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 dvf/opinionated-django --skill dj-signalsgit clone --depth 1 https://github.com/dvf/opinionated-djangoWrote 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-signals)<a href="https://agentmods.dev/skills/dvf/opinionated-django/dj-signals"><img src="https://agentmods.dev/badge/skills/dvf/opinionated-django/dj-signals/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-signals"><img src="https://agentmods.dev/badge/skills/dvf/opinionated-django/dj-signals.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 | $0.00065 | $0.00958 |
| Opus 5.5 | $0.00026 | $0.00383 |
| Sonnet 5.5 | $0.00013 | $0.00192 |
| Haiku 4.5 | $0.00006 | $0.00096 |
Grade A, and why
dj-signals 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.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add a Reliable Signal
You are adding a reliable signal to an opinionated Django project. Standard Django signals are synchronous and unreliable — receiver failures propagate to the sender, there's no delivery guarantee if the process crashes after commit, and there's no retry. This project uses the reliable signals pattern with Celery instead.
The pattern implemented here is adapted from Haki Benita's article Reliable Signals in Django. Credit for the original design goes to him — read the full article for the rationale and edge cases.
How It Works
Signal receiver tasks are enqueued inside the same database transaction as the business operation. If the transaction rolls back, the tasks roll back too. If it commits, the tasks are guaranteed to be in the queue. Celery processes them asynchronously with at-least-once delivery.
BEFORE WRITING CODE
- Find existing signals with
GrepforReliableSignalundersrc/ - Find existing receivers with
Globforsrc/**/receivers.py - Identify which service method should emit the signal and what data needs to travel with it
Step 1: Define the Signal
File: src/<app>/signals.py
from project.signals import ReliableSignal
my_event = ReliableSignal()
Step 2: Send from the Service Layer
Call send_reliable() inside a transaction.atomic() block. Arguments MUST be JSON-serializable — pass entity IDs, never model instances:
from django.db import transaction
def create_entity(self, name: str) -> MyEntityDTO:
with transaction.atomic():
entity = self.repo.create(name=name)
my_event.send_reliable(sender=None, entity_id=entity.id)
return entity
Step 3: Write the Receiver
File: src/<app>/receivers.py
from django.dispatch import receiver
from .signals import my_event
@receiver(my_event)
def on_my_event(obj_id: str, **kwargs):
# Idempotent: guard against duplicate execution
if already_processed(obj_id):
return
do_work(obj_id)
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 · 117 lines · 65 tokens per session scan A c0387b9f716c
dj-signals is a skill published in the GitHub repository dvf/opinionated-django (111 stars, last pushed 2mo ago), licensed MIT. It adds 65 tokens to every session and 958 once invoked, about $0.0003 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.
dj-signals compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dj-signalsdvf/opinionated-django | Skill | 111 | 2mo ago | MIT | A | 65 | Claude Code |
| mem0-oss-to-platformmem0ai/mem0 | Skill | 69k | 2d ago | Apache-2.0 | A | 217 | Claude Code, Codex |
| azure-mgmt-botservice-dotnetmicrosoft/skills | Skill | 3.2k | 2d ago | MIT | A | 78 | Claude Code |
| fastapi-router-pymicrosoft/skills | Skill | 3.2k | 2d ago | MIT | A | 46 | Claude Code |
| dev-runtimelangwatch/langwatch | Skill | 5.0k | today | Apache-2.0 | A | 172 | Claude Code |
| api-designericrisco/rsc-harness | Skill | 555 | 4d ago | MIT | A | 105 | Claude Code, Codex |
| migrate-segw-to-raparc-mcp/arc-1 | Skill | 253 | yesterday | MIT | A | 106 | Claude Code |