DevSpark Technical Summary DevSpark is a structured workflow framework designed to standardize AI-assisted software development through a collection of 28 markdown-based slash commands and supporting scripts, enabling repeatable processes from requirements specification through release without requiring installation or subscriptions. The system is primarily composed of Python (50.2%), PowerShell (26.3%), and Shell (23.4%) code, leveraging declarative configuration management via Pydantic, JSON Schema, and YAML to define reusable workflows, with an optional CLI built on Click for automation and validation tasks. The architecture follows a prompt-first, no-install philosophy where core functionality ships as markdown templates that AI agents (Claude, Copilot, Cursor, Gemini, and 13+ others) can directly consume, supplemented by context-gathering scripts and a harness runtime system that orchestrates multi-step engineering workflows with structured artifact preservation and execution tracing. Key capabilities include constitution-based code reviews, specification-to-implementation pipeline orchestration, pull request automation, knowledge archival through the canonical "harvest" workflow, and optional multi-app monorepo support with application registry management. The tech stack maintains a 76/100 currency score with modern dependencies, though the project shows stale activity (zero commits in 90 and 365 days), indicating it may be in maintenance mode despite solid foundational design. DevSpark targets development teams seeking to reduce cognitive load when working with AI coding assistants by providing a battle-tested, agent-agnostic workflow abstraction that bridges the gap between natural language requirements and production-ready implementations.