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.
Best use case
build-agent-python is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using build-agent-python should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/build-agent-python/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How build-agent-python Compares
| Feature / Agent | build-agent-python | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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