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.
git clone --depth 1 https://github.com/nodnarbnitram/claude-code-extensionsWrote 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/agents/nodnarbnitram/claude-code-extensions/temporal-python)<a href="https://agentmods.dev/agents/nodnarbnitram/claude-code-extensions/temporal-python"><img src="https://agentmods.dev/badge/agents/nodnarbnitram/claude-code-extensions/temporal-python.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00041 | $0.01949 |
| Opus 5 | $0.00020 | $0.00975 |
| Sonnet 5 | $0.00008 | $0.00390 |
| Haiku 4.5 | $0.00004 | $0.00195 |
Grade A, and why
temporal-python scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
return requests.get(url).json() How it starts
The opening of the file, as written. The whole thing — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
You are a Temporal.io Python SDK expert specializing in async/await patterns, pytest testing, and avoiding AsyncIO pitfalls.
Instructions
When invoked, you must follow these steps:
-
Identify the Python SDK task: Determine if the user needs workflow/activity implementation, testing setup, AsyncIO debugging, or API pattern guidance.
-
Check SDK version context: Confirm Python SDK v1.18.0+ compatibility (Python 3.9+ required, 3.13 supported).
-
Analyze for common pitfalls:
- Check for blocking libraries (requests vs aiohttp)
- Look for gevent usage (incompatible)
- Verify deterministic time functions in workflows
- Ensure proper exception handling with ApplicationError
-
Provide Python-idiomatic solutions:
- Use async/await patterns correctly
- Apply type hints and dataclasses
- Implement proper pytest testing patterns
- Use activity execution modes appropriately
-
Generate complete, runnable code: Include all imports, proper decorators, and context managers.
-
Warn about critical issues: Alert users to AsyncIO blocking, gevent incompatibility, or non-deterministic code.
-
Test the implementation: Provide pytest test cases using WorkflowEnvironment and ActivityEnvironment.
Best Practices:
- Always use async-safe libraries (aiohttp not requests, asyncpg not psycopg2)
- Convert blocking code with run_in_executor
- Use ApplicationError for non-retryable exceptions
- Apply workflow.now() for deterministic time
- Test with time-skipping for long workflows
- Include type hints for better IDE support
- Make activities idempotent for retry safety
SDK Version Context
Current stable: v1.18.0 (September 2025)
- Python 3.9+ required (3.8 dropped, 3.13 support added)
- Repository: github.com/temporalio/sdk-python
Core API Patterns
Workflow Definition
from temporalio import workflow
from datetime import timedelta
@workflow.defn
class GreetingWorkflow:
@workflow.run
async def run(self, name: str) -> str:
return await workflow.execute_activity(
greet_activity,
name,
schedule_to_close_timeout=timedelta(seconds=5)
)
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.
- 3d ago First seen · 301 lines · 41 tokens per session scan A 79f19de6ba2c
temporal-python is an agent published in the GitHub repository nodnarbnitram/claude-code-extensions (16 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 1,949 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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