> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mavera.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Trending Topic → Content Sprint

> Pull X trending topics, filter for brand relevance via Mave, and generate multi-format content within the trend window

## Scenario

When a topic trends, there's a 4-8 hour window for disproportionate reach. This job pulls trending topics, filters for brand relevance via Mave, then generates content.

## Architecture

```mermaid theme={"dark"}
flowchart LR
    A["X GET /trends/by/woeid/{id}"] --> B["Mavera POST /mave/chat (filter)"]
    B --> C["POST /generations"]
    C --> D["Trend content briefs"]
```

## Code

<CodeGroup>
  ```python Python theme={"dark"}
  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])
  ```

  ```javascript JavaScript theme={"dark"}
  // --- 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));
  ```
</CodeGroup>

## Example Output

```text theme={"dark"}
### #MarTechCollapse (9/10, ~6 hours to peak)

Tweet Thread:
1/ MarTech lost 3 players in 2 weeks. The real story isn't who died —
   it's what killed them. 🧵
2/ Avg team uses 12 tools (was 8). More ≠ better. More silos, headaches.
3/ Pattern: they solved a feature, not a workflow. Point solutions die.
4/ Audit: "If it disappeared, would we notice in a week?" If no → shelf-ware.
5/ Winners own the analytics layer — can't fake attribution with Zapier.
6/ We built [Product] for this moment. → [link]

LinkedIn: Three MarTech companies shut down in two weeks. The underlying
trend matters more than the headlines...
```

## Error Handling

<AccordionGroup>
  <Accordion title="Trends access">The trends endpoint may require elevated access. If 403, fall back to recent search volume as a proxy.</Accordion>
  <Accordion title="Trend lifespan">X trends peak in 4-8 hours. The sprint must execute in under 30 minutes. Pre-warm Mavera by caching brand context.</Accordion>
  <Accordion title="Content safety">Some trending topics carry hidden context. Add human review for topics scoring 7-8 before publishing.</Accordion>
</AccordionGroup>

***

<CardGroup cols={2}>
  <Card title="X / Twitter Integration" icon="arrow-left" href="/integrations/x-twitter" />

  <Card title="Mave Agent" icon="brain" href="/features/mave-agent" />
</CardGroup>