agentleFS
theyoungastronautstheyoungastronauts/polaris/skills/verify-django/SKILL.md

verify-django

Skill4 starsChanged 3 months agoMITView on GitHub

Django/DRF verification checklist — models, serializers, views, security, API contract.

  • Reads credentials

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run through each section. Flag issues as PASS, WARN, or FAIL.

    line 14

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Skill: Verify Django

Purpose

Systematic verification checklist for Django/DRF code. Used by the reviewer agent in a separate Claude session after code execution.

Verification Process

Run through each section. Flag issues as PASS, WARN, or FAIL.

1. Tests

  • Tests exist for new/changed code
  • pytest passes with no failures
  • Test coverage is reasonable (not just happy path)
  • Factory data is realistic (not "test123" placeholder values)

2. Models

  • Migrations are generated and match model changes
  • No migration conflicts with main branch
  • New fields have sensible defaults or are nullable where appropriate
  • Indexes exist on fields used in filters/lookups
  • __str__ methods are defined

3. Serializers

  • Fields are explicitly listed (no __all__)
  • Read vs write serializers separated where needed
  • Validation covers edge cases (empty strings, nulls, boundaries)
  • Nested serializer performance — no N+1 from nested reads

4. Views

  • permission_classes set on every view
  • Querysets use select_related/prefetch_related appropriately
  • Pagination is configured for list endpoints
  • Error responses are consistent and informative
  • No business logic in views — delegated to models/services

5. Security

  • Authentication required on all non-public endpoints
  • Object-level permissions checked (not just role-based)
  • SECRET_KEY is set from the environment and not the shipped default (django-insecure-change-me-in-production) in production
  • No sensitive data in response bodies that shouldn't be there
  • Input validation prevents injection (Django handles most, but check raw SQL)

6. API Contract

  • Endpoint paths follow project conventions
  • Response shapes are consistent with existing endpoints
  • Integration summary is up to date
  • Breaking changes are documented

7. Code Quality

  • No commented-out code
  • No debug prints or leftover logging
  • Imports are clean (no unused)
  • Linting passes (ruff/flake8)
  • Type hints on function signatures

8. Development Environment

  • Dockerfile.dev exists
  • docker-compose.yml has the services for the project's blueprint tier — minimal: web + db; standard: + redis; full: + celery + celery-beat (check which tier the project was bootstrapped with)
  • Makefile exists and wraps all commands via docker compose exec
  • No python manage.py commands used directly on host

Output

Produce a verification-report.md:

# Verification Report: [Feature/Phase]
Date: [date]
Reviewer: Claude (automated)

## Summary: [PASS/WARN/FAIL]

## Details
### Tests: [PASS/WARN/FAIL]
- [notes]

### Models: [PASS/WARN/FAIL]
- [notes]

...

## Issues Found
1. [FAIL] Description + suggested fix
2. [WARN] Description + recommendation

## Recommended Actions
- [ ] Fix: ...
- [ ] Consider: ...

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.