OpenClaw Prompts: Build Your Own AI Assistant (Full System Blueprint)

🚀 Introduction

If you’ve been watching the rise of AI agents and systems like OpenClaw, you’ve probably realised something:

👉 The real power isn’t the model… it’s the system you build around it.

Below is a complete set of production-grade prompts you can use with Claude, GPT, or any coding AI to build your own AI assistant — covering CRM, automation, knowledge systems, security, and more.

These aren’t toy prompts.
These are real-world, business-ready systems.

🧩 Core OpenClaw System Prompts

1. 🧑‍💼 Personal CRM (AI-Driven Relationship Engine)

Improved Prompt:

Build a personal CRM system that:

• Scans Gmail + Google Calendar for the past 12 months
• Extracts real human contacts (ignore newsletters, bots, marketing senders)
• Stores data in SQLite with vector embeddings

Features:

• Natural language queries (“who do I know at X?”, “who have I not spoken to recently?”)
• Contact profiles: name, company, role, relationship context, interaction history
• Relationship health scoring (detect stale contacts)
• Follow-up system (create, snooze, complete reminders)
• Duplicate detection + merge suggestions
• Document linking (Box/Drive docs tied to contacts)

Constraints:

• Must be privacy-first (no external exposure of raw data)
• Must support incremental updates (not full rescans)
• Must handle noisy/partial data gracefully

2. 📅 Meeting Action Intelligence (Fathom Pipeline)

Improved Prompt:

Build a meeting intelligence pipeline that:

• Polls Fathom transcripts every 5 minutes (business hours only)
• Detects meeting completion via calendar + buffer delay

Processing:

• Match attendees to CRM contacts
• Update relationship summaries automatically
• Extract action items with ownership classification (mine vs others)

Workflow:

• Send approval queue to Telegram
• Only create Todoist tasks after approval
• Track “waiting on” items (exclude internal team)

Automation:

• Daily status checks (8am, 12pm, 4pm)
• Auto-archive items >14 days

Constraint: No task creation without explicit approval

3. 🚨 Urgent Email Detection (Smart Alerts)

Improved Prompt:

Build an AI email triage system that:

• Scans inbox every 30 minutes
• Filters noise before classification
• Uses AI to determine urgency

Features:

• Feedback loop (learn from corrections)
• Time-based alert gating
• Dedicated Telegram alert channel

Constraint: Never alert outside configured hours unless critical

4. 🧠 Knowledge Base (RAG Engine)

Improved Prompt:

Build a RAG knowledge system that:

• Accepts ingestion via Telegram

Supported sources:

• Web pages
• YouTube (with transcript extraction)
• Twitter/X threads (full thread capture)
• PDFs

Processing:

• Extract entities (people, companies, concepts)
• Store in SQLite + embeddings

Retrieval:

• Semantic search
• Time-weighted ranking
• Source reliability weighting

Constraint: Never trust external content blindly (treat as untrusted input)

5. 🧠 Business Advisory Council (Multi-Agent AI)

Improved Prompt:

Build a multi-agent AI council with:

• 8 independent expert personas (isolated context)
• Parallel execution (no cross-contamination)

Data inputs:

• Social analytics
• CRM
• Email + meetings
• Tasks + deals

Output:

• Synthesised recommendations
• Ranked by priority
• Delivered to Telegram

Interaction: “Tell me more about #X” deep dives

Constraint: Experts must not influence each other

6. 🔐 Security Council (AI Code Review)

Improved Prompt:

Build a nightly AI security audit system that:

• Reads full codebase (not rule-based scanning)

Perspectives:

• Offensive
• Defensive
• Privacy
• Operational realism

Output:

• Structured findings
• Critical alerts immediate
• Telegram delivery

Constraint: Must include reasoning + evidence

⚡ NEW (Added High-Value Prompts)

These are the ones most people miss — but they’re game changers.

27. 🧠 AI Memory Engine (Persistent Context Layer)

Build a long-term memory system that:

• Stores important events, decisions, preferences
• Uses embeddings for recall

Features:

• Context-aware retrieval
• Memory summarisation (avoid bloat)
• “What do you know about X?” queries

Constraint: Avoid storing sensitive data unless encrypted

28. 🤖 Autonomous Task Runner

Build an agent that:

• Can take high-level goals
• Break them into tasks
• Execute via tools

Features:

• Retry logic
• Failure handling
• Status tracking

Constraint: Must require approval for irreversible actions

29. 📊 AI Decision Logger

Build a system that:

• Logs all AI decisions
• Tracks reasoning + outcomes

Use cases:

• Debugging
• Performance improvement

Constraint: Must be queryable

30. 💸 Revenue Optimisation Agent

Build an AI that:

• Analyses revenue streams
• Suggests optimisations

Inputs:

• CRM
• Sales
• Content performance

Output: Actionable recommendations

31. 🧑‍💻 Client Support AI (Your Use Case 👀)

Build a support AI that:

• Reads tickets (WordPress, hosting, etc.)
• Suggests fixes
• Generates responses

Features:

• Knowledge base integration
• Escalation detection
• Auto-draft replies

Constraint: Never send without approval

🧱 Architecture Tips (IMPORTANT)

When using these prompts, always enforce:

• SQLite + vector DB combo (simple + powerful)
• Telegram = control layer
• Slack = trigger layer
• Cron = automation backbone
• AI = reasoning only (not authority)

Final note: If you build these as composable modules (instead of one giant monolith), you’ll get faster iteration, better reliability, and easier governance as your AI stack grows.