Praxis

Praxis


_From Greek: the process of putting ideas into practice._

A complete AI-assisted development workflow, packaged as portable agent skills and sub-agents. Praxis implements a structured development cycle — from idea to production code to documented learnings — designed to make each cycle of work improve the next. The name reflects what this workflow is about: not just thinking or just doing, but the disciplined cycle of idea → practice → reflection that makes each iteration better than the last.

This workflow mirrors how I personally develop software — brainstorm until the idea is clear, plan concretely before touching code, implement with discipline, review rigorously, and always look back to learn. Praxis encodes that process so AI agents can follow it consistently.

Inspired by [Every's Compound Engineering guide](https://every.to/guides/compound-engineering) and its core principle: **every unit of engineering work should make subsequent units easier, not harder.**

Why Praxis?

**Project and technology agnostic.** Praxis is not tied to any language, framework, or tech stack. It works with any codebase — drop it into an Elixir project, a React app, a Rust CLI, or a Rails monolith. The skills describe _how to work_, not _what to work on_.

**Context window efficient.** Every design decision respects the limited context window of AI agents. Templates are loaded on demand through progressive disclosure, not upfront. Research runs in parallel sub-agents that return summaries instead of polluting the main thread. Shared conventions live in one file, referenced by many. The goal: spend tokens on the real work, not on infrastructure.

**Tool agnostic.** No dependency on a specific AI coding tool. Skills and agents use standard markdown with YAML frontmatter, compatible with [Amp](https://ampcode.com), [Claude Code](https://code.claude.com), [Cursor](https://cursor.com), [OpenCode](https://opencode.ai), and similar tools.

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