momo-code

Name: momo-code
Author: momozi1996

by momozi1996

Verified

MOMO CODE — RSI *JEV*Pi --AI coding agent that evolves with you

1,184stars

7forks

TypeScript

Installation

# Add to your Claude Code skills
git clone https://github.com/momozi1996/momo-code

Getting Started

Guides for using ai agents skills like momo-code.

Caveman: Cut Claude Token Use by 65%
How agent-side prompt compression works, when to use it, and when not to.
What is an AI Skills Marketplace?
Definitions, how marketplaces work, and how to choose between them in 2026.
Getting Started with AI Skills

Security ReportVerified

Last scanned: 10/18/2026

{
  "issues": [
    {
      "file": "README.md",
      "line": 172,
      "type": "remote-install",
      "message": "Install command (remote install script piped to a shell — review the source before running): \"curl -fsSL https://momozi.cc/install | bash\"",
      "severity": "low"
    }
  ],
  "status": "PASSED",
  "scannedAt": "2026-10-18T08:33:58.828Z",
  "npmAuditRan": true,
  "pipAuditRan": true,
  "promptInjectionRan": true
}

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README.md

Frequently Asked Questions

What is momo-code?

momo-code is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by momozi1996. MOMO CODE — RSI *JEV*Pi --AI coding agent that evolves with you. It has 1,184 GitHub stars.

Is momo-code safe to use?

Yes. momo-code passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install momo-code?

Clone the repository with "git clone https://github.com/momozi1996/momo-code" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is momo-code written in?

momo-code is primarily written in TypeScript. It is open-source under momozi1996 on GitHub, so you can review or fork the full source.

Are there alternatives to momo-code?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh momo-code against similar tools.

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MOMO CODE


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╚═╝     ╚═╝ ╚═════╝ ╚═╝     ╚═╝ ╚═════╝    ╚═════╝ ╚═════╝ ╚═════╝ ╚══════╝

AI-powered coding agent that evolves with you.
Built on opencode with a unique dual-speed self-evolution system based on Pioneer Agent.

⚠️ Experimental / Active Development

** v2.0.0 (2026-10-11) 重大架构升级中。** 当前版本引入了 Pi 框架集成、RSI 自进化循环和 JEV 决策层。

Pi 框架集成（@earendil-works/pi-coding-agent@1.1.0）作为扩展依赖
交互式 REPL，带思维链面板、决策胶囊与键盘快捷键
JEV 决策层 — 5 个决策门，影子模式 + ECE 校准
RSI 3 级自进化循环（R1/R2/R3），带预算护栏与 STOP 保护
momo-bench — 3 层评估（selection/holdout/canary）+ 7 个反向测试
6 个 Pi 扩展：experience、rsi、permissions、decisions、cot、brand
DecisionPort 类型契约（choice/bool/score/abstain）
TaskResult + EventEnvelope 类型，用于结构化日志
EvolutionCandidate 接口，带 CAS 晋升保护
经验闭环链（TaskExposure → TaskOutcome → Solidify）
momo.jsonc v2 配置 schema，带优先级 resolver

🚀 v2.0 新特性

1. Pi 框架集成（Extension 模式）

以 npm 依赖形式集成 @earendil-works/pi-coding-agent@1.1.0，非 fork
6 个 momo 专属 Pi 扩展：
- experience — 自动注入历史验证过的战术
- rsi — 采集工具调用结果与会话指标
- permissions — 敏感操作（写入/危险命令）Y/n 确认
- decisions — L0 决策胶囊实时显示
- cot — 思维链面板（⌃T 折叠/展开）
- brand — momo 品牌 Banner

2. 统一 CLI 入口

momo                              # 进入交互式 REPL（默认）
momo "帮我写一个快速排序"          # 单次任务模式
momo exec "任务描述"               # 显式单次执行
momo --json "任务描述"             # JSONL 结构化输出
momo /evolve --demo               # v1.x 遗留命令（兼容）

3. 交互式 REPL

> prompt 交互输入
/help /exit /cot 内置命令
⌃T — 展开/折叠思维链
⌃O — 切换工具详情
⌃C — 中断退出

4. JEV 决策层（Judgment-Evaluation-Verification）

5 个决策门：tool / tactic / route / diff / promote
影子模式：并行记录 JEV 决策与 LLM 决策，校准 ECE
支持多后端：TypeSafe（推荐）、Kev、Laya-MLX、规则降级

5. RSI 自进化循环（3 级）

层级	范围	自动化程度
R1 Product	提示词补丁、战术参数	全自动
R2 Strategy	L0 决策模型权重	自动 + 校准门
R3 Mechanism	评估器本身	仅生成提案，需人工 Review

预算控制：日限额、成本上限、并发实验数
安全开关：touch ~/.momo/STOP 立即停止

6. momo-bench 评估框架

三层切分：selection(60%) / holdout(30%) / canary(10% 冻结)
晋升门：7 个反向测试防止作弊
任务隔离：快照 + 验证器分离 + 预算限制

📈 Star History

自动更新的 Star 涨幅线（由star-history.com提供）。2026-10-17 快照：1,162 stars（2026-06-16 创建 → 2026-07 末 181 → 2026-08 末 394 → 2026-09 末 884 → 2026-10-17 1,162）。

Architecture

Two-Speed Evolution Algorithm	System Technical Architecture

Features
Installation
Quick Start
Usage
CLI Commands
Configuration
Experience Fast Loop (/evolve)
Self-Evolution Training (/fine-tune)
v2.0 Architecture
Migrating from Claude Code
Environment Variables
Architecture
Changelog
License

Features

25+ LLM Providers — Deepseek, Zhipu (GLM), Moonshot (Kimi), Claude, GPT-4, Gemini, Doubao, OpenRouter, Groq, Mistral, and more. The \chat command uses OpenAI-compatible protocol only
Custom Provider — Plug in any OpenAI-compatible API with MOMO_CUSTOM_* env vars
Model Tiers — Zero-config selection: ultra / standard / lite
Interactive REPL — v2.0 native TTY interface with CoT panel, decision chips, keyboard shortcuts
Pi Framework Integration — v2.0 extension-mode agent runtime with 6 momo-specific extensions
JEV Decision Layer — Shadow-mode L0 decision model with ECE calibration
RSI Self-Evolution — 3-tier recursive improvement (R1/R2/R3) with budget rails and STOP guard
momo-bench — 3-tier evaluation (selection/holdout/canary) with 7 reverse tests
Experience Fast Loop (/evolve) — Second-level prompt injection via KEP protocol. Tactics distilled from success are auto-selected via Thompson sampling
Self-Evolution Training (/fine-tune) — Hour-level weight improvement via Monte Carlo Graph Search (MCGS) + LoRA
Self-Refinement (/refine) — Reviews session trajectories and proposes small, evidence-based improvements (tactics/prompt patches) that only take effect after human approval
Recursive Subagents (/agent) — RLM-style task decomposition: plan → parallel child processes → synthesis, with depth/budget rails
Graph Engine (/graph) — Long-horizon tasks as a resumable DAG of subagents: LLM-planned dependency graph, parallel execution, retries, and /sim world-agent nodes
Long-Running Work (/goal + /heartbeat + /daemon) — Persistent goals injected into every session, timed tasks, and a daemon loop for multi-hour autonomy
Simulation Agent (/sim) — LLM-driven control of a persistent Genesis physics world: the agent writes Python into a long-lived namespace (RLM-style), with skills-as-code loaded from ~/.momo/sim/skills/
Voice Input (/voice) — Speak your prompt: mic recording (sounddevice) → OpenAI-compatible STT (Whisper/Groq) → coding session
Claude Code Interop — Seamless migration, inherits .claude/ config, MCP servers, prompts
Local-first — Your code never leaves your machine. Open source, auditable
Effect-powered — Built with Effect for composable, type-safe code

Installation

Prerequisites

macOS or Linux (Windows via WSL)
Node.js ≥ 20.0.0 — install from https://nodejs.org if you don't have it
git and curl

Check with:

node -v   # should print v20.x or later
npm -v
git --version
curl --version

Quick install (recommended)

curl -fsSL https://momozi.cc/install | bash

The installer:

Clones the repo into ~/.momo/lib/momo-code
Runs npm install + npm run build (about 30-60 s)
Drops a wrapper at ~/.momo/bin/momo
Appends ~/.momo/bin to your PATH in ~/.zshrc or ~/.bashrc

Open a new terminal (or run source ~/.zshrc) and then:

momo --version    # 1.0.0
momo --help

If you see command not found: momo, your PATH didn't pick up the change — see INSTALL.md.

From source (manual)

git clone https://github.com/momozi1996/momo-code.git
cd momo-code/packages/opencode
npm install                # installs all deps including TypeScript + Pi framework
npm run build              # compiles TS + fixes ESM imports
npm run smoke              # run smoke tests (23 checks)
node bin/momo --version

Note: If npm install fails to fetch @earendil-works/pi-coding-agent (network issues), the CLI automatically falls back to momo Native REPL (simulation mode). All other features remain usable.

From npm

npm package coming in v1.1. Use the Quick install above for v1.0.

Uninstall

# Remove install artifacts
rm -rf ~/.momo

# Remove the PATH line from your shell rc
sed -i.bak '/# momo Code CLI/,+1d' ~/.zshrc    # zsh
sed -i.bak '/# momo Code CLI/,+1d' ~/.bashrc   # bash

Quick Start

1. Set up API key

# Generic key (works with any provider)
export MOMO_API_KEY=your-api-key

# Or provider-specific
export MOMO_ANTHROPIC_API_KEY=sk-ant-...
export MOMO_OPENAI_API_KEY=sk-...

2. Start coding

# Interactive REPL (v2.0 default)
momo

# One-shot task
momo "Refactor auth to use Effect"

# Use a model tier
momo --model ultra "Complex architecture review"
momo --model standard "Fix the login bug"
momo --model lite "Quick code review"

# JSONL structured output (v2.0)
momo --json "Generate a todo app"

# Legacy one-shot (v1.x compatible)
momo -p anthropic "task"

3. First run

On first run, momo creates ~/.momo/:

~/.momo/
├── momo.jsonc          # Config
├── sessions/           # History
├── experience/         # Learned tactics (auto-created)
│   ├── tactics.json
│   └── ledger.jsonl
├── rsi-meta.json       # RSI loop metadata (v2.0)
└── STOP                # Safety halt file (create to stop RSI)

Usage

Model Tiers

Tier	Use Case
`ultra`	Complex tasks, large context
`standard`	Daily coding work
`lite`	Quick tasks, low latency

CLI Options

momo [options] [prompt]

Options:
  --model, -m <id>       Model ID or tier
  --provider, -p <name>  Provider
  --json                 JSONL structured output (v2

momo-code

Compare with similar skills

Frequently Asked Questions

What is momo-code?

Is momo-code safe to use?

How do I install momo-code?

What programming language is momo-code written in?

Are there alternatives to momo-code?

Related Skills

MOMO CODE

⚠️ Experimental / Active Development

🚀 v2.0 新特性

1. Pi 框架集成（Extension 模式）

2. 统一 CLI 入口

3. 交互式 REPL

4. JEV 决策层（Judgment-Evaluation-Verification）

5. RSI 自进化循环（3 级）

6. momo-bench 评估框架

📈 Star History

Architecture

Table of Contents

Features

Installation

Prerequisites

Quick install (recommended)

From source (manual)

From npm

Uninstall

Quick Start

1. Set up API key

2. Start coding

3. First run

Usage

Model Tiers

CLI Options