An opinionated AI engineering workflow for BMAD

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.

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.

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.

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.

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

Brief
  ↓
Feeling
  ↓
Design tokens
  ↓
Sequence
  ↓
Artifact

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.

From an empty project folder, run:

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

npx @skyf0xx/hedgehog init --landing-page

npx @skyf0xx/hedgehog init

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

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.

To see the build graph:

npx @skyf0xx/hedgehog graph

Starts a small local server and opens a live, read-only diagram of every task, status and its dependencies.

Most AI coding tools improve prompting.

Hedgehog improves the system AI builds inside.

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.

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).

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