MiniAgent AGENTS.md

Repository instructions for MiniAgent, a small Python command-line framework for building AI agents. The project emphasizes readable code, visible tool calls, and using shell commands for actions.

In plain words
What is it for?
Use them when developing or testing MiniAgent, changing its agent loop or command-line interface, or learning how text-based and function-based tool calls are implemented.
Why use it?
They explain the project structure and its design rules, helping an agent make changes without adding unnecessary tools, dependencies, or hidden behavior.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/zhulinsen/miniagent/agents-md
Clone the repo
git clone --depth 1 https://github.com/ZhuLinsen/MiniAgent

Made for: Codex, OpenCode.

Per session 2,033 This file is loaded in full into every session.
When invoked 2,033 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.02033 $0.02033
Opus 5 $0.01017 $0.01017
Sonnet 5 $0.00407 $0.00407
Haiku 4.5 $0.00203 $0.00203

Measured 3d ago against content hash fd2b6662b8ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

MiniAgent AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 173 lines

How it starts

The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — MiniAgent 项目指南

项目定位

MiniAgent 是一个极简、透明、强大的 CLI Agent 框架

一句话描述:用 ~400 行核心函数,实现 Claude Code 的编程能力 + Manus 的系统操控能力。

核心原则

1. 极简即力量

  • 核心 Agent 逻辑控制在 ~400 行,初学者 30 分钟可读完
  • 不追求工具数量,追求 bash + LLM 智能 的无限组合能力
  • 每一行代码都应该有存在的理由

2. 透明可控

  • 工具调用过程完全可见(TOOL: xxx ARGS: {...})
  • 没有黑盒抽象,没有魔法
  • 初学者可以清楚看到 AI Agent 是怎么工作的

3. bash 是万能工具

  • MiniAgent 不内置 100 个专用工具,而是依赖 bash + LLM 的组合
  • 要截图?LLM 会通过 bash 调用 python -c "from mss import mss; mss().shot()"
  • 要控制鼠标?LLM 会通过 bash 调用 pyautogui
  • 要爬网页?LLM 会通过 bash 编写并运行 Python 脚本
  • 这种设计让框架保持极简,同时能力无上限

4. 教学优先

  • 这是"最好的 AI Agent 教科书"
  • 代码结构清晰:agent.py(核心循环)→ tools/(工具集)→ cli.py(交互界面)
  • 支持文本解析和原生 Function Calling 两种模式,便于对比学习

5. 不做大杂烩

  • 不引入重型依赖(pyautogui/playwright/mss 等不作为内置依赖)
  • 不增加不必要的抽象层
  • 如果一个功能可以通过 bash 实现,就不为它单独建工具

架构概览

miniagent/
├── agent.py        # 核心 Agent 循环(~400行核心函数)
│                   # - LLM 客户端初始化
│                   # - 工具调用解析(文本模式 + 原生 FC 模式)
│                   # - 流式输出 (_call_llm_stream)
│                   # - 上下文管理 (_summarize_messages)
│                   # - 危险命令检测 (_check_dangerous)
│                   # - 工具执行循环
│                   # - 消息历史管理
├── cli.py          # 交互式命令行界面
│                   # - Rich 美化输出
│                   # - 流式 token 输出
│                   # - 工具执行回调显示
│                   # - 危险命令 Rich 确认弹窗
│                   # - 会话记忆集成
├── config.py       # 配置管理(.env / JSON / 环境变量)
├── logger.py       # 日志配置
├── memory.py       # 轻量会话记忆(~/.miniagent/memory.json)
├── mcp_client.py   # MCP 客户端 re-export(→ extensions/)
├── orchestrator.py # Agent 编排器 re-export(→ extensions/)
├── skills.py       # Skill 系统(可复用的 Agent 配置)
│                   # - name + prompt + tool whitelist + temperature
│                   # - 内置: coder/researcher/reviewer/tester
├── extensions/
│   ├── mcp_client.py   # MCP 协议客户端实现
│   │                   # - stdio JSON-RPC 传输
│   │                   # - 工具发现 + 调用
│   │                   # - 自动转为 MiniAgent 工具格式
│   └── orchestrator.py # Agent 编排器实现
│                       # - 任务分解(planner agent)
│                       # - 角色分配(基于 Skill 系统)
│                       # - 上下文传递
├── tools/
│   ├── __init__.py     # 工具注册系统(@register_tool 装饰器)
│   ├── code_tools.py   # 代码工具:read/write/edit/grep/glob/bash
│   └── basic_tools.py  # 基础工具:calculator/time/system/browser/clipboard/docx
└── utils/
    ├── json_utils.py   # 健壮的 JSON 解析(处理 LLM 输出的各种格式问题)
    ├── text_utils.py   # 共享文本工具(smart_truncate)
    └── reflector.py    # 反思机制(可选,用于改善推理质量)

Read the full file on GitHub · 173 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 173 lines · 2,033 tokens per session scan A fd2b6662b8ed

Subscribe to this mod's changes

MiniAgent AGENTS.md is an instructions file published in the GitHub repository ZhuLinsen/MiniAgent (198 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 2,033 tokens to every session, about $0.0102 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.