Post Engagement → Content Strategy

import os, requests, time

LI = os.environ["LINKEDIN_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
LI_BASE = "https://api.linkedin.com/rest"
MV_BASE = "https://app.mavera.io/api/v1"
LI_H = {"Authorization": f"Bearer {LI}", "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

ORG_URN = "urn:li:organization:12345678"

# 1. Pull recent company posts
r = requests.get(f"{LI_BASE}/posts",
    headers=LI_H,
    params={
        "q": "author",
        "author": ORG_URN,
        "count": 50,
        "sortBy": "LAST_MODIFIED",
    })
if r.status_code == 429:
    time.sleep(int(r.headers.get("Retry-After", 60)))
    r = requests.get(f"{LI_BASE}/posts", headers=LI_H,
        params={"q": "author", "author": ORG_URN, "count": 50, "sortBy": "LAST_MODIFIED"})
r.raise_for_status()
posts = r.json().get("elements", [])

# 2. Fetch engagement for each post
enriched = []
for post in posts:
    post_urn = post.get("id", "")
    commentary = post.get("commentary", "")
    if not commentary:
        continue

    sr = requests.get(f"{LI_BASE}/socialActions/{post_urn}",
        headers=LI_H)
    if sr.status_code == 429:
        time.sleep(int(sr.headers.get("Retry-After", 30)))
        sr = requests.get(f"{LI_BASE}/socialActions/{post_urn}", headers=LI_H)

    if sr.ok:
        actions = sr.json()
        likes = actions.get("likesSummary", {}).get("totalLikes", 0)
        comments = actions.get("commentsSummary", {}).get("totalFirstLevelComments", 0)
        shares = actions.get("sharesSummary", {}).get("totalShares", 0)
    else:
        likes = comments = shares = 0

    total_engagement = likes + comments * 3 + shares * 5
    enriched.append({
        "urn": post_urn,
        "commentary": commentary[:500],
        "content_type": post.get("content", {}).get("contentType", "NONE"),
        "likes": likes,
        "comments": comments,
        "shares": shares,
        "engagement_score": total_engagement,
    })
    time.sleep(0.4)

# 3. Rank and take top performers
ranked = sorted(enriched, key=lambda x: -x["engagement_score"])
top = ranked[:10]
bottom = ranked[-5:] if len(ranked) >= 15 else []

# 4. Build analysis prompt
top_block = "\n\n---\n\n".join(
    f"POST {i+1} (engagement: {p['engagement_score']} | {p['likes']} likes, {p['comments']} comments, {p['shares']} shares | {p['content_type']}):\n{p['commentary']}"
    for i, p in enumerate(top)
)
bottom_block = "\n\n---\n\n".join(
    f"LOW {i+1} (engagement: {p['engagement_score']}):\n{p['commentary']}"
    for i, p in enumerate(bottom)
) if bottom else "No low-performing posts to compare."

# 5. Ask Mave for theme analysis + content generation
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"""Analyze these LinkedIn Company Page posts. I'm giving you the top 10 performers and bottom 5.

TOP PERFORMERS:
{top_block}

LOW PERFORMERS:
{bottom_block}

Produce:
1. THEME ANALYSIS: What do the top posts have in common? (format, length, hooks, tone, topics)
2. ANTI-PATTERNS: What do underperformers share?
3. CONTENT FORMULA: A repeatable structure based on the winners
4. NEW CONCEPTS: Generate 10 new LinkedIn post concepts that follow the winning patterns. For each: topic, hook (first line), format (text-only/carousel/video/poll), and why it should work based on the data.
5. POSTING CADENCE: Recommended frequency and timing based on content type distribution."""
}).json()

print("=" * 60)
print("LINKEDIN CONTENT STRATEGY — DATA-DRIVEN")
print("=" * 60)
print(f"Analyzed {len(enriched)} posts | Top engagement: {top[0]['engagement_score']}")
print(f"Avg engagement (top 10): {sum(p['engagement_score'] for p in top) / len(top):.0f}")
print(f"Avg engagement (all): {sum(p['engagement_score'] for p in enriched) / len(enriched):.0f}")
print()
print(analysis.get("content", "")[:2000])

const LI = process.env.LINKEDIN_ACCESS_TOKEN;
const MV = process.env.MAVERA_API_KEY;
const LI_BASE = "https://api.linkedin.com/rest";
const MV_BASE = "https://app.mavera.io/api/v1";
const LI_H = { Authorization: `Bearer ${LI}`, "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const ORG_URN = "urn:li:organization:12345678";

// 1. Pull recent company posts
let res = await fetch(
  `${LI_BASE}/posts?q=author&author=${encodeURIComponent(ORG_URN)}&count=50&sortBy=LAST_MODIFIED`,
  { headers: LI_H }
);
if (res.status === 429) {
  await new Promise(r => setTimeout(r, parseInt(res.headers.get("Retry-After") || "60", 10) * 1000));
  res = await fetch(
    `${LI_BASE}/posts?q=author&author=${encodeURIComponent(ORG_URN)}&count=50&sortBy=LAST_MODIFIED`,
    { headers: LI_H }
  );
}
if (!res.ok) throw new Error(`LinkedIn ${res.status}: ${await res.text()}`);
const posts = (await res.json()).elements || [];

// 2. Fetch engagement per post
const enriched = [];
for (const post of posts) {
  const commentary = post.commentary || "";
  if (!commentary) continue;

  const sr = await fetch(`${LI_BASE}/socialActions/${post.id}`, { headers: LI_H });
  let likes = 0, comments = 0, shares = 0;
  if (sr.ok) {
    const actions = await sr.json();
    likes = actions.likesSummary?.totalLikes || 0;
    comments = actions.commentsSummary?.totalFirstLevelComments || 0;
    shares = actions.sharesSummary?.totalShares || 0;
  }

  enriched.push({
    urn: post.id, commentary: commentary.slice(0, 500),
    content_type: post.content?.contentType || "NONE",
    likes, comments, shares,
    engagement_score: likes + comments * 3 + shares * 5,
  });
  await new Promise(r => setTimeout(r, 400));
}

// 3. Rank
const ranked = enriched.sort((a, b) => b.engagement_score - a.engagement_score);
const top = ranked.slice(0, 10);
const bottom = ranked.length >= 15 ? ranked.slice(-5) : [];

// 4. Build prompt
const topBlock = top.map((p, i) =>
  `POST ${i + 1} (engagement: ${p.engagement_score} | ${p.likes} likes, ${p.comments} comments, ${p.shares} shares | ${p.content_type}):\n${p.commentary}`
).join("\n\n---\n\n");

const bottomBlock = bottom.length
  ? bottom.map((p, i) => `LOW ${i + 1} (engagement: ${p.engagement_score}):\n${p.commentary}`).join("\n\n---\n\n")
  : "No low-performing posts to compare.";

// 5. Mave analysis
const analysis = await fetch(`${MV_BASE}/mave/chat`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Analyze these LinkedIn Company Page posts. Top 10 and bottom 5.\n\nTOP PERFORMERS:\n${topBlock}\n\nLOW PERFORMERS:\n${bottomBlock}\n\nProduce:\n1. THEME ANALYSIS: What do top posts have in common?\n2. ANTI-PATTERNS: What do underperformers share?\n3. CONTENT FORMULA: Repeatable structure from winners\n4. NEW CONCEPTS: 10 new post concepts following winning patterns (topic, hook, format, rationale)\n5. POSTING CADENCE: Recommended frequency and timing`,
  }),
}).then(r => r.json());

console.log("=".repeat(60));
console.log("LINKEDIN CONTENT STRATEGY — DATA-DRIVEN");
console.log("=".repeat(60));
const avgTop = top.reduce((s, p) => s + p.engagement_score, 0) / (top.length || 1);
const avgAll = enriched.reduce((s, p) => s + p.engagement_score, 0) / (enriched.length || 1);
console.log(`Analyzed ${enriched.length} posts | Top: ${top[0]?.engagement_score} | Avg top 10: ${avgTop.toFixed(0)} | Avg all: ${avgAll.toFixed(0)}`);
console.log();
console.log((analysis.content || "").slice(0, 2000));