software-developer

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Original

English

🇨🇳

Translation

Chinese

Software Developer Skill

软件开发者技能

Purpose

用途

Implement production-quality bioinformatics software from technical specifications with comprehensive testing, documentation, and error handling.

根据技术规范实现具备全面测试、文档和错误处理的生产级生物信息学软件。

When to Use This Skill

何时使用该技能

Use this skill when you need to:

Implement software from architecture specification
Write production-ready code (not exploratory analysis)
Create command-line tools or packages
Build reusable libraries
Ensure code quality through testing

在以下场景中使用该技能：

根据架构规范实现软件
编写生产就绪代码（而非探索性分析代码）
创建命令行工具或软件包
构建可复用库
通过测试保障代码质量

Workflow Integration

工作流集成

Pattern: Receive Spec → Implement → Test → Document → Deliver

Systems Architect provides technical spec
    ↓
Software Developer implements
    ↓  (copilot reviews continuously)
Biologist Commentator validates biological correctness
    ↓
Production-ready software

模式：接收规范 → 实现 → 测试 → 文档编写 → 交付

系统架构师提供技术规范
    ↓
软件开发者实现
    ↓ （Copilot持续评审）
生物学家评论员验证生物学正确性
    ↓
生产就绪软件

Archival Compliance

归档合规性

Before writing any output file:

Check if archival context was provided via handoff from an orchestrator
- If yes: use the provided archival_context block directly
- If archival_context is "skip": bypass all compliance checks
If no handoff context: check for
```
.archive-metadata.yaml
```
in the repo root following the archival compliance check pattern: a. Read the reference document:
```
~/.claude/skills/archive-workflow/references/archival-compliance-check.md
```
b. If file not found, use graceful degradation (log warning, proceed without archival check) c. Apply the 5-step pattern to all file creation operations
Before writing output, validate path against guidelines
On violation: if invoked standalone, present advisory options; if invoked via Task tool (sub-agent), apply archival guidelines silently

software-developer specific: Focus on code naming conventions (snake_case for .py) and directory structure (src/, tests/) validation.

在编写任何输出文件之前：

检查是否通过编排器移交提供了归档上下文（archival_context）
- 如果是：直接使用提供的archival_context块
- 如果archival_context为"skip"：跳过所有合规性检查
如果没有移交上下文：检查仓库根目录下是否存在
```
.archive-metadata.yaml
```
，遵循归档合规性检查模式： a. 阅读参考文档：
```
~/.claude/skills/archive-workflow/references/archival-compliance-check.md
```
b. 如果文件未找到，使用优雅降级（记录警告，不进行归档检查继续执行） c. 对所有文件创建操作应用五步模式
在编写输出前，验证路径是否符合指南要求
若违反规范：如果是独立调用，展示建议选项；如果是通过Task工具（子Agent）调用，静默应用归档指南

软件开发者特定要求：重点关注代码命名规范（.py文件使用snake_case）和目录结构（src/、tests/）验证。

Core Capabilities

核心能力

1. Implementation from Spec

1. 基于规范实现

Translate architecture into working code
Modular, reusable functions/classes
Follow coding standards (PEP 8)
Type hints for clarity

将架构转化为可运行代码
模块化、可复用的函数/类
遵循编码标准（PEP 8）
类型提示以提升可读性

2. Error Handling

2. 错误处理

Try/except with informative messages
Validate inputs
Graceful failure
Logging for debugging

带信息性提示的Try/except处理
输入验证
优雅降级
用于调试的日志记录

3. Testing

3. 测试

Unit tests (pytest)
Integration tests
Edge case coverage
80% code coverage goal

单元测试（pytest）
集成测试
边缘场景覆盖
代码覆盖率目标>80%

4. Documentation

4. 文档编写

Docstrings (Google style)
README with usage examples
API reference
Troubleshooting guide

文档字符串（Google风格）
包含使用示例的README
API参考文档
故障排除指南

5. CLI Interface

5. CLI界面

argparse or Click
Help messages
Progress bars for long operations
Sensible defaults

argparse或Click
帮助信息
长操作进度条
合理的默认值

Standard Package Structure

标准软件包结构

Use

assets/package_structure_template/

project_name/
├── src/
│   ├── __init__.py
│   ├── module1.py
│   ├── module2.py
│   └── cli.py
├── tests/
│   ├── test_module1.py
│   ├── test_module2.py
│   ├── fixtures/
│   └── test_data/
├── docs/
│   ├── usage.md
│   └── api.md
├── README.md
├── setup.py
├── pyproject.toml
├── requirements.txt
├── environment.yml
└── .gitignore

使用

assets/package_structure_template/

：

project_name/
├── src/
│   ├── __init__.py
│   ├── module1.py
│   ├── module2.py
│   └── cli.py
├── tests/
│   ├── test_module1.py
│   ├── test_module2.py
│   ├── fixtures/
│   └── test_data/
├── docs/
│   ├── usage.md
│   └── api.md
├── README.md
├── setup.py
├── pyproject.toml
├── requirements.txt
├── environment.yml
└── .gitignore

Code Quality Standards

代码质量标准

Docstring Format (Google Style)

文档字符串格式（Google风格）

python

def calculate_cpm(counts: pd.DataFrame) -> pd.DataFrame:
    """
    Calculate counts per million (CPM) normalization.

    Parameters
    ----------
    counts : pd.DataFrame
        Raw count matrix (genes × samples)

    Returns
    -------
    pd.DataFrame
        CPM-normalized counts

    Raises
    ------
    ValueError
        If counts contain negative values

    Examples
    --------
    >>> counts = pd.DataFrame({'A': [10, 20], 'B': [30, 40]})
    >>> cpm = calculate_cpm(counts)
    >>> cpm['A'].sum()  # Should be ~1,000,000
    1000000.0
    """
    if (counts < 0).any().any():
        raise ValueError("Counts cannot be negative")

    return (counts / counts.sum(axis=0)) * 1e6

python

def calculate_cpm(counts: pd.DataFrame) -> pd.DataFrame:
    """
    计算每百万次计数（CPM）归一化。

    参数
    ----------
    counts : pd.DataFrame
        原始计数矩阵（基因 × 样本）

    返回值
    -------
    pd.DataFrame
        CPM归一化后的计数

    异常
    ------
    ValueError
        如果计数包含负值

    示例
    --------
    >>> counts = pd.DataFrame({'A': [10, 20], 'B': [30, 40]})
    >>> cpm = calculate_cpm(counts)
    >>> cpm['A'].sum()  # 应约为1,000,000
    1000000.0
    """
    if (counts < 0).any().any():
        raise ValueError("Counts cannot be negative")

    return (counts / counts.sum(axis=0)) * 1e6

Error Handling

错误处理

python

undefined

python

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✅ Good: Informative error messages

✅ 良好示例：信息丰富的错误提示

try: data = pd.read_csv(filepath) except FileNotFoundError: raise FileNotFoundError( f"Data file not found: {filepath}\n" f"Expected location: {Path(filepath).absolute()}" ) except pd.errors.EmptyDataError: raise ValueError( f"Data file is empty: {filepath}\n" f"Check that file was generated correctly" )

undefined

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Logging

日志记录

python

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def process_samples(sample_list):
    logger.info(f"Processing {len(sample_list)} samples")
    for i, sample in enumerate(sample_list):
        logger.debug(f"Processing sample {i+1}/{len(sample_list)}: {sample}")
        # ... processing code ...
    logger.info("Processing complete")

python

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def process_samples(sample_list):
    logger.info(f"Processing {len(sample_list)} samples")
    for i, sample in enumerate(sample_list):
        logger.debug(f"Processing sample {i+1}/{len(sample_list)}: {sample}")
        # ... 处理代码 ...
    logger.info("Processing complete")

Testing with pytest

使用pytest进行测试

python

undefined

python

undefined

tests/test_normalization.py

import pytest import pandas as pd import numpy as np from src.normalization import calculate_cpm

def test_cpm_sum_equals_million(): """Test that CPM normalization sums to ~1 million.""" counts = pd.DataFrame({'A': [10, 20, 30], 'B': [40, 50, 60]}) cpm = calculate_cpm(counts) assert np.allclose(cpm.sum(axis=0), 1e6)

def test_cpm_raises_on_negative(): """Test that negative counts raise ValueError.""" counts = pd.DataFrame({'A': [-10, 20], 'B': [30, 40]}) with pytest.raises(ValueError, match="negative"): calculate_cpm(counts)

def test_cpm_handles_zero_sum(): """Test behavior when column sums to zero.""" counts = pd.DataFrame({'A': [0, 0], 'B': [10, 20]}) # Should handle gracefully (decide behavior: NaN or raise)

undefined

import pytest import pandas as pd import numpy as np from src.normalization import calculate_cpm

def test_cpm_sum_equals_million(): """测试CPM归一化的总和是否约为100万。""" counts = pd.DataFrame({'A': [10, 20, 30], 'B': [40, 50, 60]}) cpm = calculate_cpm(counts) assert np.allclose(cpm.sum(axis=0), 1e6)

def test_cpm_raises_on_negative(): """测试负值计数是否会触发ValueError。""" counts = pd.DataFrame({'A': [-10, 20], 'B': [30, 40]}) with pytest.raises(ValueError, match="negative"): calculate_cpm(counts)

def test_cpm_handles_zero_sum(): """测试列总和为零时的行为。""" counts = pd.DataFrame({'A': [0, 0], 'B': [10, 20]}) # 应优雅处理（确定行为：返回NaN或触发异常）

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CLI Template

CLI模板

See

assets/cli_template.py

python

#!/usr/bin/env python3
"""
QC Pipeline CLI

Usage:
    qc_pipeline samples.csv --output results/
"""

import click
import logging
from pathlib import Path

@click.command()
@click.argument('sample_file', type=click.Path(exists=True))
@click.option('--output', '-o', default='results/', help='Output directory')
@click.option('--threads', '-t', default=4, help='Number of threads')
@click.option('--verbose', '-v', is_flag=True, help='Verbose logging')
def main(sample_file, output, threads, verbose):
    """Run QC pipeline on samples."""

    # Setup logging
    level = logging.DEBUG if verbose else logging.INFO
    logging.basicConfig(level=level)
    logger = logging.getLogger(__name__)

    # Validate inputs
    output_dir = Path(output)
    output_dir.mkdir(parents=True, exist_ok=True)

    logger.info(f"Processing samples from {sample_file}")
    logger.info(f"Output directory: {output_dir}")
    logger.info(f"Using {threads} threads")

    # Main logic
    try:
        # ... pipeline code ...
        logger.info("Pipeline complete!")
    except Exception as e:
        logger.error(f"Pipeline failed: {e}")
        raise

if __name__ == '__main__':
    main()

查看

assets/cli_template.py

：

python

#!/usr/bin/env python3
"""
QC流程CLI

用法：
    qc_pipeline samples.csv --output results/
"""

import click
import logging
from pathlib import Path

@click.command()
@click.argument('sample_file', type=click.Path(exists=True))
@click.option('--output', '-o', default='results/', help='输出目录')
@click.option('--threads', '-t', default=4, help='线程数')
@click.option('--verbose', '-v', is_flag=True, help='详细日志')
def main(sample_file, output, threads, verbose):
    """对样本运行QC流程。"""

    # 配置日志
    level = logging.DEBUG if verbose else logging.INFO
    logging.basicConfig(level=level)
    logger = logging.getLogger(__name__)

    # 验证输入
    output_dir = Path(output)
    output_dir.mkdir(parents=True, exist_ok=True)

    logger.info(f"Processing samples from {sample_file}")
    logger.info(f"Output directory: {output_dir}")
    logger.info(f"Using {threads} threads")

    # 主逻辑
    try:
        # ... 流程代码 ...
        logger.info("Pipeline complete!")
    except Exception as e:
        logger.error(f"Pipeline failed: {e}")
        raise

if __name__ == '__main__':
    main()

Testing Strategy

测试策略

1. Unit Tests

1. 单元测试

Test individual functions in isolation.

独立测试单个函数。

2. Integration Tests

2. 集成测试

Test components working together.

测试组件协同工作。

3. Regression Tests

3. 回归测试

Save expected outputs, compare to current.

保存预期输出，与当前结果对比。

4. Edge Case Tests

4. 边缘场景测试

Empty input
Single element
All zeros
Missing values
Very large input

空输入
单个元素
全零值
缺失值
超大输入

Copilot Integration

Copilot集成

During implementation:

Write code section
Copilot reviews immediately
Fix critical issues before proceeding
Iterate until approved
Move to next section

实现过程中：

编写代码段
Copilot立即评审
修复关键问题后再继续
迭代直至通过审核
进入下一部分

Quality Checklist

质量检查清单

References

参考资料

For detailed standards:

```
references/coding_standards.md
```
- PEP 8, naming, function length
```
references/testing_patterns.md
```
- pytest, fixtures, mocking
```
references/error_handling_guide.md
```
- Exception hierarchy, logging
```
references/documentation_standards.md
```
- Docstrings, README, API docs

详细标准请参考：

```
references/coding_standards.md
```
- PEP 8、命名规范、函数长度
```
references/testing_patterns.md
```
- pytest、fixtures、模拟
```
references/error_handling_guide.md
```
- 异常层级、日志记录
```
references/documentation_standards.md
```
- 文档字符串、README、API文档

Scripts

脚本

Available in

scripts/

```
project_template_generator.py
```
- Creates project structure
```
test_runner.py
```
- Runs pytest with coverage

scripts/

目录下提供：

```
project_template_generator.py
```
- 创建项目结构
```
test_runner.py
```
- 运行pytest并生成覆盖率报告

software-developer

Original

Translation

Software Developer Skill

软件开发者技能

Purpose

用途

When to Use This Skill

何时使用该技能

Workflow Integration

工作流集成

Archival Compliance

归档合规性

Core Capabilities

核心能力

1. Implementation from Spec

1. 基于规范实现

2. Error Handling

2. 错误处理

3. Testing

3. 测试

4. Documentation

4. 文档编写

5. CLI Interface

5. CLI界面

Standard Package Structure

标准软件包结构

Code Quality Standards

代码质量标准

Docstring Format (Google Style)

文档字符串格式（Google风格）

Error Handling

错误处理

✅ Good: Informative error messages

✅ 良好示例：信息丰富的错误提示

Logging

日志记录

Testing with pytest

使用pytest进行测试

tests/test_normalization.py

tests/test_normalization.py

CLI Template

CLI模板

Testing Strategy

测试策略

1. Unit Tests

1. 单元测试

2. Integration Tests

2. 集成测试

3. Regression Tests

3. 回归测试

4. Edge Case Tests

4. 边缘场景测试

Copilot Integration

Copilot集成

Quality Checklist

质量检查清单

References

参考资料

Scripts

脚本

Success Criteria

成功标准