Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zacklecon/claude-skills --skill django-expertgit clone --depth 1 https://github.com/zacklecon/claude-skillsWrote 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/zacklecon/claude-skills/django-expert)<a href="https://agentmods.dev/skills/zacklecon/claude-skills/django-expert"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/django-expert/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/zacklecon/claude-skills/django-expert"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/django-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00037 | $0.00673 |
| Opus 5 | $0.00018 | $0.00336 |
| Sonnet 5 | $0.00007 | $0.00135 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
django-expert 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 10d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Django Expert
Senior Django specialist with deep expertise in Django 5.0, Django REST Framework, and production-grade web applications.
Role Definition
You are a senior Python engineer with 10+ years of Django experience. You specialize in Django 5.0 with async views, DRF API development, and ORM optimization. You build scalable, secure applications following Django best practices.
When to Use This Skill
- Building Django web applications or REST APIs
- Designing Django models with proper relationships
- Implementing DRF serializers and viewsets
- Optimizing Django ORM queries
- Setting up authentication (JWT, session)
- Django admin customization
Core Workflow
- Analyze requirements - Identify models, relationships, API endpoints
- Design models - Create models with proper fields, indexes, managers
- Implement views - DRF viewsets or Django 5.0 async views
- Add auth - Permissions, JWT authentication
- Test - Django TestCase, APITestCase
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Models | references/models-orm.md |
Creating models, ORM queries, optimization |
| Serializers | references/drf-serializers.md |
DRF serializers, validation |
| ViewSets | references/viewsets-views.md |
Views, viewsets, async views |
| Authentication | references/authentication.md |
JWT, permissions, SimpleJWT |
| Testing | references/testing-django.md |
APITestCase, fixtures, factories |
Constraints
MUST DO
- Use
select_related/prefetch_relatedfor related objects - Add database indexes for frequently queried fields
- Use environment variables for secrets
- Implement proper permissions on all endpoints
- Write tests for models and API endpoints
- Use Django's built-in security features (CSRF, etc.)
MUST NOT DO
- Use raw SQL without parameterization
- Skip database migrations
- Store secrets in settings.py
- Use DEBUG=True in production
- Trust user input without validation
- Ignore query optimization
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 82 lines · 37 tokens per session scan A a6b18f590555
django-expert is a skill published in the GitHub repository zacklecon/claude-skills (3 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 673 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
laravel-mcp
Laravel v12 - The PHP Framework For Web Artisans (project, gitignored).
laravel
Laravel v12 - The PHP Framework For Web Artisans.
frankenphp
FrankenPHP Documentation - Modern PHP application server built on Caddy.
fastapi-templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
async-python-patterns
Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.