待翻译：Show HN: "Hedgehog" - An Opinionated workflow for building with AI

AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。

Notifications You must be signed in to change notification settings Fork 0 Star 21 BranchesTags Open more actions menu Folders and files NameName Last commit message Last commit date Latest commit History 208 Commits 208 Commits .claude/skills/bmad-revendor .claude/skills/bmad-revendor .github/workflows .github/workflows .hedgehog/chain .hedgehog/chain bin bin docs/images docs/images scripts scripts skills skills src src .gitignore .gitignore ARCHITECTURE.md ARCHITECTURE.md CLAUDE.md CLAUDE.md CONTRIBUTING.md CONTRIBUTING.md LICENSE LICENSE README.md README.md TODO.md TODO.md package.json package.json Repository files navigation 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. 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, 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. 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. 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. Resources Readme MIT license Contributing Contributing Activity Stars 21 stars Watchers 0 watching Forks 0 forks Report repository