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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.

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.

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.

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)

Inbox Processing Workflow

Principles

The goal is to consistently keep the 000_Inbox at zero, deliberately processing each item to ensure nothing is lost and everything has an associated place or action. Attention span is a finite resource, and this workflow seeks to optimize its use.

Manual Triage Flow (Classic GTD)

This is the process for each item when processing the Inbox manually. Open each note, one by one, and follow this decision tree without moving to the next until action has been taken on the current one.

1. What is this? and Is it actionable?

tensor-compress: Complete Technical Specification

Executive Summary

tensor-compress is a production-grade Rust library implementing quantum-inspired Tensor Train (TT) decomposition for neural network compression with distributed parameter serving. The library enables 45-60% model size reduction while maintaining <1% accuracy loss, with seamless integration into vector databases like ruvector for edge AI deployment scenarios.

Key Innovation: Combines classical tensor factorization with modern distributed systems architecture, enabling surgical knowledge editing and cost-efficient model serving.

Public Data Sources

Streaming Metadata

Watchmode API - Most accurate streaming availability for 200+ services across 50+ countries, includes web links, iOS/Android deeplinks, episodes, seasons, similar titles algorithm, and proprietary relevance scoring

Flix Patrol https://flixpatrol.com/about/api/

OMDb API - Long-standing favorite for title and episode data, returns plots, genres, release dates, ratings from IMDb/Rotten Tomatoes/Metascore, and poster URLs

Production-Grade Tiny Recursive Model Router for AI Agent Orchestration

The research reveals that sub-millisecond neural routing can achieve 85-99% cost reduction compared to direct LLM inference while maintaining 90-95% quality. Production implementations at Cloudflare demonstrate 309µs P50 latency with 20% improvement through Rust optimization, while RouteLLM achieves 72% cost savings routing 74% of queries to lightweight models. This guide provides complete implementation patterns for Rust core, WASM sandboxed inference, and TypeScript integration via NAPI-RS, enabling real-time agent decision-making with guaranteed uncertainty quantification through conformal prediction.

Why this architecture matters for agent orchestration

AgentDB retrieval produces 50-100 memory candidates requiring scoring before expensive LLM evaluation. Without local routing, each agent decision costs $0.01-0.10 in API calls. A tiny FastGRNN model (under 1MB) can score candidates in 2-5µs each, routing only the top 3-

	# 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.

	#!/usr/bin/env bun
	/**
	* ============================================================
	* PROOF: Anthropic is specifically blocking "OpenCode"
	* in Claude Code OAuth system prompts
	* ============================================================
	*
	* Video covering this script here: https://www.youtube.com/watch?v=G9YX6StP2-M
	*
	* This script demonstrates that Anthropic has specifically blocked