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-prefixed-ulidsgit 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-prefixed-ulids)<a href="https://agentmods.dev/skills/dvf/opinionated-django/dj-prefixed-ulids"><img src="https://agentmods.dev/badge/skills/dvf/opinionated-django/dj-prefixed-ulids/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-prefixed-ulids"><img src="https://agentmods.dev/badge/skills/dvf/opinionated-django/dj-prefixed-ulids.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 | $0.00102 | $0.01388 |
| Opus 5 | $0.00051 | $0.00694 |
| Sonnet 5 | $0.00020 | $0.00278 |
| Haiku 4.5 | $0.00010 | $0.00139 |
Grade A, and why
dj-prefixed-ulids 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prefixed ULID Primary Keys
This project uses Stripe-style prefixed ULIDs as the primary key for every Django model:
prd_01jq3v8f6a7b2c8d9e0f1g2h3j4k
ord_01jq3v8fgh7x2y5z9a1b2c3d4e5f
A 3-4 character prefix identifies the entity type, followed by an underscore and a lowercase ULID. ULIDs are 128-bit, lexicographically sortable by creation time, URL-safe, and collision-resistant.
Why
- Debuggable.
ord_01jq...in a log line tells you immediately it's an order — no need to cross-reference the column. - Safe to expose. Unlike auto-increment integers, prefixed ULIDs leak no ordering or volume information, and unlike opaque UUIDs they remain human-readable.
- Time-sortable. ULIDs sort chronologically, so
ORDER BY iddoubles asORDER BY created_atwithout a second index. - Type-safe across layers. Every ID is a
strend-to-end — noUUID/strcoercion at the service/API boundary. - No integer collisions. Exporting, importing, and sharding are all easier without monotonic counters.
The Generator
Put this in src/project/ids.py:
from ulid import ULID
def prefixed_ulid(prefix: str) -> str:
return f"{prefix}_{str(ULID()).lower()}"
def _make_generator(prefix: str):
def generate() -> str:
return prefixed_ulid(prefix)
generate.__name__ = f"generate_{prefix}_id"
generate.__qualname__ = f"generate_{prefix}_id"
return generate
Then register a generator per aggregate root:
generate_prd_id = _make_generator("prd")
generate_ord_id = _make_generator("ord")
generate_itm_id = _make_generator("itm")
The __name__ / __qualname__ rewrite matters: Django migrations serialize the default callable's fully qualified name, so each generator needs a distinct identity or the autodetector will get confused.
Choosing a Prefix
- 3 to 4 lowercase letters — short enough to stay readable in logs
- Must be unique across the whole project
- Prefer mnemonic, not cryptic:
ordfor order,invfor invoice,prdfor product,usrfor user - Avoid collisions with existing prefixes — grep
src/project/ids.pybefore inventing a new one - Never rename a prefix once it's in production; the prefix is part of the 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.
- 11d ago First seen · 132 lines · 102 tokens per session scan A f1db8007c2c3
dj-prefixed-ulids is a skill published in the GitHub repository dvf/opinionated-django (110 stars, last pushed 27d ago), licensed MIT. It adds 102 tokens to every session and 1,388 once invoked, about $0.0005 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.
Other skills, from other repositories
azure-cosmos-db-py
Build Azure Cosmos DB NoSQL services with Python/FastAPI following production-grade patterns. Use when implementing database client setup with dual auth (DefaultAzureCredential + emulator), service...
azure-cosmos-py
Client library for Azure Cosmos DB NoSQL API — globally distributed, multi-model database.
azure-data-tables-py
NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).
neo4j-driver-python-skill
Neo4j Python Driver v6 — driver lifecycle, executequery, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver…
alembic
Manage database migrations with Alembic. Use when a user asks to version database schemas, create migration scripts, handle schema changes in production, or manage SQLAlchemy model migrations.
huawei-cloud-ges-graph
Provides access guide for Huawei Cloud Graph Database GES service. Covers Cypher queries, GQL queries, schema/label management, summary info queries, graph data editing and more. Use this skill when users want to operate Huawei Cloud graph database GES service via terminal.