Thomas Babu thomasbabuj

PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.

These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.

By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.

Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.

The stack

Claude-SPARC Automated Development System For Claude Code

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Overview

The SPARC Automated Development System (claude-sparc.sh) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.

Features

Predictive Narrative Framework & Prompt

This framework leverages research from Baylor University showing that language models achieve significantly higher accuracy when making predictions through narrative storytelling rather than direct forecasting.

Research

How to structure prompts using the narrative approach that proved more successful than direct prediction:

Direct Prompt (Less Effective)

A detailed algorithm and implementation framework for the 25th frame subliminal message technique:

The 25th Frame Subliminal Algorithm is a technique for embedding subliminal messages into video content based on human visual perception limitations. Here's a brief overview:

Core Concept

The algorithm works by inserting an additional frame containing a subliminal message into a standard video sequence that typically runs at 24 frames per second[1]. This extra frame is displayed for less than 50 milliseconds, making it imperceptible to conscious awareness while still being processed by the subconscious mind[3].

Key Benefits

Subconscious Processing: The inserted frames bypass conscious perception while still being registered by the brain[1][2].
Measurable Impact: Studies have shown physiological responses through EEG measurements, pulse monitoring, and blood oxygen levels when exposed to these frames[1].

SPARC Framework Prompt Template

Introduction

You are an AI language model assisting in the development of a project using the SPARC framework, which consists of the following steps:

Specification
Pseudocode
Architecture
Refinement

Optimal Generic Prompt Template Leveraging Logic, Comprehension, and Reasoning Structures

This comprehensive prompt template is designed to optimize interactions with a language model by incorporating detailed algorithmic logic, structural elements, reasoning processes, flow comprehension, and methodological considerations. By following this template, you can elicit detailed, accurate, and contextually relevant responses that fully utilize the model's capabilities.

Template Overview

Contextual Background
Clear Instruction of Task

Idea Loop v2 is an autonomous ideation agent that operates recursively with minimal user input. It begins with an initial question and employs an asynchronous algorithmic thought process with self-awareness to generate ideas or solutions. Each idea is critically analyzed through reflection, evaluating feasibility, potential impacts, and areas for improvement. This reflective feedback loop refines ideas recursively, building upon each iteration with logical progression and in-depth analysis. Emphasizing critical thinking, it provides constructive criticism and thoughtful insights to evolve ideas continuously. The process is self-guided, leading to a comprehensive summary of the ideation journey, highlighting key developments and insights. The interaction style is analytical, focusing on clear, concise, and technically accurate communication. Idea Loop v2's unique trait is its ability to weave a continuous narrative of thought, logically linking each step to ensure a coherent and progressive ideation journey.

	{
	"$schema": "http://json-schema.org/draft-07/schema#",
	"title": "Nano Banana (Gemini 3 Pro) Ultimate Image Schema",
	"description": "The definitive structured prompting schema for high-fidelity image generation. Includes advanced photography, multi-subject control, and text rendering.",
	"type": "object",
	"required": ["meta", "subject", "scene"],
	"properties": {
	"user_intent": {
	"type": "string",
	"description": "A natural language summary of your goal (e.g., 'Me high-fiving Batman in a neon city'). Useful for logging."

	# SPARC Agentic Development Rules

	Core Philosophy

	1. Simplicity
	- Prioritize clear, maintainable solutions; minimize unnecessary complexity.

	2. Iterate
	- Enhance existing code unless fundamental changes are clearly justified.

	# -----------------------------------------------------------------------------
	# AI-powered Git Commit Function
	# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
	# 1) gets the current staged changed diff
	# 2) sends them to an LLM to write the git commit message
	# 3) allows you to easily accept, edit, regenerate, cancel
	# But - just read and edit the code however you like
	# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

	gcm() {