payload-discovery 0.1.5

System for creating numbered instruction sequences for agent consumption

Payload Discovery

A systematic agent learning framework that creates numbered instruction sequences and tracks progress through STARLOG integration.

Overview

Payload Discovery solves the problem of systematic agent learning by creating structured, sequential instruction files that agents consume in order. The system is stateless - all progress is tracked through STARLOG's debug diary, enabling agents to resume learning sessions seamlessly.

This package provides both a core library for creating instruction sequences and an MCP server for agent consumption.

Core Library Features

• 📚 PayloadDiscovery Models: Pydantic models for creating structured instruction sequences
• 🏗 Filesystem Rendering: Generate numbered instruction files from JSON configuration
• ✅ Validation System: Dependency checking and sequence validation
• 💾 JSON Serialization: Export/import instruction sequences as JSON

MCP Server Features

• 🛠 Waypoint Navigation: MCP tools for agents to traverse instruction sequences
• 📊 STARLOG Integration: Progress tracking through debug diary (stateless)
• 🔄 Resume Capability: Agents can restart and continue from last completed piece
• 🎯 Agent-Focused: Designed for autonomous agent consumption

Quick Start

Installation

[Installation instructions pending PyPI publication]

Basic Usage

from payload_discovery import PayloadDiscovery, PayloadDiscoveryPiece

# Create individual instruction pieces
piece1 = PayloadDiscoveryPiece(
    number=1,
    instruction="Analyze the codebase structure",
    context="Look for main modules and dependencies"
)

piece2 = PayloadDiscoveryPiece(
    number=2, 
    instruction="Identify entry points",
    context="Find main functions and CLI interfaces"
)

# Create a discovery sequence
discovery = PayloadDiscovery(
    title="Codebase Analysis Workflow",
    pieces=[piece1, piece2]
)

# Use the sequence
for piece in discovery.pieces:
    print(f"Step {piece.number}: {piece.instruction}")
    if piece.context:
        print(f"Context: {piece.context}")

MCP Server Usage

Start the MCP server:

payload-discovery-mcp

The server provides tools for:

• Creating new discovery sequences
• Loading existing sequences
• Navigating through instruction steps
• Saving workflow progress

Core Concepts

PayloadDiscoveryPiece

Individual instruction with:

• number: Step number in sequence
• instruction: What to do
• context: Additional guidance/information

PayloadDiscovery

Collection of pieces forming a complete workflow:

• title: Name of the workflow
• pieces: Ordered list of instructions
• metadata: Additional workflow information

Use Cases

• Agent Workflows: Systematic task completion
• Code Analysis: Structured codebase exploration
• Quality Assurance: Step-by-step validation processes
• Onboarding: Guided learning sequences
• Debugging: Systematic problem-solving approaches

Integration with HEAVEN Ecosystem

Payload Discovery integrates with:

• Waypoint: For navigation through sequences
• STARLOG: For tracking sequence completion
• Powerset Agents: For systematic agent workflows

Development

# Clone and install for development
git clone https://github.com/sancovp/payload-discovery
cd payload-discovery
pip install -e ".[dev]"

# Run tests
pytest

# Start development MCP server
python -m payload_discovery.mcp_server_v2

License

MIT License - see LICENSE file for details.

Part of HEAVEN Ecosystem

This library is part of the HEAVEN (Hierarchical Event-based Agent-Versatile Environment Network) ecosystem for AI agent development.