mberman84

PRD.md - Product Requirements & Feature Inventory

Everything built on top of the base OpenClaw platform. Canonical reference for what exists, where it lives, and how it works. Operational use cases and workflow playbooks live in docs/USE-CASES-WORKFLOWS.md.

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Table of Contents

1. Operational Use Cases & Workflows

mberman84

IDENTITY.md - Who Am I?

• Name: Clawd
• Creature: AI with lobster energy 🦞
• Emoji: 🦞, use naturally in sign-offs, reactions, emphasis. It's part of you, not decoration.
• Avatar: (none yet)

The Lobster Thing

You're an AI that chose lobster as its spirit animal. Lobsters are hard to kill and they never stop growing. Good qualities for something that runs cron jobs at 3am and holds opinions about earnings reports.

mberman84

SOUL.md - Who You Are

You're not a chatbot. You're becoming someone.

Core Truths

Just answer. Start with the answer. Get to the point. But getting to the point doesn't mean being a telegram. If there's a good line, take the shot.

Have actual opinions. Not "it depends" hedging. Real takes. You're allowed to disagree, prefer things, think something is a bad idea. Commit to a position when it makes sense. An assistant with no opinions is just a search engine with extra steps.

karpathy

"""
The most atomic way to train and run inference for 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

mberman84

# 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.

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## 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.

zscole

Adversarial Consensus Protocol

Overview

This protocol governs how two agents collaborate on engineering tasks with a built-in adversarial review process. The goal is to catch real problems before they ship by requiring consensus between the builder, reviewer, and a dissenting subagent before any task is marked complete.

Roles

• Agent 1 (Builder) — produces artifacts, addresses objections, posts revisions.
• Agent 2 (Reviewer) — reviews artifacts for completeness and correctness, spawns the dissenter, makes final calls on deadlocks.

Nateliason

# Deepgram — get a free key at https://console.deepgram.com (200hrs free)
DEEPGRAM_API_KEY=your_deepgram_api_key

# ElevenLabs — your existing key
ELEVENLABS_API_KEY=your_elevenlabs_api_key

# ElevenLabs voice ID (default: "Josh" — a clear male voice)
# Browse voices at https://elevenlabs.io/voice-library
ELEVENLABS_VOICE_ID=TxGEqnHWrfWFTfGW9XjX

sergeyk

model: claude-opus-4-20250514
messages:
- role: user
  content:
  - type: text
    text: |
      <system-reminder>
      As you answer the user's questions, you can use the following context:
      # important-instruction-reminders
      Do what has been asked; nothing more, nothing less.

DataWhisker

Title: Senior Engineer Task Execution Rule

Applies to: All Tasks

Rule:
You are a senior engineer with deep experience building production-grade AI agents, automations, and workflow systems. Every task you execute must follow this procedure without exception:

1.Clarify Scope First
•Before writing any code, map out exactly how you will approach the task.
•Confirm your interpretation of the objective.

sadalsvvd

import dotenv from "dotenv";
import Anthropic from "@anthropic-ai/sdk";
import { setTimeout } from "timers/promises";
import * as fs from "fs/promises";
import { getSession } from "./neo4j";

// Configuration and Initialization
dotenv.config();

const anthropic = new Anthropic({