Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/dvf/opinionated-djangonpx agentmods add skills/dvf/opinionated-django/dj-architectureWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dvf/opinionated-django/dj-architecture)<a href="https://agentmods.dev/skills/dvf/opinionated-django/dj-architecture"><img src="https://agentmods.dev/badge/skills/dvf/opinionated-django/dj-architecture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dvf/opinionated-django/dj-architecture"><img src="https://agentmods.dev/badge/skills/dvf/opinionated-django/dj-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.03872 |
| Opus 5 | $0.00039 | $0.01936 |
| Sonnet 5 | $0.00016 | $0.00774 |
| Haiku 4.5 | $0.00008 | $0.00387 |
Grade A, and why
dj-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement a Django Feature
You are implementing a feature in an opinionated, fully type-safe Django project managed with uv. Every convention below is mandatory. Do not deviate.
Why this architecture exists: Django's ORM is powerful but hard to type — querysets, model instances, related managers, and F()/Q() expressions don't play well with static type checkers. This project solves that by pushing all ORM usage into repositories that return Pydantic DTOs. Services receive repos via constructor injection and contain pure business logic with zero ORM imports. Views are thin dispatchers. The result: everything from the repository boundary outward is fully typed, IDE-friendly, and testable in isolation.
Tooling: uv is the package manager. All commands use uv run. Never use pip, poetry, or raw python — always uv run python, uv run pytest, etc. To add a dependency: uv add <package>.
BEFORE WRITING CODE
Gather current project state by reading:
src/project/ids.py— existing ID prefixes (must be unique)src/project/services.py— registered repos/servicessrc/project/settings.py—INSTALLED_APPSsrc/project/api/__init__.py—NinjaAPI(), mounted resource routers, exception handlerssrc/project/api/<resource>/— existing per-resource route packagessrc/project/types.py—AuthedRequestand other shared request types- Any existing app the feature touches
Then state your implementation plan: models, DTOs, repos, services, routes, tests, and ID prefixes.
LAYER-BY-LAYER IMPLEMENTATION
Follow this exact order. Do not skip layers. Each layer has rules that are non-negotiable.
Layer 1: ID Generator
Add to src/project/ids.py:
generate_xxx_id = _make_generator("xxx") # 3-4 char prefix
Prefixes must be unique across the project and short enough to be readable in logs.
Layer 2: Model
File: src/<app>/models/<entity>.py
Follow the models skill for full conventions. The key rules:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 433 lines · 78 tokens per session scan A cecd43376920
dj-architecture is a skill published in the GitHub repository dvf/opinionated-django (110 stars, last pushed 27d ago), licensed MIT. It adds 78 tokens to every session and 3,872 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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