基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/jr2804/prompts --skill documentation-standards命令会保持在同一行。复制前请横向滚动并检查完整内容。
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| name | documentation-standards |
| description | Universal documentation standards and best practices for software projects |
Provide universal documentation standards and best practices that apply across different projects and domains.
# Universal documentation structure
docs/
├── README.md # Project overview
├── architecture/ # System architecture
│ ├── overview.md # High-level architecture
│ └── components/ # Component details
├── development/ # Development guidelines
│ ├── setup.md # Development environment
│ ├── workflow.md # Development workflow
│ └── standards.md # Coding standards
├── api/ # API documentation
│ ├── reference.md # API reference
│ └── examples.md # Usage examples
└── user/ # User documentation
├── getting-started.md # Quick start guide
├── tutorials/ # Step-by-step tutorials
└── reference/ # Detailed reference
Universal Documentation Standards:
.md files)# Universal Markdown Standards
## Headings
- Use ATX-style headings (#, ##, ###)
- Maintain consistent heading hierarchy
- Limit to 3-4 heading levels
## Code Blocks
```python
# Always specify language for syntax highlighting
def example_function():
"""Example with proper formatting"""
return "formatted code"
```
## Lists
- Use consistent list formatting
- Prefer hyphens for bullet points
- Use numbered lists for sequential steps
## Links
- Use relative links for internal references
- Use absolute links for external references
- Include link descriptions
Use this skill when:
# Universal API Documentation Template
## Endpoint: `/api/v1/resource`
**Method:** `GET`
**Description:** Retrieve resource information
**Parameters:**
- `id` (string, required): Resource identifier
- `format` (string, optional): Response format (json, xml)
**Response:**
```json
{
"id": "string",
"name": "string",
"created_at": "datetime",
"status": "string"
}
```
**Examples:**
```bash
# Request example
curl -X GET "https://api.example.com/v1/resource?id=123&format=json"
# Response example
{
"id": "123",
"name": "Sample Resource",
"created_at": "2023-01-01T00:00:00Z",
"status": "active"
}
### Code Documentation
```python
# Universal Python docstring template
class DataProcessor:
"""Process and transform data for analysis.
This class provides methods for cleaning, normalizing, and
transforming raw data into analysis-ready formats.
Attributes:
config (dict): Configuration parameters for processing
logger (Logger): Logger instance for tracking operations
"""
def __init__(self, config=None):
"""Initialize DataProcessor with configuration.
Args:
config (dict, optional): Processing configuration.
Defaults to default configuration.
Raises:
ValueError: If configuration is invalid
"""
self.config = config or self._get_default_config()
self.logger = self._setup_logger()
self._validate_config()
# Universal Tutorial Template
## Tutorial: Getting Started with [Feature]
### Prerequisites
- Basic understanding of [related concept]
- [Software] version [version] installed
- Access to [required resources]
### Step 1: Setup
1. Install required dependencies
```bash
pip install required-package
```
2. Configure your environment
```bash
export ENV_VAR=value
```
### Step 2: Basic Usage
1. Import the module
```python
from package import Module
```
2. Create an instance
```python
processor = Module(config)
```
### Step 3: Advanced Features
1. Configure advanced options
```python
processor.configure_advanced(option=value)
```
2. Process data
```python
result = processor.process(data)
```
### Troubleshooting
- **Issue**: Common problem description
**Solution**: Step-by-step resolution
- **Issue**: Another common problem
**Solution**: Alternative approach
Applies to:
Create, style, export, and validate publication-quality scientific and technical figures. xy is the preferred default (matplotlib.pyplot-compatible for easy migration); matplotlib/seaborn/plotly also supported. Use when the user asks for a "publication figure", "paper figure", "journal-ready plot", "scientific visualization", "SVG/PDF/TIFF export", "colorblind-safe palette", "multi-panel layout", "error bars / significance markers", or works with figure scripts that import matplotlib, seaborn, plotly, or xy.
Guide for creating, validating, improving, and benchmarking skills. Use when users want to create a skill from scratch, edit or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Generate comprehensive database schemas with proper relations, migrations, and ORM/ODM models for PostgreSQL, MongoDB, and SQLite. Use when creating database schemas that integrate with FastAPI applications, including SQLAlchemy models for SQL databases, PyMongo/ODMantic models for MongoDB, Alembic migrations, and proper relationship definitions.