원클릭으로 Manus에서 모든 스킬 실행

$pwd:

setup-environment

Name: Setup Environment
Author: jerry-ai-dev

// One-shot environment bootstrapper for the Smart Appointment AI Agent. Creates a Python virtual environment, installs dependencies from requirements.txt, scaffolds a .env file with generic OpenAI-compatible LLM / embedding / OpenWeather keys, prepares the data directory, and verifies the install. Use when user says "setup environment", "一键配置", "初始化环境", "install deps", "配置环境", "setup", "bootstrap", "搭建环境", or whenever a fresh checkout needs to be made runnable.

Manus에서 실행

$ git log --oneline --stat

stars:30

forks:13

updated:2026년 5월 8일 13:24

파일 탐색기

4 개 파일

SKILL.md

readonly

related-skills.json

같은 저장소

interview-prep.md

from "jerry-ai-dev/smart-appointment-ai-agent"

针对 Smart Appointment AI Agent（按摩房智能预约系统）的模拟技术面试官。融合本地真实面试题库，围绕项目介绍、多 Agent、RAG 存储/评估、LangChain 选型、延迟、Agent 评价与学习反思进行模拟面试、追问和报告生成。Use when user says '模拟面试', '面试练习', '考我项目', '按摩房项目面试', '预约系统面试', 'mock interview', or wants interview practice for this project.

2026-05-0830

project-learner.md

from "jerry-ai-dev/smart-appointment-ai-agent"

Smart Appointment AI Agent（按摩房智能预约系统）项目知识点打卡/复习教练。按知识域选择或推荐知识点，结合源码与本地真实面试题库进行问答、追问、评分、参考答案讲解和进度记录。Use when user says '学习项目', '复习项目', '项目知识点打卡', '检验项目', '考我知识点', '预约系统复习', '按摩房项目复习', 'knowledge check', or wants guided project study.

2026-05-0830

resume-writer.md

from "jerry-ai-dev/smart-appointment-ai-agent"

基于 Smart Appointment AI Agent（按摩房智能预约系统）生成定制化简历项目经历。沿用 Modular RAG MCP Server 简历生成风格，结合项目源码、Agent/RAG/用户记忆/反思包装方向，按四段式结构输出高质量中文或英文项目描述。Use when user says '写简历', '简历项目', '项目经历', 'resume', 'write resume', '包装项目', '优化简历', '按摩房项目简历', '预约系统简历', or asks to generate resume content based on this project.

2026-05-0830

package.json

"author": "jerry-ai-dev"

"repository": "jerry-ai-dev/smart-appointment-ai-agent"

GitHub 저장소 열기 Creator 저장소 보기

$ install --global

$ download --local

Manus에서 실행

$ useful --forSOC

네트워크·컴퓨터 시스템 관리자컴퓨터 및 수학직15-1244L4

name

setup-environment

description

One-shot environment bootstrapper for the Smart Appointment AI Agent. Creates a Python virtual environment, installs dependencies from requirements.txt, scaffolds a .env file with generic OpenAI-compatible LLM / embedding / OpenWeather keys, prepares the data directory, and verifies the install. Use when user says "setup environment", "一键配置", "初始化环境", "install deps", "配置环境", "setup", "bootstrap", "搭建环境", or whenever a fresh checkout needs to be made runnable.

Setup Environment

One trigger completes check Python → create venv → install deps → scaffold .env → init data dir → verify → (optional) launch app.

Optional modifiers:

--run after setup, also start uvicorn on 127.0.0.1:8001
--force recreate the .venv from scratch
--no-verify skip the import-time verification step

Pipeline

Check Python (3.10–3.12)
        ↓
Create / reuse .venv
        ↓
Activate venv  (idempotent)
        ↓
pip install -r requirements.txt
        ↓
Ensure .env (copy from .env.example if missing, then prompt for model keys)
        ↓
Ensure data/ directory exists
        ↓
Verify imports (fastapi, langchain, faiss, sqlalchemy, mcp …)
        ↓
[optional] uvicorn app:app --host 127.0.0.1 --port 8001

⚠️ Always use the project-local Python environment for this repository. Do not run this project with global/system python, pip, pytest, or uvicorn; global Python may be 3.13+ and incompatible with LangChain 0.3.x. Activate .venv first, or call the project interpreter directly.

Windows (PowerShell): .\.venv\Scripts\Activate.ps1

Windows (cmd): .\.venv\Scripts\activate.bat

macOS / Linux: source .venv/bin/activate

Direct Windows Python: .\.venv\Scripts\python.exe

Quick Start

Windows (PowerShell)

powershell -ExecutionPolicy Bypass -File .github\skills\setup-environment\scripts\setup.ps1

macOS / Linux

bash .github/skills/setup-environment/scripts/setup.sh

The script is idempotent: re-running only patches whatever is missing.

Step-by-Step (when running manually)

1. Check Python

Required: Python 3.10, 3.11, or 3.12.

⚠ Python 3.13 / 3.14 are not supported. PEP 649 deferred annotation evaluation in 3.13+ breaks LangChain 0.3.x / pydantic forward-ref evaluation (TypeError: 'function' object is not subscriptable). The setup script detects this and refuses to continue. On Windows, install Python 3.12 from python.org or via winget install --id Python.Python.3.12 -e. The script will auto-pick py -3.12 if available.

python --version

If missing or incompatible, stop and ask the user to install Python 3.10–3.12.

2. Create the virtual environment

python -m venv .venv

If --force was passed, delete .venv first.

3. Activate

# Windows
.\.venv\Scripts\Activate.ps1
# Unix
source .venv/bin/activate

4. Fill model configuration before continuing

Before installing dependencies or launching the app, ensure .env has real model settings. If .env is missing, copy .env.example to .env, then pause and ask the user to fill these fields.

This project uses two different model capabilities:

Chat LLM: used by the appointment, consultation, task-classification, and user-behavior agents to understand user messages and generate replies.
Embedding model: used by RAG retrieval and technician similarity matching to convert text into vectors.

Do not assume every provider offers both. Chat and embedding settings are intentionally separated in .env:

MODEL_PROVIDER=...
LLM_API_KEY=...
LLM_BASE_URL=...
LLM_MODEL=...

EMBEDDING_PROVIDER=...
EMBEDDING_API_KEY=...
EMBEDDING_BASE_URL=...
EMBEDDING_MODEL=...

The API key may be the same when one provider supplies both capabilities, such as Qwen, Zhipu, OpenAI, or Azure OpenAI. It may need to be different when the chat provider does not provide embeddings, such as DeepSeek.

Provider choices:

Provider	Chat LLM	Embedding	Where to get key	Fill in `.env`
Qwen	Yes	Yes	Alibaba Cloud Bailian / DashScope console	`MODEL_PROVIDER=qwen`, `EMBEDDING_PROVIDER=qwen`; fill `LLM_` and `EMBEDDING_`. The API key can usually be the same.
DeepSeek	Yes	Usually no general embedding API	DeepSeek Platform	`MODEL_PROVIDER=deepseek`; fill `LLM_*`. Choose another provider for `EMBEDDING_PROVIDER`, usually Qwen, Zhipu, OpenAI, or Azure.
Zhipu	Yes	Yes	BigModel console	`MODEL_PROVIDER=zhipu`, `EMBEDDING_PROVIDER=zhipu`; fill `LLM_` and `EMBEDDING_`.
OpenAI	Yes	Yes	OpenAI Platform	`MODEL_PROVIDER=openai`, `EMBEDDING_PROVIDER=openai`; fill `LLM_` and `EMBEDDING_`.
Azure OpenAI	Yes	Yes	Azure Portal	Use `MODEL_PROVIDER=azure` and/or `EMBEDDING_PROVIDER=azure`, then add and fill the matching `AZURE_OPENAI_*` values.

Recommended examples:

# Qwen for both chat and embeddings
MODEL_PROVIDER=qwen
LLM_API_KEY=your_qwen_key
LLM_BASE_URL=your_qwen_openai_compatible_chat_base_url
LLM_MODEL=your_qwen_chat_model

EMBEDDING_PROVIDER=qwen
EMBEDDING_API_KEY=your_qwen_key
EMBEDDING_BASE_URL=your_qwen_openai_compatible_embedding_base_url
EMBEDDING_MODEL=your_qwen_embedding_model

# DeepSeek for chat, Qwen for embeddings
MODEL_PROVIDER=deepseek
LLM_API_KEY=your_deepseek_key
LLM_BASE_URL=your_deepseek_openai_compatible_chat_base_url
LLM_MODEL=your_deepseek_chat_model

EMBEDDING_PROVIDER=qwen
EMBEDDING_API_KEY=your_qwen_key
EMBEDDING_BASE_URL=your_qwen_openai_compatible_embedding_base_url
EMBEDDING_MODEL=your_qwen_embedding_model

Stop here if any value still looks like your_..._here. Tell the user: “Please fill .env, then tell me it is ready, and I will continue setup.”

5. Install dependencies

.\.venv\Scripts\python.exe -m pip install --upgrade pip
.\.venv\Scripts\python.exe -m pip install -r requirements.txt

faiss-cpu may take 1–2 minutes on first install — that is normal.

6. Validate `.env`

If a .env already exists, leave it alone. Otherwise copy .env.example to .env. Then check the values:

Key	Required	Notes
`MODEL_PROVIDER`	✅	`qwen`, `deepseek`, `zhipu`, `openai`, `openai-compatible`, or `azure`
`LLM_API_KEY`	✅ for non-Azure	Chat model API key
`LLM_BASE_URL`	✅ for non-Azure	OpenAI-compatible base URL provided by the selected provider
`LLM_MODEL`	✅ for non-Azure	Chat model name provided by the selected provider
`EMBEDDING_PROVIDER`	✅	Usually `qwen`, `zhipu`, `openai`, or `azure`
`EMBEDDING_API_KEY`	✅ for non-Azure	Embedding model API key; can match `LLM_API_KEY`
`EMBEDDING_BASE_URL`	✅ for non-Azure	OpenAI-compatible embedding base URL provided by the selected provider
`EMBEDDING_MODEL`	✅ for non-Azure	Embedding model name provided by the selected provider
`AZURE_OPENAI_*`	✅ only for Azure	Used when provider is `azure`
`OPENWEATHER_API_KEY`	⚠ optional	Used by the MCP weather tool

If any required key still equals the placeholder (your_..._here), pause and ask the user to fill it in before continuing. Never commit a populated .env.

7. Ensure data directory

New-Item -ItemType Directory -Force -Path data | Out-Null

The app writes the SQLite DB and FAISS index cache here on first launch.

8. Verify

.\.venv\Scripts\python.exe .github\skills\setup-environment\scripts\verify_env.py

This script imports the critical packages and validates the model provider variables.

9. (Optional) Launch

.\.venv\Scripts\python.exe -m uvicorn app:app --host 127.0.0.1 --port 8001 --reload

Open http://127.0.0.1:8001/docs to confirm.

Troubleshooting

Symptom	Fix
`Activate.ps1 cannot be loaded because running scripts is disabled`	Run PowerShell as user and execute: `Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned`, or invoke with `powershell -ExecutionPolicy Bypass -File ...`
`faiss-cpu` build fails on Windows	Upgrade pip first (`python -m pip install --upgrade pip`), ensure Python is 64-bit, retry
`mcp` package not found	Confirm Python is 3.10–3.12; `mcp>=1.0.0` requires modern Python
`TypeError: 'function' object is not subscriptable` during LangChain import	You are on Python 3.13/3.14. PEP 649 breaks LangChain 0.3.x. Install Python 3.12 and rebuild `.venv` (`-Force`)
`ModuleNotFoundError` after install	The shell is using system Python. Re-activate `.venv` and re-run
Port 8001 already in use	Stop the previous server or pass `--port 8002` to uvicorn
Model auth errors at startup	Re-check `MODEL_PROVIDER`, `LLM_API_KEY`, `LLM_BASE_URL`, `LLM_MODEL`, and embedding settings in `.env`

Files in this skill

.github/skills/setup-environment/
├── SKILL.md           ← this file
└── scripts/
    ├── setup.ps1      ← one-shot bootstrapper for Windows
    ├── setup.sh       ← one-shot bootstrapper for macOS/Linux
    └── verify_env.py  ← import-level smoke test

A .env.example template lives at the project root (sibling of requirements.txt) so the bootstrap scripts can copy it on a clean checkout.

setup-environment

이 저장소의 다른 Skills

Setup Environment

Pipeline

Quick Start

Windows (PowerShell)

macOS / Linux

Step-by-Step (when running manually)

1. Check Python

2. Create the virtual environment

3. Activate

4. Fill model configuration before continuing

5. Install dependencies

6. Validate .env

7. Ensure data directory

8. Verify

9. (Optional) Launch

Troubleshooting

Files in this skill

Setup Environment

Pipeline

Quick Start

Windows (PowerShell)

macOS / Linux

Step-by-Step (when running manually)

1. Check Python

2. Create the virtual environment

3. Activate

4. Fill model configuration before continuing

5. Install dependencies

6. Validate .env

7. Ensure data directory

8. Verify

9. (Optional) Launch

Troubleshooting

Files in this skill

이 저장소의 다른 Skills

6. Validate `.env`

6. Validate `.env`