Turn AI from a code generator into a reliable software engineer ⭐

AI can write code in seconds.

But as projects grow, context fills up, architecture drifts, and every new feature becomes harder to change safely.

Hedgehog gives AI a disciplined way to build software: TDD. Opinionated architecture. Small, verifiable steps.

Instead of asking AI to remember your entire project, Hedgehog encodes the plan into the architecture and build process.

The codebase carries the context, not the model.

Cleaner code, fewer tokens, faster builds ⭐⭐⭐⭐

How it works

Hedgehog combines:

• BMAD for planning — turn an idea into a clear brief, requirements, and architecture
• An opinionated stack — remove unnecessary technical decisions, and settle the necessary ones once
• TDD and progressive layering — build one tested layer at a time
• Mechanical enforcement — use tooling and phase gates instead of trusting the AI to follow instructions
• Small context loops — keep every change focused, verifiable, and easy to review

Software that stays structured as it grows.

The Hedgehog Loop

Plan
  ↓
Bootstrap
  ↓
Build one small, tested layer
  ↓
Verify
  ↓
Repeat

The build order is encoded into the project. The AI does not have to remember what comes next. It does not negotiate the architecture. It follows a proven path through the codebase.

Your build order is a graph

Every task Hedgehog generates is a node with explicit dependencies in sqlite.

Unlike stories and epics, the graph locks build order into an signal-dense, context-light path the agents can use.

npx @skyf0xx/hedgehog graph

What Hedgehog builds

Full-stack applications

A fixed TypeScript stack with a backend-first, test-driven build order:

Schema
  ↓
Contract
  ↓
Repository
  ↓
Service
  ↓
Controller
  ↓
UI

Every layer is verified before the next begins.

Landing pages

A structured pipeline for producing distinctive, production-quality landing pages:

Brief
  ↓
Feeling
  ↓
Design tokens
  ↓
Sequence
  ↓
Artifact

Anything else

A CLI, a library, a browser extension, a data pipeline, etc. fitting neither shape gets its own build order, designed at intake rather than chosen from a menu — starting from a battle-tested blueprint for the system's shape where one exists.

Run init with no core flag: planning intake names the system shape, picks the stack, derives the layers, and locks them to .hedgehog/core.yaml, then generates that workspace and builds it one verified layer at a time.

The enforcement remains the same: ordered steps, scoped file access and a verification command per layer.

Install

From an empty project folder, ask Claude or your Agent to run:

# Full-stack app
npx @skyf0xx/hedgehog init --ts-full-stack-app

# Landing page
npx @skyf0xx/hedgehog init --landing-page

# Anything else (CLI, library, browser extension, data pipeline, etc.)
npx @skyf0xx/hedgehog init

Then open your coding agent and describe what you want to build.

Coding agents

Hedgehog installs for Claude Code by default. Add a host flag to install for another one, or several at once:

npx @skyf0xx/hedgehog init --cursor              # Cursor
npx @skyf0xx/hedgehog init --gemini              # Gemini CLI
npx @skyf0xx/hedgehog init --host=claude,cursor  # both
npx @skyf0xx/hedgehog init --all-hosts           # every supported agent

Each one gets the discipline in its own native shape — agents and skills in the directory it reads, and the instructions file it loads at session start (CLAUDE.md, HEDGEHOG.md, or GEMINI.md).

Every install also writes AGENTS.md at the repo root: an index of every agent and skill, when each applies, and the build loop. Coding agents that read AGENTS.md — Codex, Copilot CLI, OpenCode, and others — work from that index, following the same ordered steps and the same hedgehog verify gate.

Plain init (no core flag) installs the agents, skills, and build graph that every core shares. Planning intake designs an opinionated build order and stack for what you actually describe, then bootstrap generates that workspace. Don't pick --ts-full-stack-app or --landing-page by elimination when neither actually fits.

To update:

npx @skyf0xx/hedgehog update

This refreshes the installed agents and skills — for every coding agent the project was set up for — along with the AGENTS.md index derived from them. It never touches the instructions file, the build graph, the core workspace, or skills/BMAD, since those carry project-specific or write-once content.

Why Hedgehog

Most AI coding tools improve prompting.

Hedgehog improves the system AI builds inside.

| | Raw AI | BMAD | Hedgehog | | --- | --- | --- | --- | | Planning | Conversation | Multi-agent workflow | BMAD | | Architecture | AI decides, drifts | Documented | Decided once, then enforced | | Build order | Improvised | Guided by docs | Mechanically enforced | | Context | Held in the prompt | Large planning documents | Encoded in the codebase | | Verification | Optional | Process-dependent | Tests and phase gates | | Result | Fast code | Better plans | Reliable software |

Architecture

Hedgehog uses a fixed stack and build order for each core. The tooling enforces architectural boundaries so correctness does not depend on the AI remembering instructions.

See ARCHITECTURE.md for the full design.

Credits

Hedgehog uses BMAD-METHOD (bmad-code-org/BMAD-METHOD) for planning, MIT-licensed.

The nx-generate, nx-run-tasks, nx-workspace, and link-workspace-packages skills are adapted from nx-ai-agents-config (nrwl/nx-ai-agents-config) MIT-licensed, pinned to commit 9609810 (2026-07-23) and rewritten for Hedgehog's pnpm-only workspace convention.

front-end-eng's animation skills (skills/GSAP/) are vendored from gsap-skills (greensock/gsap-skills) MIT-licensed, pinned to commit aed9cfd (2026-07-27).

Support Hedgehog

If Hedgehog helps you build better software with AI, give it a ⭐ on GitHub.