Keyword Ideas → Generate at Scale

import os, requests, time
from collections import defaultdict
from openai import OpenAI

AH = os.environ["AHREFS_API_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
MB = "https://app.mavera.io/api/v1"
AH_H = {"Authorization": f"Bearer {AH}", "Accept": "application/json"}
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
SEEDS, COUNTRY = ["content marketing", "audience research"], "us"
mavera = OpenAI(base_url=MB, api_key=MV)

resp = requests.get("https://api.ahrefs.com/v3/keywords-explorer/keyword-ideas", headers=AH_H,
    params={"keywords": ",".join(SEEDS), "country": COUNTRY, "limit": 200,
            "order_by": "volume:desc",
            "select": "keyword,volume,keyword_difficulty,cpc,parent_topic"})
resp.raise_for_status()
ideas = resp.json().get("keywords", [])

clusters = defaultdict(list)
for kw in ideas:
    clusters[kw.get("parent_topic", kw["keyword"].split()[0])].append(kw)
top_clusters = sorted(clusters.items(), key=lambda c: sum(k["volume"] for k in c[1]),
                       reverse=True)[:8]
print(f"Keywords: {len(ideas)} | Clusters: {len(clusters)} | Top {len(top_clusters)}\n")

bv = requests.post(f"{MB}/brand-voices", headers=MH, json={
    "name": "SEO Content Voice",
    "description": "Clear, authoritative, data-backed. For practitioners, not beginners.",
    "samples": ["73% of teams skip validation. Here's the framework that fixes that.",
                "1,200 top pages analyzed. Pattern: data-first, opinion-second."],
}).json()

for topic, kws in top_clusters:
    kws_sorted = sorted(kws, key=lambda k: k["volume"], reverse=True)
    total_vol = sum(k["volume"] for k in kws)
    avg_kd = sum(k.get("keyword_difficulty", 0) for k in kws) // max(len(kws), 1)
    kw_list = "\n".join(f"- {k['keyword']} (vol: {k['volume']}, KD: {k.get('keyword_difficulty', 0)}, "
                        f"CPC: ${k.get('cpc', 0):.2f})" for k in kws_sorted[:12])
    gen = requests.post(f"{MB}/generations", headers=MH, json={
        "prompt": f"SEO plan for '{topic}'\nVol: {total_vol} | KD: {avg_kd} | KWs: {len(kws)}\n\n"
                  f"KEYWORDS:\n{kw_list}\n\nGenerate: 1) Title (60ch) 2) Meta (155ch) "
                  f"3) Primary + 4 secondary KWs 4) H2/H3 outline 5) Word count "
                  f"6) SERP features 7) Internal links",
        "brand_voice_id": bv["id"],
    }).json()
    print(f"=== {topic} ({len(kws)} kws, {total_vol:,} vol, KD {avg_kd}) ===")
    print(gen.get("output", gen.get("content", ""))[:600], "\n")
    time.sleep(1)

chat = mavera.responses.create(model="mave", input=[{"role": "user", "content":
    f"Summarize: {len(top_clusters)} clusters, "
    f"{sum(len(kws) for _, kws in top_clusters)} keywords, "
    f"{sum(sum(k['volume'] for k in kws) for _, kws in top_clusters):,} volume. "
    f"2-week execution schedule by volume and difficulty."}])
print("=== Execution Schedule ===\n" + chat.output[0].content[0].text)

const AH = process.env.AHREFS_API_TOKEN, MV = process.env.MAVERA_API_KEY;
const MB = "https://app.mavera.io/api/v1";
const AH_H = { Authorization: `Bearer ${AH}`, Accept: "application/json" };
const MH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const SEEDS = ["content marketing", "audience research"];

const params = new URLSearchParams({ keywords: SEEDS.join(","), country: "us",
  limit: "200", order_by: "volume:desc",
  select: "keyword,volume,keyword_difficulty,cpc,parent_topic" });
const ideas = (await fetch(
  `https://api.ahrefs.com/v3/keywords-explorer/keyword-ideas?${params}`, { headers: AH_H }
).then((r) => r.json())).keywords || [];

const clusters = {};
for (const kw of ideas) (clusters[kw.parent_topic || kw.keyword.split(" ")[0]] ??= []).push(kw);
const topClusters = Object.entries(clusters)
  .map(([t, kws]) => ({ t, kws, vol: kws.reduce((s, k) => s + k.volume, 0) }))
  .sort((a, b) => b.vol - a.vol).slice(0, 8);
console.log(`Keywords: ${ideas.length} | Clusters: ${Object.keys(clusters).length}\n`);

const bv = await fetch(`${MB}/brand-voices`, { method: "POST", headers: MH,
  body: JSON.stringify({ name: "SEO Content Voice",
    description: "Clear, authoritative, data-backed. For practitioners.",
    samples: ["73% skip validation. Here's the fix.",
      "1,200 top pages analyzed. Pattern: data-first, opinion-second."] }),
}).then((r) => r.json());

for (const { t, kws, vol } of topClusters) {
  const sorted = kws.sort((a, b) => b.volume - a.volume);
  const avgKd = Math.round(kws.reduce((s, k) => s + (k.keyword_difficulty || 0), 0) / kws.length);
  const kwList = sorted.slice(0, 12).map((k) =>
    `- ${k.keyword} (vol: ${k.volume}, KD: ${k.keyword_difficulty || 0})`).join("\n");
  const gen = await fetch(`${MB}/generations`, { method: "POST", headers: MH,
    body: JSON.stringify({
      prompt: `SEO plan for '${t}'\nVol: ${vol} | KD: ${avgKd}\nKEYWORDS:\n${kwList}\n\n`
        + `1) Title 2) Meta 3) KWs 4) Outline 5) Word count 6) SERP features 7) Links`,
      brand_voice_id: bv.id }),
  }).then((r) => r.json());
  console.log(`=== ${t} (${kws.length} kws, ${vol.toLocaleString()} vol) ===`);
  console.log((gen.output || gen.content || "").slice(0, 600), "\n");
  await new Promise((r) => setTimeout(r, 1000));
}

const OpenAI = (await import("openai")).default;
const mavera = new OpenAI({ baseURL: MB, apiKey: MV });
const chat = await mavera.responses.create({ model: "mave",
  input: [{ role: "user", content: `Summarize: ${topClusters.length} clusters, `
    + `${topClusters.reduce((s, c) => s + c.kws.length, 0)} keywords. `
    + `2-week execution schedule by volume and difficulty.` }] });
console.log("=== Schedule ===\n" + chat.output[0].content[0].text);