AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 ufy2024/AuC --skill django-verificationgit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/django-verification)<a href="https://agentmods.dev/skills/ufy2024/auc/django-verification"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/django-verification/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/ufy2024/auc/django-verification"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/django-verification.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 20 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00031 | $0.02902 |
| Opus 5 | $0.00015 | $0.01451 |
| Sonnet 5 | $0.00006 | $0.00580 |
| Haiku 4.5 | $0.00003 | $0.00290 |
Grade A, and why
django-verification 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 9d 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 — 491 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Django Verification Loop
Run before PRs, after major changes, and pre-deploy to ensure Django application quality and security.
When to Activate
- Before opening a pull request for a Django project
- After major model changes, migration updates, or dependency upgrades
- Pre-deployment verification for staging or production
- Running full environment → lint → test → security → deploy readiness pipeline
- Validating migration safety and test coverage
Phase 1: Environment Check
# Verify Python version
python --version # Should match project requirements
# Check virtual environment
which python
pip list --outdated
# Verify environment variables
python -c "import os; import environ; print('DJANGO_SECRET_KEY set' if os.environ.get('DJANGO_SECRET_KEY') else 'MISSING: DJANGO_SECRET_KEY')"
If environment is misconfigured, stop and fix.
Phase 2: Code Quality & Formatting
# Type checking
mypy . --config-file pyproject.toml
# Linting with ruff
ruff check . --fix
# Formatting with black
black . --check
black . # Auto-fix
# Import sorting
isort . --check-only
isort . # Auto-fix
# Django-specific checks
python manage.py check --deploy
Common issues:
- Missing type hints on public functions
- PEP 8 formatting violations
- Unsorted imports
- Debug settings left in production configuration
Phase 3: Migrations
# Check for unapplied migrations
python manage.py showmigrations
# Create missing migrations
python manage.py makemigrations --check
# Dry-run migration application
python manage.py migrate --plan
# Apply migrations (test environment)
python manage.py migrate
# Check for migration conflicts
python manage.py makemigrations --merge # Only if conflicts exist
Report:
- Number of pending migrations
- Any migration conflicts
- Model changes without migrations
Phase 4: Tests + Coverage
# Run all tests with pytest
pytest --cov=apps --cov-report=html --cov-report=term-missing --reuse-db
# Run specific app tests
pytest apps/users/tests/
# Run with markers
pytest -m "not slow" # Skip slow tests
pytest -m integration # Only integration tests
# Coverage report
open htmlcov/index.html
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.
- 9d ago First seen · 491 lines · 31 tokens per session scan A 59ee55bb6792
django-verification is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 2,902 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 2026-09-03.
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