Draft a researched LinkedIn post each morning from your topic list

from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from clickruntime import on_trigger, credentials, http, ai

SHEET_ID = "your-spreadsheet-id"
VOICE = "Plain, practical, first person. Short paragraphs. No hashtags or emojis."

def sonar(prompt):
    r = http.post("https://api.perplexity.ai/chat/completions", headers={"Authorization": f"Bearer {credentials.secret('PERPLEXITY_API_KEY')}"},
                  json={"model": "sonar", "messages": [{"role": "user", "content": prompt}]}, timeout=90).json()
    return r["choices"][0]["message"]["content"], [s["url"] for s in r.get("search_results", [])]

@on_trigger
def run(event):
    sheets = build("sheets", "v4", credentials=Credentials(credentials.connection("google", "work")),
                   cache_discovery=False).spreadsheets().values()
    rows = sheets.get(spreadsheetId=SHEET_ID, range="LinkedIn!A2:C").execute().get("values", [])
    nxt = next(((i + 2, r[0]) for i, r in enumerate(rows) if len(r) == 1 and r[0]), None)
    if not nxt:
        return {"drafted": False}
    n, topic = nxt
    facts, links = sonar(f"What is new and notable about {topic} in the last month? Key facts and examples.")
    post = ai.generate(f"Write a LinkedIn post (150-220 words) with a clear opinion about {topic}. {VOICE}\n"
                       f"Research:\n{facts[:3000]}", max_output_tokens=500).text
    sheets.update(spreadsheetId=SHEET_ID, range=f"LinkedIn!B{n}:C{n}", valueInputOption="RAW",
                  body={"values": [[post.strip(), "Drafted"]]}).execute()
    return {"drafted": topic, "sources": len(links)}