Trending Topic → Content Sprint

import os, requests, time

X = os.environ["X_BEARER_TOKEN"]; MV = os.environ["MAVERA_API_KEY"]
X_BASE = "https://api.x.com/2"; MV_BASE = "https://app.mavera.io/api/v1"
X_H = {"Authorization": f"Bearer {X}"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

WOEID = 23424977  # United States
BRAND = "B2B marketing analytics. Audience: marketing leaders. Tone: insightful, data-driven, slightly witty. Own: attribution, analytics, MarTech. Avoid: politics, crypto."

# 1. Fetch trends
r = requests.get(f"{X_BASE}/trends/by/woeid/{WOEID}", headers=X_H)
if r.status_code == 429:
    time.sleep(int(r.headers.get("x-rate-limit-reset", time.time()+60)) - int(time.time()))
    r = requests.get(f"{X_BASE}/trends/by/woeid/{WOEID}", headers=X_H)
if r.status_code == 403:
    print("Trends requires elevated access. Using fallback.")
    trends = [{"name": "marketing analytics", "tweet_volume": 0}]
else:
    r.raise_for_status()
    td = r.json()
    trends = [{"name": t.get("name",""), "tweet_volume": t.get("tweet_volume",0)}
              for t in td.get("data", td.get("trends",[]))[:30]]

# 2. Filter via Mave
trend_list = "\n".join(f"- {t['name']} (vol: {t['tweet_volume'] or 'N/A'})" for t in trends[:25])
relevance = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Content strategist: review X trends for brand relevance.\n\nTRENDS:\n{trend_list}\n\nBRAND: {BRAND}\n\nScore each 1-10. Return only 7+ with angle, format, urgency."
}).json()

# 3. Content sprint
sprint = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
    "prompt": f"Content sprint from trend analysis.\n\n{relevance.get('content','')[:2000]}\n\n"
        "For each relevant trend:\n1. Tweet thread (5-7 tweets, <280 chars, hook first)\n"
        "2. LinkedIn post (150-200 words)\n3. Blog outline (title + 5 sections)\n\nTie to marketing analytics."
}).json()
print(sprint.get("output", sprint.get("content",""))[:2000])

// --- Same X_BASE, MV_BASE, X_H, MV_H setup as Job 1 ---
const WOEID = 23424977;
const BRAND = "B2B marketing analytics. Insightful, data-driven. Own: attribution, MarTech. Avoid: politics.";

let trends, r = await fetch(`${X_BASE}/trends/by/woeid/${WOEID}`, { headers: X_H });
if (r.status === 429) { await new Promise(res => setTimeout(res, 60000));
  r = await fetch(`${X_BASE}/trends/by/woeid/${WOEID}`, { headers: X_H }); }
if (r.status === 403) { trends = [{ name: "marketing analytics", tweet_volume: 0 }]; }
else { const td = await r.json();
  trends = (td.data||td.trends||[]).slice(0,30).map(t => ({ name: t.name, tweet_volume: t.tweet_volume||0 })); }

const trendList = trends.slice(0,25).map(t => `- ${t.name} (vol: ${t.tweet_volume||"N/A"})`).join("\n");
const relevance = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ message: `Review X trends.\n\nTRENDS:\n${trendList}\n\n${BRAND}\n\nReturn 7+ with angle, format, urgency.` }),
}).then(r => r.json());

const sprint = await fetch(`${MV_BASE}/generations`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ prompt: `Sprint.\n\n${(relevance.content||"").slice(0,2000)}\n\nPer trend: tweet thread (5-7), LinkedIn (150-200w), blog outline.` }),
}).then(r => r.json());
console.log((sprint.output || sprint.content || "").slice(0, 2000));