> ## 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.

# Trend-to-Content Pipeline

> Monitor rising subreddit posts, filter for brand alignment via Mave, and generate trend-aligned content briefs

## Scenario

Subreddits surface emerging topics hours before mainstream channels. This job monitors `/rising`, sends each trend to Mave for brand-alignment, then feeds approved trends into Generate.

## Architecture

```mermaid theme={"dark"}
flowchart LR
    A["Reddit GET /r/{subreddit}/rising"] --> B["Mavera POST /mave/chat (alignment)"]
    B --> C["POST /generations"]
    C --> D["Trend-aligned content"]
```

## Code

<CodeGroup>
  ```python Python theme={"dark"}
  import os, requests, time

  # --- Auth setup same as Job 1 ---

  SUB = "marketing"
  BRAND = "B2B marketing analytics platform. Audience: Marketing directors/VPs. Tone: Data-driven, practical, contrarian."

  # 1. Rising posts
  rising = [p["data"] for p in requests.get(f"{RD}/r/{SUB}/rising",
      headers=RD_H, params={"limit": 20, "raw_json": 1}).json()["data"]["children"]]

  # 2. Brand alignment via Mave
  aligned = []
  for post in rising:
      check = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
          "message": f"Evaluate trend for brand alignment (score 1-10).\n\nTREND: {post['title']}\nCONTEXT: {post.get('selftext','')[:300]}\nSUBREDDIT: /r/{SUB} | Score: {post.get('score',0)}\n\nBRAND: {BRAND}\n\nIf 7+: suggest angle + format. Estimate trend velocity."
      }).json()
      content = check.get("content", "")
      lines = [l for l in content.split("\n") if "/10" in l or "score" in l.lower()]
      try: score = int("".join(c for c in (lines[0] if lines else "0") if c.isdigit())[:2])
      except: score = 0
      if score >= 7:
          aligned.append({"title": post["title"], "score": post.get("score",0), "alignment": score, "angle": content[:300]})
      time.sleep(0.5)

  print(f"Evaluated: {len(rising)} | Aligned: {len(aligned)}")

  # 3. Generate content
  for trend in aligned[:5]:
      gen = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
          "prompt": f"Write a LinkedIn article (600-800 words) based on:\n\nTREND: {trend['title']}\nANGLE: {trend['angle'][:200]}\n\nHook: Open with Reddit insight. Body: Data + frameworks. CTA: Free trial. Tone: data-driven, contrarian.",
      }).json()
      print(f"\n{'='*50}\nTREND: {trend['title']} | Alignment: {trend['alignment']}/10")
      print(gen.get("output", gen.get("content", ""))[:500])
  ```

  ```javascript JavaScript theme={"dark"}
  // --- Auth setup same as Job 1 ---

  const SUB = "marketing";
  const BRAND = "B2B marketing analytics. Audience: Marketing directors/VPs. Tone: Data-driven, contrarian.";

  // 1. Rising posts
  const rising = (await (await fetch(`${RD}/r/${SUB}/rising?limit=20&raw_json=1`, { headers: RD_H })).json())
    .data.children.map(p => p.data);

  // 2. Brand alignment
  const aligned = [];
  for (const post of rising) {
    const check = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
      body: JSON.stringify({ message: `Evaluate trend for brand alignment (1-10).\n\nTREND: ${post.title}\nCONTEXT: ${(post.selftext||"").slice(0,300)}\n\n${BRAND}\n\nIf 7+: suggest angle. Estimate velocity.` }),
    }).then(r => r.json());
    const line = (check.content || "").split("\n").find(l => l.includes("/10")) || "";
    const score = parseInt((line.match(/\d+/) || ["0"])[0], 10);
    if (score >= 7) aligned.push({ title: post.title, score: post.score||0, alignment: score, angle: (check.content||"").slice(0,300) });
    await new Promise(r => setTimeout(r, 500));
  }

  console.log(`Evaluated: ${rising.length} | Aligned: ${aligned.length}`);

  // 3. Generate
  for (const trend of aligned.slice(0, 5)) {
    const gen = await fetch(`${MV_BASE}/generations`, { method: "POST", headers: MV_H,
      body: JSON.stringify({ prompt: `LinkedIn article (600-800 words).\n\nTREND: ${trend.title}\nANGLE: ${trend.angle.slice(0,200)}\n\nHook with Reddit insight. Data + frameworks. CTA: free trial.` }),
    }).then(r => r.json());
    console.log(`\n${"=".repeat(50)}\nTREND: ${trend.title} | Alignment: ${trend.alignment}/10`);
    console.log((gen.output || gen.content || "").slice(0, 500));
  }
  ```
</CodeGroup>

## Example Output

```text theme={"dark"}
Evaluated: 20 | Aligned: 6

TREND: "Our attribution model was completely wrong" | Alignment: 10/10

# Our Attribution Model Was Lying to Us (and Yours Probably Is Too)

A thread in /r/marketing surfaced something uncomfortable: a director
rebuilt attribution and found 40% of "high-performing" channels were
getting credit for conversions they didn't influence...
```

## Error Handling

<AccordionGroup>
  <Accordion title="Rising vs hot">`/rising` surfaces fast-growing posts. Fall back to `/hot?limit=10` for smaller subreddits.</Accordion>
  <Accordion title="Content length">If truncated, increase `max_tokens` or split into sections.</Accordion>
</AccordionGroup>

***

<CardGroup cols={2}>
  <Card title="Reddit Integration" icon="arrow-left" href="/integrations/reddit" />

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