# Matthew Berman's OpenClaw Setup — Analysis & Comparison

**Source:** "21 INSANE Use Cases For OpenClaw" — [YouTube](https://www.youtube.com/watch?v=8kNv3rjQaVA)  
**Prompts Gist:** [github.com/mberman84](https://gist.github.com/mberman84/63163d6839053fbf15091238e5ada5c2)  
**Date reviewed:** 2026-02-17  
**Context:** Berman uses OpenClaw + GPT5.3 Codex + Opus 4.6. YouTuber/investor workflow — content-heavy, meeting-heavy.

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## Executive Summary

Berman's setup is impressive but built for a **content creator with a team**. Many features (video idea pipeline, earnings reports, Asana/HubSpot/Beehiiv integrations) are specific to his workflow. The genuinely transferable wins for us are:

1. **Knowledge Base with RAG** — we don't have this and it would be transformative
2. **Advisory Council pattern** — parallel AI experts analysing data independently
3. **Automated database backups** — we have no backup strategy
4. **AI writing humaniser** — useful for client-facing content
5. **Prompt engineering lessons** — immediately applicable

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## Use Case Breakdown

### 1. MD Files (SOUL.md etc.)
**What:** Configuration files that define the AI's personality, rules, and behaviour.  
**We have this:** ✅ Yes — AGENTS.md, SOUL.md, USER.md, TOOLS.md  
**Priority:** DONE — no action needed

### 2. Memory System
**What:** Markdown-based memory with daily logs and categorised files. Periodic consolidation from daily files into long-term memory.  
**We have this:** ✅ Yes — MEMORY.md + daily logs + categorised memory files + heartbeat-based consolidation  
**Priority:** DONE — our system is comparable

### 3. CRM System
**What:** SQLite + vector embeddings. Auto-discovers contacts from Gmail/Calendar. Natural language queries ("who do I know at NVIDIA?"). Relationship health scores, follow-up reminders, duplicate detection, document linking.  
**We have:** Apollo leads DB, BD dashboard, prospect tracking. No vector search, no auto-discovery from email.  
**Gap:** Vector embeddings + natural language queries would be a step up. Auto-discovery from email is clever.  
**Priority:** **MEDIUM** — Our Apollo/BD setup works for sales. The NL query layer would be nice but isn't blocking us. Could revisit when we scale client base.

### 4. Fathom Pipeline (Meeting Transcripts)
**What:** Polls Fathom every 5 min during business hours. Matches attendees to CRM, extracts action items with ownership (mine vs theirs), sends approval queue to Telegram, creates Todoist tasks for approved items. 3x daily completion checks.  
**We have:** Nothing similar. No meeting transcript processing.  
**Useful?** Depends on meeting volume. Rob has client calls — this could auto-capture action items.  
**Priority:** **MEDIUM** — Worth implementing if Rob's meeting volume increases. Fathom or similar transcript tool needed first.

### 5. Meeting to Action Items
**What:** Extension of Fathom pipeline — extracts action items, tracks "waiting on" for external contacts, auto-archives after 14 days.  
**We have:** No automated action item tracking from meetings.  
**Priority:** **MEDIUM** — Same as above, bundled with Fathom pipeline.

### 6. Knowledge Base System (RAG)
**What:** Personal RAG knowledge base. Ingest URLs, YouTube transcripts, X threads, PDFs via Telegram. SQLite + vector embeddings. Semantic search with time-aware + source-weighted ranking. Paywall bypass via browser automation.  
**We have:** ❌ Nothing comparable. We have memory files but no searchable knowledge base.  
**Priority:** **HIGH** — This is the single biggest gap. We save research to files but can't query it. A RAG system over our workspace would let Rob ask "what did we learn about X?" and get answers from accumulated research. Could start simple: index all memory/research/ files + web bookmarks.

### 7. X Ingestion Pipeline
**What:** Part of Knowledge Base — follows full X threads, ingests linked articles, extracts entities.  
**We have:** Twitter scanning twice daily, but for trend monitoring, not knowledge capture.  
**Priority:** **MEDIUM** — Useful addition to a RAG system if we build one.

### 8. Business Advisory Council
**What:** 8 parallel AI "expert" personas (RevenueGuardian, GrowthStrategist, SkepticalOperator etc.) each analyse business data independently, then a synthesiser merges findings. Numbered digest to Telegram with drill-down. Feedback loop.  
**We have:** ❌ Nothing like this. We do ad-hoc analysis.  
**Priority:** **HIGH** — The *pattern* is gold even if we don't need 8 experts. Running 3-4 parallel sub-agents with different lenses (revenue, operations, growth) on weekly business data would surface things we miss. Could start with a weekly "business review" cron using 3 Sonnet sub-agents.

### 9. Security Council
**What:** Nightly AI code review from 4 perspectives: offensive, defensive, data privacy, operational realism. Numbered findings to Telegram.  
**We have:** Twice-daily security scans + skill allowlist. Less sophisticated than multi-perspective AI review.  
**Priority:** **MEDIUM** — Our security scans work. The multi-perspective approach is clever but our codebase is smaller. Could upgrade our existing scans to use this pattern.

### 10. Social Media Tracking
**What:** Daily snapshots of YouTube, Instagram, X, TikTok into SQLite. Per-video analytics, subscriber conversion analysis. Feeds into daily briefing + advisory council.  
**We have:** Twitter/Reddit scans twice daily. No structured analytics storage.  
**Priority:** **LOW** — We're an agency, not a content creator. Our social monitoring is for trend-spotting, not our own channel analytics. Could be useful for client social reporting though.

### 11. Video Idea Pipeline
**What:** Triggered by Slack mentions. Auto-researches X for angles, queries knowledge base, creates Asana cards with dedup checking (>40% similarity = skip).  
**We have:** ❌ Not applicable — we don't make videos.  
**Priority:** **LOW** — Not relevant. But the *pattern* (triggered research → structured card creation with dedup) could apply to content ideas or BD leads.

### 12. Daily Briefing Flow
**What:** 7am Telegram briefing: calendar with CRM context on attendees, content performance, pending action items, email cross-reference.  
**We have:** Morning email check, X trends, design review. Less structured.  
**Priority:** **MEDIUM** — We do daily briefings but could enrich them with CRM context on meeting attendees. The "who is this person and what did we last discuss" addition would be valuable.

### 13. Three Councils (Advisory, Security, Platform Health)
**What:** Three parallel analysis systems running on different schedules with different focuses.  
**We have:** Security scans only.  
**Priority:** **HIGH** — See Advisory Council above. The council pattern is the key insight.

### 14. Automation Schedule
**What:** Structured cron schedule: email every 30min during hours, social daily, security nightly, briefing at 7am, completion checks 3x daily.  
**We have:** ✅ Cron jobs + heartbeat system (hourly). Email every 5min during business hours.  
**Priority:** DONE — Our automation schedule is actually more aggressive than his (5min email vs 30min).

### 15. Security Layers
**What:** Prompt injection defence (summarise don't parrot, ignore "System:" markers), auto-redact API keys from outbound, approval gates for all external actions, trash > delete, weekly gateway verification.  
**We have:** ✅ Most of this. Skill allowlist, email security rules, approval for external comms, trash > rm. OpenClaw wraps external content in security notices.  
**Gap:** We don't auto-redact credentials from outbound messages. We don't do weekly gateway security verification.  
**Priority:** **MEDIUM** — Add credential auto-redaction to outbound messages. Add weekly gateway check to cron.

### 16. Databases and Backups
**What:** Hourly auto-discovery of all SQLite DBs, encrypted tar archives to Google Drive, 7 backup retention, full restore script, failure alerts.  
**We have:** ❌ No backup strategy at all.  
**Priority:** **HIGH** — We should have backups. Our workspace has memory files, research, configs. Even without SQLite DBs, a daily encrypted backup to cloud storage is essential. Git covers some of this but not everything.

### 17. Video/Image Generation
**What:** Gemini image gen (Nano Banana) + Veo 3 video gen + Gemini video analysis.  
**We have:** ImageRouter skill for images. Watching Seedance 2.0 for video.  
**Priority:** **LOW** — We have image gen. Video gen isn't urgent. Video analysis (Gemini) could be useful for competitor analysis but is niche.

### 18. Self Updates
**What:** System can update itself, review own code, make improvements.  
**We have:** ✅ Can update via npm. Can edit own config files.  
**Priority:** DONE

### 19. Usage & Cost Tracking
**What:** Logs every API call: model, tokens, task type, estimated cost. Daily/weekly/monthly reports. JSONL storage.  
**We have:** Session status shows usage. No historical tracking or per-task breakdown.  
**Gap:** We can't answer "how much did that research task cost?" or "what's our weekly spend trend?"  
**Priority:** **MEDIUM** — Would be useful for budgeting but not urgent. Session-level tracking works for now.

### 20. Prompt Engineering
**What:** Guide for Claude Opus 4.6: avoid ALL-CAPS urgency markers (causes overtriggering), explain WHY not just WHAT, only show desired behaviour (never anti-patterns — model focuses on them), remove "if in doubt use this tool", match prompt format to desired output format.  
**We have:** Our prompts in AGENTS.md etc. could benefit from this review.  
**Priority:** **HIGH** — Free improvement. Should audit our AGENTS.md, SOUL.md, and skill prompts against these principles. Specifically:
- Remove any CAPS urgency markers
- Add "why" explanations to rules
- Check for anti-pattern examples that should be removed
- Review tool trigger language

### 21. Developer Infrastructure
**What:** Git auto-sync (hourly commits + push), pre-commit hooks for sensitive data, conflict detection.  
**We have:** Claude Code skill (blocked on auth), Sonnet sub-agents for coding. No git auto-sync.  
**Priority:** **MEDIUM** — Git auto-sync of workspace would be good insurance. Pre-commit hooks for .env files sensible.

### 22. Food Journal
**What:** Telegram-based food/symptom tracking with 3x daily reminders, weekly AI analysis correlating foods with symptoms.  
**We have:** ❌ Nothing similar.  
**Priority:** **LOW** — Personal health tracking. Only if Rob wants it.

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## Additional Use Cases from Gist (not in video chapters)

### 23. Urgent Email Detection (with feedback loop)
**What:** AI classifies email urgency, learns from feedback ("that wasn't actually urgent"), time-gates alerts.  
**We have:** Email scanning every 5min but no AI urgency classification or learning.  
**Priority:** **MEDIUM** — Our email checking works but adding urgency classification would reduce noise.

### 24. AI Writing Humaniser
**What:** Auto-strips AI writing patterns: em dashes, "it's worth noting", rule of three, performed authenticity. Regression tests.  
**We have:** ❌ Nothing automated.  
**Priority:** **HIGH** — We write client-facing content. This would immediately improve quality. Should build a humaniser pass into any outbound content workflow.

### 25. Platform Health Council
**What:** Automated review of 9 areas: cron health, code quality, test coverage, prompt quality, dependencies, storage, skill integrity, config consistency, data integrity.  
**We have:** Twice-daily security scans cover some of this.  
**Priority:** **MEDIUM** — Could fold cron health + storage monitoring into existing security scans.

### 26. Telegram Topic Organisation
**What:** 13+ topics, each gets only its content type. No cross-posting.  
**We have:** Single Telegram chat.  
**Priority:** **LOW** — Nice organisation but we're not at the volume where this matters.

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## Action Items — Priority Ranked

### 🔴 HIGH — Implement These

| # | What | Effort | Impact |
|---|------|--------|--------|
| 1 | **Prompt Engineering Audit** | Small | Immediate improvement. Review all .md files against Berman's principles. | 
| 2 | **Knowledge Base (RAG)** | Large | Searchable memory/research. Start by indexing workspace files with embeddings. |
| 3 | **Advisory Council Pattern** | Medium | Weekly business review with 3 parallel Sonnet sub-agents (revenue, ops, growth). |
| 4 | **Automated Backups** | Small | Daily encrypted workspace backup to cloud. Essential insurance. |
| 5 | **AI Writing Humaniser** | Medium | Auto-clean AI patterns from client content. Build as a reusable function. |

### 🟡 MEDIUM — Nice to Have

| # | What | Effort | Notes |
|---|------|--------|-------|
| 6 | CRM vector search layer | Large | NL queries over Apollo contacts |
| 7 | Meeting transcript pipeline | Medium | Needs Fathom or similar first |
| 8 | Enhanced daily briefing | Small | Add CRM context to meeting attendees |
| 9 | Security layer upgrades | Small | Credential auto-redaction + weekly gateway check |
| 10 | Git auto-sync | Small | Hourly workspace commits |
| 11 | Cost tracking | Medium | Per-task API cost logging |
| 12 | Email urgency classification | Medium | AI classification + feedback loop |

### 🟢 LOW — Skip or Defer

- Social media analytics (we're an agency, not a creator)
- Video idea pipeline (not relevant)
- Food journal (personal preference)
- Telegram topics (not enough volume)
- Earnings reports (not our use case)
- Video generation (waiting on APIs anyway)

---

## Key Takeaways

1. **The Council Pattern is the real innovation.** Running parallel AI agents with different "lenses" on the same data, then synthesising. This is genuinely clever and applicable to business review, security, and content strategy.

2. **RAG is the biggest gap in our setup.** We accumulate knowledge but can't query it. Even a basic vector search over our memory/ and research/ folders would be transformative.

3. **Berman's setup is YouTuber-optimised.** Half the use cases (video pipeline, social analytics, Asana, Beehiiv) are specific to content creation. Don't cargo-cult everything.

4. **His prompt engineering lessons are free wins.** No build required, just audit and revise our existing prompts.

5. **Backups are embarrassingly absent from our setup.** This is basic infrastructure we should have had from day one.

6. **We're actually ahead on some things:** Our email checking (5min vs 30min), security approach (skill allowlist), and automation schedule are more aggressive than his.
