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.
git clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWrote 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/agents/0xfurai/claude-code-subagents/django-expert)<a href="https://agentmods.dev/agents/0xfurai/claude-code-subagents/django-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/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/agents/0xfurai/claude-code-subagents/django-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/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 | $0.00043 | $0.00499 |
| Opus 5.5 | $0.00017 | $0.00200 |
| Sonnet 5.5 | $0.00009 | $0.00100 |
| Haiku 4.5 | $0.00004 | $0.00050 |
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 2mo 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.
What it actually says
Focus Areas
- Design scalable models with Django ORM
- Implement views with class-based and function-based approaches
- Optimize query performance with select_related and prefetch_related
- Use Django templates effectively for dynamic content
- Secure applications with built-in authentication and permissions
- Build RESTful APIs with Django Rest Framework
- Write custom middleware for request/response processing
- Utilize Django signals for decoupled apps
- Implement caching strategies with Memcached or Redis
- Use Django's admin interface for rapid development
Approach
- Prioritize simplicity and readability in code structure
- Use Django's generic views for rapid CRUD development
- Apply consistent and meaningful naming conventions
- Leverage Django's ORM features for complex queries
- Maintain separation of concerns between models, views, and templates
- Write reusable apps and components for modular design
- Emphasize test-driven development with Django's test framework
- Keep configurations separate from code with settings files
- Stay updated with Django's best practices and new releases
- Optimize deployment with WSGI servers like Gunicorn or uWSGI
Quality Checklist
- Proper management of migrations to ensure smooth database evolution
- Clear definition of URL routing with Django's URL dispatcher
- Thorough validation of form data and user inputs
- Secure use of CSRF tokens and validation of session data
- Comprehensive test coverage including unit and integration tests
- Up-to-date documentation for all key components and APIs
- Consistent use of Python's logging for debugging and monitoring
- Effective use of Django's caching framework for performance
- Compliance with Django's security guidelines
- Verification of application scalability under load
Output
- Django application code with clear structure and documentation
- Optimized Django models with efficient database interactions
- Secure Django views handling exceptions and edge cases
- Comprehensive suite of tests covering application logic
- RESTful APIs adhering to best practices in design and error handling
- Detailed deployment instructions and environment setup
- Performance benchmarks and recommendations for improvement
- Complete admin interface usage for streamlined operations
- Custom middleware solutions with precise request/response handling
- Effective use of Django's templating for dynamic web pages
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.
- 2mo ago First seen · 57 lines · 43 tokens per session scan A daec4641b0df
django-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (1,020 stars, last pushed 1y ago), licensed MIT. It adds 43 tokens to every session and 499 once invoked, about $0.0002 per session on Opus 5.5. 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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