python-backend-pack
Curated bundle of essential Python backend development skills. Includes FastAPI patterns, packaging, database migrations, CLI design, and Redis caching. Use when building Python API services or backend systems.
Best use case
python-backend-pack is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Curated bundle of essential Python backend development skills. Includes FastAPI patterns, packaging, database migrations, CLI design, and Redis caching. Use when building Python API services or backend systems.
Teams using python-backend-pack 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/python-backend-pack/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-backend-pack Compares
| Feature / Agent | python-backend-pack | 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?
Curated bundle of essential Python backend development skills. Includes FastAPI patterns, packaging, database migrations, CLI design, and Redis caching. Use when building Python API services or backend systems.
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
# Python Backend Pack A curated bundle of essential skills for building Python backend services — from project structure through API, database, caching, and CLI. ## What's Included - **fastapi-patterns** — Production FastAPI applications with async patterns, dependency injection, and Pydantic models - **python-packaging-patterns** — Project structure, pyproject.toml, dependency management, and distribution - **database-migration-patterns** — Safe schema evolution with Alembic, zero-downtime strategies, and rollback - **cli-tool-design** — Command-line interfaces with Typer/Click, subcommands, and shell completion - **redis-patterns** — Caching, pub/sub messaging, rate limiting, and distributed locks ## Getting Started Install this bundle when starting a Python backend project. The recommended sequence: 1. **Structure:** Use python-packaging-patterns to set up pyproject.toml and project layout 2. **API:** Use fastapi-patterns for async API endpoints with Pydantic validation 3. **Database:** Use database-migration-patterns for schema management with Alembic 4. **Caching:** Use redis-patterns for caching and rate limiting layers 5. **CLI:** Use cli-tool-design for management commands and operational tooling This pack covers the core Python backend stack used across ORGAN-III (commerce), ORGAN-IV (orchestration), and META-ORGANVM.
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taxonomy-modeling-design
Phase 2 of the pentaphase structural-overhaul protocol. Classifies entities, standardizes attributes, establishes relationships, and designs the access framework. Use when the user invokes phase 2 of an overhaul, asks to "design the taxonomy" or "model the structure", or has completed a landscape audit and is ready to redesign. Consumes phase-1-landscape-report.md; produces phase-2-taxonomy-model.md.
systemic-ingestion-normalization
Phase 4 of the pentaphase structural-overhaul protocol. Purges redundancies, enriches and aligns legacy entities to the new schema, executes phased ingestion into the new environment, and audits integrity. Use when the user invokes phase 4 of an overhaul, asks to "migrate the data" or "ingest into the new system", or has a configured environment ready to accept legacy entities. Consumes phase-3-environment-spec.md; produces phase-4-ingestion-report.md.
system-environment-configuration
Phase 3 of the pentaphase structural-overhaul protocol. Translates the taxonomy model into objective technical criteria, evaluates candidate mechanisms or frameworks, instantiates the chosen architecture, and programs validation rules. Use when the user invokes phase 3 of an overhaul, asks to "select a system" or "configure the environment", or has a taxonomy model and is ready to choose technology. Consumes phase-2-taxonomy-model.md; produces phase-3-environment-spec.md.
pentaphase-orchestrator
Threads the full five-phase structural-overhaul protocol — landscape discovery, taxonomy design, environment configuration, systemic ingestion, governance evolution — for any substrate the user names. Use when the user requests a structural overhaul, system redesign, or end-to-end restructuring of a documentation system, asset registry, code monorepo, knowledge base, or operational workflow; or when they explicitly invoke the pentaphase methodology. Coordinates handoffs between phase-skills and seats validation gates between phases.
landscape-discovery-audit
Phase 1 of the pentaphase structural-overhaul protocol. Inventories assets, maps current flow, identifies friction, and defines value metrics for any substrate. Use when the user invokes phase 1 of an overhaul, requests a baseline audit, asks to "discover the landscape" of a system, or wants to understand current state before redesigning. Produces phase-1-landscape-report.md.