP (no live data):
# "There are many SERP APIs available in 2026. Some are expensive
#  while others are more affordable. It's important to choose the
#  right one for your needs. Consider factors like pricing, features,
#  and reliability."

# DATA-ENRICHED (with live research):
# "DataForSEO queue costs $0.0006/query with a 5-minute delay.
#  Scavio charges $0.005/credit for instant multi-platform results.
#  Serper sells credit packs starting at $1/1k. Reddit user u/dev_seo
#  reported switching from SerpAPI ($75/mo for 5k searches) to
#  DataForSEO and cutting costs by 90% for their daily rank tracker."

# The difference: specific prices, specific tools, real user testimony,
# verifiable claims with sources.