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do-app-platform-skills

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About do-app-platform-skills

Claude/Agent Skills for DigitalOcean App Platform - deployment, migration, networking, database configuration, and troubleshooting

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Web Self-hosted

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Python

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README.md

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DigitalOcean App Platform Skills

DigitalOcean App Platform Skills is an open-source, production-focused skills repository for AI coding assistants that helps engineers design, migrate, deploy, and operate applications on DigitalOcean App Platform. It is for platform engineers, backend/full-stack developers, DevOps teams, and AI-agent workflow builders who want repeatable App Platform outcomes using structured skill playbooks.

Overview

This repository provides modular “skills” that combine concise routing guidance (SKILL.md), deeper reference material, templates, and optional scripts for specific App Platform domains.

At a high level, the project enables a workflow where an AI assistant can:

  1. Interpret an engineering goal (for example: “migrate my Heroku app to DigitalOcean”).
  2. Load the most relevant skill(s).
  3. Generate or refine concrete artifacts (for example: .do/app.yaml, workflow definitions, migration checklists, SQL scripts, troubleshooting reports).
  4. Follow opinionated, secure defaults aligned with DigitalOcean App Platform practices.

Repository Architecture

  • skills/ contains domain-specific skills (designer, deployment, migration, networking, postgres, troubleshooting, and more).
  • shared/ contains cross-cutting references and canonical schema/config artifacts (including skill-schema.json, regions/instance-size defaults, and platform patterns).
  • architecture/Docs/ contains project-level architecture, governance, quality strategy, and operational guidance.
  • scripts/ contains validation and analytics tooling for repository quality and usage analysis.
  • tests/ contains comprehensive Python test suites for workflows, validation logic, edge cases, and skill-specific behavior.

Documentation Map

Getting Started

Prerequisites

Before using or contributing to this repository, ensure the following are available:

  • Operating system: macOS, Linux, or Windows with a POSIX-compatible shell environment recommended.
  • Python: 3.11+ (recommended 3.12) for local scripts and test tooling.
  • DigitalOcean CLI (doctl): 1.82.0+ (recommended 1.100.0+) for App Spec v2 compatibility and platform operations.
  • Node.js: 20+ (recommended 22 LTS) when working with Node-based target applications in examples/workflows.
  • Git: latest stable for repository workflows.
  • DigitalOcean account + API token: required for authenticated platform operations and deployment testing.
  • Optional tooling: gh (GitHub CLI), Docker, and PostgreSQL client utilities for deeper local validation workflows.

Recommended version check commands:

python3 --version
doctl version
node --version
git --version

Installation

Clone the repository and set up a local development environment:

# 1) Clone repository
git clone https://github.com/digitalocean-labs/do-app-platform-skills.git
cd do-app-platform-skills

# 2) Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# 3) Install development dependencies
pip install -r requirements-dev.txt

# 4) Authenticate doctl (required for platform-integrated workflows)
doctl auth init
# Verify it works
doctl account get

# 5) Run baseline validation/tests
python scripts/validate_skills.py
make lint
pytest -q

Install as an assistant skill source (example paths):

# Claude Code
mkdir -p ~/.claude/skills
ln -s "$PWD" ~/.claude/skills/do-app-platform-skills

# Codex
mkdir -p ~/.codex/skills
ln -s "$PWD" ~/.codex/skills/do-app-platform-skills

# Cursor
mkdir -p ~/.cursor/skills
ln -s "$PWD" ~/.cursor/skills/do-app-platform-skills

Quick Start

Minimal workflow to begin using this project in an AI-assisted session:

  1. Open your application repository in your AI coding assistant.
  2. Ensure this repository is available as a skill source (cloned or linked).
  3. Use a focused instruction that references App Platform skills explicitly.

Example prompt:

Create a production-ready App Platform spec for my Python FastAPI API with managed PostgreSQL.
Use the DigitalOcean App Platform skills and output .do/app.yaml with secure defaults.

Expected output from a typical first run:

  • Initial .do/app.yaml scaffold suitable for App Platform.
  • Service/database wiring guidance.
  • Follow-up recommendations for CI/CD and troubleshooting skill handoff.

Usage

Use the repository by selecting the skill matching your current objective and chaining skills as work matures from design to operations.

Typical Skill Selection Flow

  • Design / first App Spec: start with skills/designer/
  • Migration from other platforms: use skills/migration/
  • CI/CD and release workflows: use skills/deployment/
  • Network, domains, CORS, VPC: use skills/networking/
  • PostgreSQL and data-layer concerns: use skills/postgres/ and skills/managed-db-services/
  • Production diagnosis and incident response: use skills/troubleshooting/

Common Usage Patterns

  • Greenfield: Designer → Deployment → Troubleshooting
  • Lift-and-shift migration: Migration → Designer refinements → Deployment
  • Database-centric modernization: Postgres/Managed DB → Designer integration → Deployment

Validation and Quality Commands

# Validate repository skill content and structure
python scripts/validate_skills.py

# Lint YAML configuration and shared metadata
make lint

# Run all tests
pytest -q

# Run targeted suites (examples)
pytest tests/test_migration -q
pytest tests/test_validation -q

For deeper system design and operating guidance, see:

Roadmap / Status

Status: Active development and production-oriented hardening.

Current maturity characteristics:

  • Broad skill coverage across design, migration, deployment, networking, database, and troubleshooting domains.
  • Established validation and test infrastructure with targeted edge-case coverage.
  • Ongoing improvements to skill quality, reference depth, and workflow robustness.

Near-term roadmap themes:

  • Expand and refine high-value migration/deployment templates.
  • Strengthen schema and rule validation for safer agent output.
  • Improve analytics-driven iteration on skill usefulness and discoverability.
  • Continue quality and governance alignment for open-source collaboration at scale.

Security

See SECURITY.md for how to report vulnerabilities.

Contributing

We welcome community contributions! Please see CONTRIBUTING.md.

Please also review CODE_OF_CONDUCT.md.

Additional contributor references:

License

This project is licensed under the MIT License. See the LICENSE file.

Trademarks

DigitalOcean and related marks are trademarks of DigitalOcean.