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AI orchestration platform — 16-phase workflow engine for autonomous software development","maintainers":[{"name":"ans4175","email":"ans4175@gmail.com"},{"name":"anak10thn","email":"anak10thn@gmail.com"}],"readme":"# Jonggrang\n\n> AI agents write code fast. Too fast. Jonggrang makes sure they don't make a mess while doing it.\n\nJonggrang is a CLI tool that puts AI coding agents through a **Plan → Implement → Simplify → Test → Review** pipeline. It breaks features into atomic tasks, runs each one with a fresh agent, and refuses to let anything through until the quality gates are green.\n\nWorks with four AI backends: [OpenCode](https://opencode.ai/), [Claude Code](https://claude.ai/code), [OpenAI Codex CLI](https://github.com/openai/codex), and **Jonggrang** (built on [Pi SDK](https://pi.dev/)). Pick your poison.\n\n---\n\n## What Makes It Different\n\nAI agents write code fast. Too fast. The bottleneck isn't speed — it's **knowing when to stop and clean up.**\n\nJonggrang enforces a pipeline where every feature passes through the same gates: Plan → Implement → Simplify → Test → Review. Not because every step is always necessary, but because skipping steps is how complexity accumulates silently.\n\n**Fresh context per task.** Each task starts with a clean agent instance. No stale assumptions carrying forward. **Hooks police in real-time** — secrets blocked, context overload prevented, exit refused until quality gates pass. **Simplify phase** revisits every changed file to reduce complexity before the PR opens.\n\nLeft unchecked, an AI agent will always take the shortest path — even if that path goes through a minefield. Jonggrang puts up the guardrails.\n\n→ [Read the full philosophy & architecture](docs/PHILOSOPHY.md)\n\n---\n\n## 🚀 I Just Want to Use It\n\nFive commands. That's all you need.\n\n```bash\n# In your project directory:\njonggrang init\njonggrang plan \"what you want to build\"    # AI writes a plan — you review it\njonggrang approve                          # Decompose plan into tasks\njonggrang work                             # Execute tasks one by one\njonggrang review                           # Comprehensive code review\n```\n\n**One-shot shortcut:**\n```bash\njonggrang work \"REST API for todo management\" --yes\n```\n\n**Interactive chat:**\n```bash\njonggrang agent    # Full TUI chat with /plan, /work, /review, etc.\n```\n\n→ [Step-by-step guide for beginners](docs/QUICKSTART.md)\n\n---\n\n## 🔧 I Want to Hack on It\n\nCome on in. The water's fine.\n\n```bash\ngit clone <repo-url> && cd jonggrang\nmake install\nmake build\n```\n\nProject entry points: CLI binary, Pi TUI extension, web dashboard server. Hooks live in `hooks/`, skills in `skills/`, orchestration engine reads `MANIFEST.yaml`.\n\n→ [Full development setup guide](docs/QUICKSETUP.md)\n\n---\n\n## Commands at a Glance\n\n| Command | What it does |\n|---------|-------------|\n| `jonggrang init` | Interactive wizard — sets up `.jonggrang/`, `AGENTS.md`, hooks, skills |\n| `jonggrang plan \"desc\"` | AI analyzes the goal, asks clarifying questions if anything is ambiguous, then writes a draft plan to `.jonggrang/.drafts/<session>/plan.md` — human reviews before code |\n| `jonggrang plan --append <id> \"desc\"` | Extend an **existing approved plan**: generate a delta draft for the additional scope only; on `approve` the new tasks are appended to that feature (numbering continues, completed tasks untouched) |\n| `jonggrang approve` | Decomposes the most-recent draft (or `--session <id>`) into `.jonggrang/.output/features/<id>/jonggrang-tasks.json`. With `--feature <id>` (or a draft carrying `append_to:`) it decomposes into an **existing** feature instead of minting a new one |\n| `jonggrang work` | Executes task queue with fresh context per task |\n| `jonggrang status` | Shows task board |\n| `jonggrang review` | Comprehensive code review → markdown report |\n| `jonggrang agent` | Full TUI chat session with `/plan`, `/work`, `/review` commands |\n| `jonggrang web` | Visual Kanban dashboard with real-time logs + parallel run (one worktree/branch per plan, review & push per branch) |\n| `jonggrang issues list` | List GitHub/GitLab issues from configured sources (or `--repo owner/repo`) |\n| `jonggrang issues pickup <p> <repo> <n>` | Generate a plan in this project from a GitHub/GitLab issue |\n| `jonggrang manifest` | Inspect output files tracked per phase (`list`, `show [id]`, `add`) |\n| `jonggrang design` | Global, reusable design templates + web studio (`list`, `new`, `promote`, `show`, `get`, `validate`) |\n| `jonggrang memory` | Read, recall, stage fragments, compact feature memory, and promote stable project lessons |\n| `jonggrang codemap` | Show/refresh deterministic codebase map (LLM-free, cached at `.jonggrang/codemap/codemap.json`) |\n| `jonggrang device` | Local-sandbox devices: `register` this machine on a jonggrang server, or `key` + `adopt` to pair with the dashboard instead of ssh; `list` / `remove` on the server (`provision` is what register runs there over ssh) |\n| `jonggrang tunnel` | The reverse SSH tunnel a registered device keeps open: `up`, `status`, `down` |\n\n```bash\n# Quick flags\njonggrang init --force               # Re-init: refresh AGENTS.md, CLAUDE.md, skills and sub-agents\njonggrang plan \"feature\" --yes       # Skip review, auto-approve\njonggrang work \"feature\" --yes       # Full pipeline in one command\njonggrang plan \"feature\" --src docs/brd.md  # Reference source document for the agent to read\njonggrang plan \"feature\" --deep      # 3-phase deep analysis (risks, alternatives)\njonggrang plan \"feature\" --base develop  # Cut the worktree from a chosen branch (fetched fresh from origin)\njonggrang plan \"feature\" --baseline helo@1  # Style from a design template / baseline pack (skips the design question); web = the \"design:\" picker\njonggrang plan \"feature\" --no-ask    # Skip the agent's clarifying-questions step\njonggrang plan --append feat-abc123 \"also add rate limiting\"  # Extend an approved plan (tasks appended, numbering continues)\njonggrang plan --append feat-abc123 \"...\" --deep  # Deep analysis on the added scope (Affected Areas / Risks)\njonggrang approve --session draft-abc123  # Approve a specific pending draft\njonggrang approve --feature feat-abc123    # Decompose the draft into an EXISTING feature (append)\njonggrang work --mode autonomous     # Override autonomy mode\njonggrang work --compact             # Stop after Implement, defer the quality gates (memory still written)\njonggrang work --resume --full       # Run the gates a compact run deferred\njonggrang work --task task-003       # Execute specific task only\njonggrang work --feature feat-abc123 # Target a specific approved feature (multi-feature projects)\n```\n\n> When the request is ambiguous, `plan` first asks you a few clarifying questions\n> (pick an option — each carries its rationale — or type your own answer) so the\n> agent plans from real intent instead of guessing. The Q&A is saved with the plan\n> and reused on `plan --revise`. The web dashboard shows the same questions as a form.\n>\n> UI plans also audit the project's existing design system. They produce a tracked\n> `.jonggrang/UI.md`, a per-feature `UI_HANDOFF.md`, and bounded task context instead\n> of injecting a whole design guide into every agent. See [UI planning context](docs/UI_CONTEXT.md).\n\n---\n\n## Requirements\n\n- **Node.js** (latest LTS)\n- **An AI agent** — pick one:\n  - [OpenCode](https://opencode.ai/) → `curl -fsSL https://opencode.ai/install | bash`\n  - [Claude Code](https://docs.anthropic.com/en/docs/claude-code) → `npm install -g @anthropic-ai/claude-code`\n  - [OpenAI Codex CLI](https://github.com/openai/codex) → `npm install -g @openai/codex`\n  - Jonggrang (Pi SDK) → `npm install -g @earendil-works/pi-coding-agent`\n- [jq](https://jqlang.github.io/jq/) → `brew install jq`\n- git\n- *(optional)* [anoa](https://github.com/porcupine-md/anoa-browser) → `curl -fsSL https://raw.githubusercontent.com/porcupine-md/anoa-browser/master/scripts/install-linux.sh | bash` — lets agents validate frontend design in a real headless browser. A single self-contained binary that carries its own browser, so there is no second download; preinstalled in the Jonggrang sandbox image.\n\n---\n\n## Configuration\n\n```jsonc\n// .jonggrang/jonggrang.json (after init)\n{\n  \"tool\": \"opencode\",          // opencode | claude | jonggrang | codex\n  \"mode\": { \"autonomy\": \"balanced\" },\n  \"work\": { \"max_iterations\": 0 }   // 0 = unlimited (run until all tasks complete)\n}\n```\n\nTwo-layer config: `~/.jonggrang/settings.json` (your defaults) → `.jonggrang/jonggrang.json` (this project's quirks).\n\n→ [Full config reference](docs/CONFIG.md)\n\n---\n\n## 📚 More Reading\n\n| Doc | When you want to... |\n|-----|---------|\n| [QUICKSTART.md](docs/QUICKSTART.md) | Get building in 5 minutes |\n| [QUICKSETUP.md](docs/QUICKSETUP.md) | Set up your dev environment |\n| [PHILOSOPHY.md](docs/PHILOSOPHY.md) | Understand why this thing exists |\n| [JONGGRANG.md](docs/JONGGRANG.md) | Read the full blueprint |\n| [WORKFLOW.md](docs/WORKFLOW.md) | Grok the 16-phase pipeline |\n| [SKILLS.md](docs/SKILLS.md) | Teach the agents new tricks |\n| [CONFIG.md](docs/CONFIG.md) | Tweak every knob |\n| [UI_CONTEXT.md](docs/UI_CONTEXT.md) | Understand UI guides, baseline packs, and feature handoffs |\n| [UI_BASELINES.md](docs/UI_BASELINES.md) | Add or version a UI baseline pack |\n| [DESIGN.md](docs/DESIGN.md) | Global design templates + the web Design studio |\n| [COMMIT-CONVENTION.md](docs/COMMIT-CONVENTION.md) | Write agent-readable commit messages |\n\n---\n\n## Contributors\n\n- **[Eka Irawan (Ibnu)](https://github.com/anak10thn)** + **[Ahmad Anshorimuslim Syuhada (Ans)](https://github.com/ans-4175)**\n\n→ [Full credits & backstory](AUTHORS.md)\n\n---\n\n## License\n\nMIT © Porcupine Team\n\nSee [LICENSE](LICENSE) for full text. TL;DR: **free to use for anything, including commercial, with no warranty. Contributors are protected from liability.**\n","readmeFilename":"README.md"}