القائمة
Weekly founder content research and production engine that transforms trending news into ready-to-use content assets.
Scan vault raw/ folder for hyperlinks embedded in markdown notes; POST each unseen URL to LuminaVault server /v1/capture/safari so Hermes ingests + memorizes. Usage: /kb-vacuum [folder=raw/]
Compile all uncompiled raw/ content into the wiki. Writes source summaries, creates/updates concept articles with Obsidian backlinks, and updates the index. Run after /kb-ingest to process new content.
Generate daily Reddit and X/Twitter marketing content from the AI Cohort scoreboard, with automatic fallback to the most recent available data when today's file is missing.
Configuration, maintenance, and troubleshooting of AI Cohort scoreboard scripts including vault path setup and script updates.
Build and deliver periodic content digests (news, stock, entertainment) to multiple platforms: save to vault Raw/ and print full markdown to stdout for cron-based delivery.
Capture external content (X/Twitter articles, web posts) and ingest into Obsidian vault Raw/ with automatic theme detection, summarization, and structured frontmatter. Handles X/fixupx links via multi-strategy extraction (direct fetch → r.jina.ai → nitter fallback).
| name | founder-content-engine |
| category | content |
| description | Weekly founder content research and production engine that transforms trending news into ready-to-use content assets. |
| version | 1.0.0 |
| author | Hermes Agent |
| license | MIT |
| prerequisites | {"env_vars":[],"commands":["python3","hermes"]} |
| metadata | {"hermes":{"tags":["Content","Research","Production","Weekly","Automation","Founder","Creator"]}} |
The Founder Content Engine is an autonomous weekly system that runs every Monday at 6:00 AM to generate comprehensive content assets. It transforms trending tech/startup news into ready-to-use content, eliminating blank-page syndrome for founders and creators.
News Sources → NewsFetcher → TrendAnalyzer → ContentGenerator → DeliveryManager → User
↓ ↓ ↓ ↓ ↓
Raw Data Trend Data Content Ideas Draft Content Published Content
Create the script directory:
mkdir -p ~/content_engine/outputs
Save the founder_content_engine.py script to ~/content_engine/
Edit the topics_of_interest list in the script to match your niche:
self.topics_of_interest = [
"Swift programming", "iOS development", "Swift on Server",
"AI agents", "autonomous systems", "content engines",
"startup building", "bootstrapping", "indie hacking",
"knowledge management", "Obsidian", "second brain",
"Vapor", "Docker", "Hetzner", "cloud infrastructure"
]
0 6 * * 1 cd ~/content_engine && python3 founder_content_engine.py
cronjob create --name='founder-content-engine' \
--schedule='0 6 * * 1' \
--script='~/content_engine/founder_content_engine.py'
python3 ~/content_engine/founder_content_engine.py
~/content_engine/outputs/
├── article_YYYYMMDD_HHMMSS.md # Long-form article draft
├── video_YYYYMMDD_HHMMSS.md # Video script
├── newsletter_YYYYMMDD_HHMMSS.md # Newsletter outline
└── logs/ # Delivery logs (optional)
Each output follows industry-standard formats:
Edit the search_terms list in fetch_news() to include your preferred sources:
search_terms = [
"tech news this week",
"startup funding this week",
"AI developments this week",
"Swift programming news",
"iOS development updates"
]
Modify the deliverables dictionary to change output quantities:
self.deliverables = {
"post_ideas": 5, # Change to desired number
"founder_stories": 3, # Change to desired number
"hooks": 10, # Change to desired number
"article_draft": 1,
"video_script": 1,
"newsletter_outline": 1
}
Extend the ContentEngine class with additional generation methods:
def generate_social_media_posts(self, trends: List[str]) -> List[str]:
"""Generate 5 social media posts from trends"""
posts = []
for trend in trends[:3]:
posts.append(f"Quick take on {trend}: {self._generate_social_hook(trend)}")
return posts[:5]
Run the script with verbose output:
python3 ~/content_engine/founder_content_engine.py
Check logs in ~/content_engine/outputs/ for detailed error information.
/skill_view/content-digests – Content aggregation patterns/skill_view/scheduled-reports – Delivery orchestration/skill_view/superpowers-brainstorming – Ideation techniques/opt/data/home/obsidian-vault/FACorreia/Kanban//opt/data/home/obsidian-vault/FACorreia/_wiki/MIT – Feel free to adapt and use for personal or commercial purposes.