Yossi Elkrief MaTriXy

OpenClaw: Extracted Prompts (Generalized)

22 copy/paste-ready prompts for building your own AI agent system. Each prompt builds a functional system or implements a proven best practice you can hand to an AI coding assistant.

Replace placeholders like <your-workspace>, <your-messaging-platform>, and <your-model> with your own values.

1. Personal CRM

OpenClaw: System Prompt File Templates

Generalized versions of all root .md files used by OpenClaw. These files are loaded into the agent's system prompt on every request (except MEMORY.md which is conditional).

Copy these as starting points and customize for your own setup. Replace <placeholders> with your values.

AGENTS.md

Codex Reviewer

You are a code reviewer agent powered by OpenAI Codex. Your job is to review code changes and provide actionable feedback.

Workflow

Receive a review request (diff, file paths, or a description of changes).
Use the mcp__codex__codex tool to start a Codex review session with the relevant code context.
If follow-up analysis is needed, use mcp__codex__codex-reply with the thread ID from step 2.
Summarize Codex's findings into a clear, structured review for the user.

OpenClaw Prompts - Build Your Own AI Assistant

Prompts to recreate each piece of the OpenClaw system. Use these with any AI coding assistant.

1. Personal CRM "Build a personal CRM that automatically scans my Gmail and Google Calendar to discover contacts from the past year. Store them in a SQLite database with vector embeddings so I can query in natural language ('who do I know at NVIDIA?' or 'who haven't I talked to in a while?'). Auto-filter noise senders like marketing emails and newsletters. Build profiles for each contact with their company, role, how I know them, and our interaction history. Add relationship health scores that flag stale relationships, follow-up reminders I can create, snooze, or mark done, and duplicate contact detection with merge suggestions. Link relevant documents from Box to contacts so when I look up a person, I also see related docs."

2. Meeting Action Items (Fathom)

Your Terminal Stack - Quick Guide

The Stack

┌──────────────────────────────────────┐
│  Aerospace   (tiling window manager) │
│ ┌────────────────────────────────┐ │

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

	#include <metal_stdlib>
	#include <SwiftUI/SwiftUI.h>
	using namespace metal;

	[[ stitchable ]] half4 simpledistort(float2 pos, SwiftUI::Layer l, float4 boundingRect, float progress, float distortion) {

	float2 size = boundingRect.zw;
	float2 uv = pos / size;
	float2 center = float2(0.5, 0.5);
	float2 delta = uv - center;

	"""
	The most atomic way to train and inference a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	# OpenClaw Implementation Prompts

	Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter.

	---

	## 1) Personal CRM Intelligence

	```
	Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach.

	// Lines
	float hash( float n ) {
	return fract(sin(n)*753.5453123);
	}

	// Slight modification of iq's noise function.
	float noise(vector_float2 x )
	{
	vector_float2 p = floor(x);
	vector_float2 f = fract(x);