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 agentmods add skills/julien777z/agent-sync-action/python-type-safetynpx skills add julien777z/agent-sync-action --skill python-type-safetygit clone --depth 1 https://github.com/julien777z/agent-sync-actionWhat 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 | $0.00044 | $0.01367 |
| Opus 5 | $0.00022 | $0.00683 |
| Sonnet 5 | $0.00009 | $0.00273 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
python-type-safety 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 2d 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Type Safety
Leverage Python's type system to catch errors at static analysis time. Type annotations serve as enforced documentation that tooling validates automatically.
When to Use This Skill
- Adding type hints to existing code
- Creating generic, reusable classes
- Defining structural interfaces with protocols
- Configuring mypy or pyright for strict checking
- Understanding type narrowing and guards
- Building type-safe APIs and libraries
Core Concepts
1. Type Annotations
Declare expected types for function parameters, return values, and variables.
2. Generics
Write reusable code that preserves type information across different types.
3. Protocols
Define structural interfaces without inheritance (duck typing with type safety).
4. Type Narrowing
Use guards and conditionals to narrow types within code blocks.
Quick Start
def get_user(user_id: str) -> User | None:
"""Return type makes 'might not exist' explicit."""
...
# Type checker enforces handling None case
user = get_user("123")
if user is None:
raise UserNotFoundError("123")
print(user.name) # Type checker knows user is User here
Fundamental Patterns
Pattern 1: Annotate All Public Signatures
Every public function, method, and class should have type annotations.
def get_user(user_id: str) -> User:
"""Retrieve user by ID."""
...
def process_batch(
items: list[Item],
max_workers: int = 4,
) -> BatchResult[ProcessedItem]:
"""Process items concurrently."""
...
class UserRepository:
def __init__(self, db: Database) -> None:
self._db = db
async def find_by_id(self, user_id: str) -> User | None:
"""Return User if found, None otherwise."""
...
async def find_by_email(self, email: str) -> User | None:
...
async def save(self, user: User) -> User:
"""Save and return user with generated ID."""
...
Use mypy --strict or pyright in CI to catch type errors early. For existing projects, enable strict mode incrementally using per-module overrides.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 203 lines · 44 tokens per session scan A a3034264cf71
python-type-safety is a skill published in the GitHub repository julien777z/agent-sync-action (5 stars, last pushed 3d ago), licensed MIT. It adds 44 tokens to every session and 1,367 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 2026-08-31.
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