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 DVNghiem/FlowDeck --skill python-patternsgit clone --depth 1 https://github.com/DVNghiem/FlowDeckWrote 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/dvnghiem/flowdeck/python-patterns)<a href="https://agentmods.dev/skills/dvnghiem/flowdeck/python-patterns"><img src="https://agentmods.dev/badge/skills/dvnghiem/flowdeck/python-patterns/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/dvnghiem/flowdeck/python-patterns"><img src="https://agentmods.dev/badge/skills/dvnghiem/flowdeck/python-patterns.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 Data Exfiltration · line 262 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00033 | $0.03204 |
| Opus 5.5 | $0.00013 | $0.01282 |
| Sonnet 5.5 | $0.00007 | $0.00641 |
| Haiku 4.5 | $0.00003 | $0.00320 |
Grade A, and why
python-patterns 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 1mo 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 — 535 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Patterns Skill
Idiomatic Python for production-grade code. Covers modern Python 3.10+ practices.
When to Activate
Activate when:
- Writing new Python modules or packages
- Reviewing Python code for correctness and idiom
- Deciding between data modeling approaches (dataclass vs TypedDict vs Pydantic)
- Designing async services or background workers
- Setting up testing infrastructure
Type Hints
Python's type system (PEP 484, 526, 544) makes code self-documenting and enables static analysis with mypy or pyright.
Basic Annotations
# Variables (PEP 526)
count: int = 0
names: list[str] = []
mapping: dict[str, int] = {}
# Functions — always annotate public API
def greet(name: str, times: int = 1) -> str:
return (f"Hello, {name}!\n" * times).rstrip()
# Optional and Union (Python 3.10+ union syntax preferred)
def find_user(user_id: int) -> "User | None":
...
# Use TypeAlias for reused complex types
type UserId = int # Python 3.12+
UserId = NewType("UserId", int) # pre-3.12
Protocols (PEP 544) — Structural Subtyping
Prefer Protocol over ABC when you don't control the implementor.
from typing import Protocol, runtime_checkable
@runtime_checkable
class Serializable(Protocol):
def to_dict(self) -> dict[str, object]: ...
def save(obj: Serializable) -> None:
data = obj.to_dict()
...
# Any class with to_dict() satisfies Serializable — no inheritance required
Generics
from typing import TypeVar, Generic
T = TypeVar("T")
class Stack(Generic[T]):
def __init__(self) -> None:
self._items: list[T] = []
def push(self, item: T) -> None:
self._items.append(item)
def pop(self) -> T:
return self._items.pop()
Data Modeling: Dataclass vs TypedDict vs Pydantic
Choose based on where the data lives and what guarantees you need.
Dataclass — in-memory objects with behavior
from dataclasses import dataclass, field
@dataclass
class Order:
id: str
items: list[str] = field(default_factory=list)
total: float = 0.0
def add_item(self, item: str, price: float) -> None:
self.items.append(item)
self.total += price
# Use @dataclass(frozen=True) for immutable value objects
@dataclass(frozen=True)
class Money:
amount: int # stored in cents
currency: str = "USD"
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.
- 1mo ago First seen · 535 lines · 33 tokens per session scan A 1797a60f7b97
python-patterns is a skill published in the GitHub repository DVNghiem/FlowDeck (36 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 3,204 once invoked, about $0.0001 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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