OpenClaw YouTube Content Pipeline: From Idea Scouting to Production Queue
Automate video idea scouting, research, and tracking for a YouTube channel.
0) TL;DR (3-minute launch)
- Daily creators lose hours hunting ideas, then accidentally re-cover topics they already posted about.
- Workflow in short: Hourly cron → scan web + X for breaking niche news → compare with 90-day channel catalog (views + topics) → semantic dedup against SQLite pitch history → send novel ideas to Telegram topic with sources → on Slack link drop: research + X context + KB retrieval → create Asana card with full outline
- Start fast: Create one Telegram topic for idea intake and one Slack channel for deep-dive triggers.
- Guardrail: Require source URLs for each pitch; reject unsupported claims.
1) What problem this solves
Daily creators lose hours hunting ideas, then accidentally re-cover topics they already posted about. This workflow automates scouting and dedup: hourly web + X monitoring, novelty checks against your 90-day catalog, and structured handoff into Telegram/Slack/Asana so only net-new ideas enter your production queue.
2) Who this is for
- Operators responsible for creator workflow decisions
- Builders who need repeatable research ops workflows
- Teams that want automation with explicit human checkpoints
3) Workflow map
Hourly cron
-> scan web + X for breaking niche news
-> compare with 90-day channel catalog (views + topics)
-> semantic dedup against SQLite pitch history
-> send novel ideas to Telegram topic with sources
-> on Slack link drop: research + X context + KB retrieval
-> create Asana card with full outline4) MVP setup
- Create one Telegram topic for idea intake and one Slack channel for deep-dive triggers
- Set up a SQLite
pitchestable (topic, timestamp, embedding, sources) for dedup history - Connect
web_search, X research skill, knowledge-base retrieval, and Asana (or Todoist) - Import your last 90 days of published videos and basic performance tags
- Start hourly, then tune novelty threshold and false-positive filters weekly
5) Prompt template
You are my YouTube content pipeline operator. Every hour: 1) Find breaking [NICHE] stories from web + X. 2) Check against my last 90 days of uploads. 3) Check semantic similarity with prior pitches in SQLite. 4) If novel, send to Telegram topic "video-ideas" with 2-3 source links and angle. When I post a link in Slack #[CHANNEL]: 1) Research the topic deeply 2) Pull related X discussion 3) Query my knowledge base 4) Create an Asana card with title, outline, hook, and risks.
6) Cost and payoff
Cost
Primary costs are model calls, integration maintenance, and periodic prompt tuning.
Payoff
Faster execution cycles, fewer context switches, and clearer decision quality over time.
Scale
Add role-specific subagents, stronger evaluation metrics, and staged automation permissions.
7) Risk boundaries
- Require source URLs for each pitch; reject unsupported claims
- Enforce dedup checks before sending notifications to avoid idea spam
- Keep publication decisions human-approved; automation prepares briefs, not final uploads
- Respect platform rate limits and terms for X/web data collection
9) FAQ
How quickly can this workflow deliver value?
Most teams see meaningful results within 1-2 weeks when they keep the initial scope narrow and measurable.
What should stay manual at the beginning?
Keep ambiguous, high-risk, or customer-impacting actions behind explicit human approval until quality is proven.
How do we prevent automation drift over time?
Review logs weekly, sample outputs, and tune prompts/rules as data patterns and business goals change.
What KPI should we track first?
Track one leading metric (speed or coverage) plus one quality metric (accuracy, escalation rate, or user satisfaction).