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py-micro-superpowers — Superpowers for Python microservices

py-micro-superpowers

Superpowers for Python microservices.

Version 4.0.0 25 agents 17 commands 21 skills MIT license

A Claude Code plugin that gives Claude deep knowledge of the Python ecosystem — Django, FastAPI, pytest, Pydantic, Celery, PostgreSQL, PostGIS — and first-class support for multi-service monorepos. Built on Anthropic's superpowers plugin: superpowers owns the engineering process (brainstorm → plan → execute → review), this plugin supplies the Python domain expertise and microservice orchestration.

Formerly compound-engineering-feat-python. Originally forked from Compound Engineering Plugin by Kieran Klaassen, fully rewritten for the Python ecosystem.

What It Does

/workflows:brainstorm → /workflows:plan → /workflows:work → /workflows:review

Each step loads relevant skills (Django patterns, pytest conventions, PostgreSQL optimization…), spawns specialized review agents, and enforces quality gates — tests must pass, linting must be clean, pre-commit hooks must succeed — before anything ships.

flowchart LR
    B["/workflows:brainstorm"] -->|spec| P["/workflows:plan"]
    P -->|plan| W["/workflows:work"]
    W -->|branch + commits| R["/workflows:review"]
    R -->|todos| W
    S[superpowers plugin<br/>process primitives] -.-> B & P & W & R
    K[Python skills +<br/>review agents] -.-> B & P & W & R
Loading

For monorepos, every command detects your services (*/pyproject.toml), scopes work to the ones you pick, executes plans in dependency order, and propagates a <focus_context> block to every sub-agent so nested delegation never loses track of which service it is working on.

Installation

Requires the superpowers plugin (process primitives):

/plugin marketplace add anthropics/claude-plugins-official
/plugin install superpowers@claude-plugins-official

Then add this repo as a marketplace and install:

/plugin marketplace add weorbitant/compound-engineering-feat-python-plugin
/plugin install py-micro-superpowers@py-micro-superpowers

To install from a local clone, use /plugin marketplace add /path/to/checkout instead.

Optional Dependencies

Checked automatically on session start — you'll see warnings for anything missing:

Tool Used By Install
agent-browser /test-browser, /feature-video, design agents, bug reproduction npm install -g agent-browser && agent-browser install
rclone /feature-video (cloud uploads) rclone.org/install

Quick Start

# Brainstorm a feature idea
/workflows:brainstorm add rate limiting to the payment API

# Plan the implementation
/workflows:plan add rate limiting with Redis sliding window

# Execute the plan
/workflows:work docs/superpowers/plans/2026-04-01-rate-limiting.md

# Review the changes
/workflows:review

Brainstorm specs land at docs/superpowers/specs/; plans at docs/superpowers/plans/.

Core Workflow Commands

Commands use the workflows: prefix to avoid collisions with Claude Code built-ins (/plan, /review).

Command Purpose
/workflows:brainstorm Explore requirements before planning (delegates to superpowers:brainstorming)
/workflows:plan Generate implementation plans (delegates to superpowers:writing-plans)
/workflows:work Execute a plan with Python quality gates (delegates to superpowers:subagent-driven-development / superpowers:executing-plans)
/workflows:review Multi-agent Python code review (delegates to superpowers:requesting-code-review)

Each workflow command:

  • Loads project-specific skills from the py-micro-superpowers.local.md configuration
  • Auto-detects multi-service monorepos and adjusts behavior accordingly
  • Propagates service context to all sub-agents to prevent focus loss

Utility Commands

Command Description
/deepen-plan Enrich a plan with parallel research agents for each section
/changelog Generate changelogs from recent merges
/create-agent-skill Guided skill creation
/generate_command Generate new slash commands
/reproduce-bug Reproduce bugs using logs and browser automation
/resolve_parallel Resolve TODO/FIXME comments in parallel
/resolve_todo_parallel Resolve todos from plan files in parallel
/triage Triage and prioritize issues
/test-browser Browser tests on PR-affected pages
/agent-native-audit Architecture review against 8 agent-native principles
/feature-video Record video walkthroughs and attach to PR
/report-bug Report a plugin bug
/heal-skill Fix skill documentation issues

Agents (25)

Agents run as sub-processes during workflows, grouped by specialization. A 3-tier model strategy keeps cost proportional to stakes:

  • Opus — high-stakes judgment: architecture, security, migrations, data integrity, performance
  • Sonnet — domain review and research: framework reviewers, researchers, design agents
  • Haiku — mechanical tasks: linting and type-checking

Reviewers work from baked-in checklists with PASS/FAIL code examples, backed by the plugin's skills for deep reference. When a finding hinges on version-specific library behavior they aren't certain of, they verify it against live docs through the bundled context7 MCP server rather than asserting from memory.

Review

Agent Model Focus
python-quality-reviewer sonnet PEP 8 style, modern syntax, type annotations, Pydantic v2
django-reviewer sonnet Models, views, URLs, admin, signals, middleware, Ninja
django-drf-reviewer sonnet DRF serializers, viewsets, permissions, pagination
django-celery-reviewer sonnet Task design, idempotency, retry strategy, queue routing
fastapi-reviewer sonnet Routes, DI, async correctness, OpenAPI + SQLAlchemy data layer, Alembic
pytest-reviewer sonnet Fixtures, isolation, assertions, mocking, async
postgresql-reviewer sonnet Query optimization, indexing, connection pooling
postgis-reviewer sonnet SRID consistency, spatial indexing, GeoDjango
migration-reviewer opus Migration safety, data-migration validation, schema drift (Django + Alembic)
architecture-strategist opus Architectural integrity, patterns, anti-patterns, duplication
data-integrity-guardian opus Data models, constraints, transactions, privacy
deployment-verification-agent opus Go/No-Go deployment checklists
performance-oracle opus Algorithmic complexity, queries, async runtime, memory
security-sentinel opus Security audits and OWASP compliance
agent-native-reviewer sonnet Action + context parity for agent-native features
code-simplicity-reviewer sonnet Final pass for simplicity and minimalism

Research

Agent Model Focus
best-practices-researcher sonnet Official docs, version constraints, community best practices
git-history-analyzer sonnet Git history and code evolution
repo-research-analyst sonnet Repository structure and conventions

Design

Agent Model Focus
design-implementation-reviewer sonnet UI vs Figma comparison, with iterative fix mode
design-iterator sonnet Iterative UI refinement

Workflow

Agent Model Focus
bug-reproduction-validator sonnet Systematic bug reproduction
python-lint haiku ruff + mypy linting and type-checking
pr-comment-resolver sonnet PR comment resolution
spec-flow-analyzer opus User flow gap analysis

Skills (21)

Skills provide domain knowledge that agents and workflows load on demand — reference documentation, conventions, and patterns. Process primitives (brainstorming, planning, TDD, git worktrees, parallel agents) come from the superpowers plugin.

Python Core

  • python-style — PEP 8, naming, imports, logging with emoji prefixes
  • python-typing — Type hints, Pydantic v2, Protocol, generics

Django

  • django-patterns — Models, views, admin, signals, middleware, Django Ninja
  • django-drf — DRF serializers, viewsets, permissions, authentication
  • django-async — Celery task patterns, Django Channels

FastAPI

  • fastapi-patterns — Routes, dependency injection, async patterns, auth
  • fastapi-sqlalchemy — SQLAlchemy 2.0 async, Alembic migrations

Testing

  • pytest-patterns — Fixtures, parametrize, mocking, async testing

Database

  • postgresql-patterns — Schema design, indexing, query optimization, monitoring
  • postgis-patterns — Spatial queries, GeoDjango, GeoAlchemy2

Architecture & Design

  • agent-native-architecture — Agent-native app patterns (action parity, context injection, shared workspace)
  • frontend-design — Production-grade frontend interfaces

Microservice Support

  • microservice-focus — Service discovery, --services parsing, multi-service prompting, <focus_context> construction

Development Tools

  • setup — Interactive project configuration for review agents and workflow skills

Content & Workflow

  • commit — Conventional commits with gitmoji, logical grouping, pre-commit handling
  • document-review — Structured document improvement
  • file-todos — File-based todo tracking
  • resolve-pr-parallel — Parallel PR comment resolution

Integrations

  • rclone — Cloud storage uploads (S3, R2, B2)
  • agent-browser — CLI browser automation via Vercel's agent-browser
  • gemini-imagegen — Image generation/editing with Google Gemini API

MCP Servers

Server Description Tools
context7 Framework documentation lookup resolve-library-id, query-docs

Supports 100+ frameworks including Django, FastAPI, SQLAlchemy, Celery, and more. Starts automatically when the plugin is enabled. If it doesn't, add it manually to .claude/settings.json:

{
  "mcpServers": {
    "context7": { "type": "http", "url": "https://mcp.context7.com/mcp" }
  }
}

Configuration

Workflow Skills

Customize which skills load per workflow via py-micro-superpowers.local.md in your project root (a legacy compound-engineering.local.md is still honored):

---
workflow_skills:
  brainstorm: [django-patterns, my-company-domain]
  plan: [django-patterns]
  work: [django-patterns, pytest-patterns]
  review: [django-patterns, pytest-patterns]
---

Skills are additive — loaded alongside each command's built-in skills. Project-local skills in .claude/skills/ can also be referenced by name.

Review Agents

Run /setup — it auto-detects your stack (Django, FastAPI, DRF, Celery, SQLAlchemy, PostgreSQL, PostGIS, Pydantic) and writes the review_agents roster used by /workflows:review into py-micro-superpowers.local.md.

Microservice Support

Focus Gate logic lives in the microservice-focus skill, invoked once by each workflow command:

  • Auto-detection — discovers services via */pyproject.toml at root level
  • Focus Gate — each workflow command prompts for relevant services (or accepts --services)
  • Dependency-aware execution — plans with services: YAML frontmatter execute in topological order (wavefront pattern)
  • Per-service context — loads each service's README, dependencies, and structure before acting
  • Stack-aware routing — Django patterns for Django services, FastAPI for FastAPI, per-service agent rosters

Pipeline Mode

All workflow commands accept --services for automated invocation (skips all interactive prompts):

/workflows:brainstorm --services order-service,payment-service "add cross-service event bus"

Documentation Output

Doc Type Single Service Multi-Service
Specs docs/superpowers/specs/ {service}/docs/superpowers/specs/ per service
Plans docs/superpowers/plans/ Root overview + per-service plan files under {service}/docs/superpowers/plans/

How It Works

/workflows:work is the engine that turns plans into shipped code:

  1. Load context — project workflow skills + microservice focus gate (execution order for multi-service plans)
  2. Delegate executionsuperpowers:subagent-driven-development runs the task loop with per-task commits and reviews
  3. Python quality gate (mandatory) — full pytest suite + ruff + mypy + pre-commit hooks must all pass; loops until clean
  4. Ship — commit via the commit skill, push, open a PR that includes a Post-Deploy Monitoring & Validation plan

Never ships with failing tests, never skips hooks.

Development

scripts/validate.sh runs the structural checks CI enforces on every push: frontmatter integrity, reference-link resolution, script compilation, stale-reference guards, and component-count consistency.

Version History

See CHANGELOG.md for detailed version history.

Current version: 4.0.0 — renamed from compound-engineering-feat-python; the repository URL is unchanged.

Contributing

Contributions are welcome. The plugin is open source under the MIT license.

  1. Fork the repo and create a feature branch
  2. Follow the conventions encoded in the plugin's own skills (PEP 8, type hints, pytest, conventional commits)
  3. Run ./scripts/validate.sh and the plugin against itself (/workflows:review) before opening a PR
  4. Open a PR against main — include a short rationale and any relevant plan/spec docs

Issues, ideas, and discussion: GitHub Issues.

License

MIT — See LICENSE

Original work copyright (c) 2025 Kieran Klaassen. Modifications copyright (c) 2026 BaronVonSario.

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