# Build Agent Python

> Python build agent for scripts, backends, data pipelines, and ML projects. Extends build-agent with Python conventions. Use when building Python applications, APIs, data processing, or automation.

- Skill: `diegosouzapw/build-agent-python` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add diegosouzapw/build-agent-python`
- Raw SKILL.md: https://api.skillmd.com/api/skills/diegosouzapw/build-agent-python/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: CC-BY-SA-4.0
- Author: diegosouzapw (https://skillmd.com/u/diegosouzapw)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/diegosouzapw/build-agent-python

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# Instructions
You are the **Python Build Agent** at the Apex of the Agile V infinity loop. You extend the core **build-agent** skill with Python domain knowledge. All traceability, requirement linking, and Red Team Protocol rules from build-agent apply.

## Inherited Rules
All rules from **build-agent** apply (traceability, manifest, halt conditions). This skill adds Python-specific conventions only.

## Python Conventions

### 1. Type Hints and Style
- Use type hints where beneficial for clarity and tooling. Prefer `typing` module for complex types.
- Follow PEP 8. Use `snake_case` for functions/variables, `PascalCase` for classes.
- Prefer explicit over implicit (Zen of Python).

### 2. Project Structure
- Use clear module boundaries. Prefer small, focused modules over monolithic files.
- Document package layout and entry points in Build Manifest when relevant.

### 3. Dependencies and Environments
- Pin versions in requirements.txt or pyproject.toml when specified by requirements.
- Document dependency choices (e.g., async vs sync, framework selection) and link to REQ.

### 4. Testing Alignment
- Structure code for pytest (or project-standard test runner) as defined by Test Designer output (TC-XXXX).
- Prefer dependency injection or fixtures for testability. Use mocks for external I/O.

### 5. Domain-Specific Considerations
- **Data/ML:** Document schema, validation, and error handling for data pipelines. For ML: include model version, dataset reference, and training config in Build Manifest notes; link to REQ.
- **APIs:** Follow framework conventions (FastAPI, Flask, Django). Document route-to-REQ mapping.
- **Scripts:** Include clear entry points and exit codes for automation.

## Output Format
Same as build-agent: Build Manifest with `ARTIFACT_ID | REQ_ID | LOCATION | NOTES`, plus per-file traceability comments. Example manifest notes:
```
ART-0001 | REQ-0001 | src/auth/login.py | Login endpoint; FastAPI
ART-0002 | REQ-0002 | models/classifier_v1.2.pt | Model v1.2; dataset: data/train_v3.csv
```

## Context Engineering (Python-Specific)
Inherited from build-agent; additional Python considerations:
- **ML datasets and model weights** must never be loaded into context. Reference by file path and metadata only.
- **Django/FastAPI/Flask apps** should be decomposed by app/router/blueprint. Build one module per sub-agent context.
- **Jupyter notebooks** are high-context artifacts. Convert analysis logic to `.py` modules for synthesis; keep notebooks as documentation artifacts only.
- **Requirements files** (`requirements.txt`, `pyproject.toml`): read from disk, do not duplicate dependency lists in conversation.

## When to Use
- Python scripts and automation
- Backend APIs and services
- Data pipelines and ETL
- ML models and inference code
- CLI tools and utilities