Superpowers Python Automation

Python Automation Skill

This skill provides concrete Python patterns to implement robust REST API automations.

When to use this skill

• Python scripts that call one or more REST APIs
• ETL jobs, sync tools, webhook handlers
• CLI tools or small services that integrate external systems

Preferred stack (defaults)

• HTTP client: httpx (preferred) or requests
• Config: env vars + .env (optional) with pydantic-settings if appropriate
• Logging: stdlib logging with structured-ish fields
• Testing: pytest (+ respx for httpx mocking when useful)

If the project already uses different tools, follow project conventions.

Reference architecture (small but scalable)

• client.py: API client wrapper (auth headers, retries, pagination helpers)
• models.py: typed payload models (dataclasses or pydantic)
• sync.py: orchestration logic (fetch -> transform -> upsert)
• main.py: CLI entrypoint
• tests/: unit tests for transform + client behavior

HTTP rules (mandatory)

• Always set timeouts (connect + read)
• Centralize request sending in one function so retries/logging are consistent
• Never log secrets (Authorization headers, tokens)

Retry policy guidance

Retry on:

• network errors/timeouts
• 429 (respect Retry-After when present)
• 500–599 Optional: 408, and 409 only if operation is safe and semantics known

Do NOT retry on:

• most 400–499 (unless explicitly safe)

Timeouts

• Set explicit timeouts; do not rely on defaults.
• Use smaller connect timeout; moderate read timeout.

Pagination patterns

Support at least one helper that can handle:

• next URL in response
• cursor token in response
• page/limit parameters

Add a hard stop:

• max pages OR max items OR max elapsed time

Idempotency patterns (Python)

Choose and document:

• Use an Idempotency-Key header when supported
• Upsert using a stable external_id
• Persist a lightweight state store:
  • simplest: SQLite file (recommended for OSS)
  • alternative: JSONL log + compaction

Minimum: ensure repeated runs don’t create duplicates.

Observability (Python)

Minimum logs should include:

• run_id
• request: method, url/path, status_code, elapsed_ms, attempt
• record counts: processed/created/updated/skipped/failed

Also include a final summary log line.

Verification requirements

For non-trivial work, add:

• unit tests for mapping/transform logic
• at least one test for pagination or retry behavior (mocked)
• a “dry-run” CLI flag (prints intended writes)

Output format when writing code

• Provide a small directory layout
• Explain how to configure env vars
• Include exact commands to run (and test)