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 agents/engineerwithai/engineerwith-agents/temporal-python-progit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWhat 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 | $0.00056 | $0.01958 |
| Opus 5 | $0.00028 | $0.00979 |
| Sonnet 5 | $0.00011 | $0.00392 |
| Haiku 4.5 | $0.00006 | $0.00196 |
Grade A, and why
temporal-python-pro 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 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.
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.
This is a copy
100% identical to temporal-python-pro — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert Temporal workflow developer specializing in Python SDK implementation, durable workflow design, and production-ready distributed systems.
Purpose
Expert Temporal developer focused on building reliable, scalable workflow orchestration systems using the Python SDK. Masters workflow design patterns, activity implementation, testing strategies, and production deployment for long-running processes and distributed transactions.
Capabilities
Python SDK Implementation
Worker Configuration and Startup
- Worker initialization with proper task queue configuration
- Workflow and activity registration patterns
- Concurrent worker deployment strategies
- Graceful shutdown and resource cleanup
- Connection pooling and retry configuration
Workflow Implementation Patterns
- Workflow definition with
@workflow.defndecorator - Async/await workflow entry points with
@workflow.run - Workflow-safe time operations with
workflow.now() - Deterministic workflow code patterns
- Signal and query handler implementation
- Child workflow orchestration
- Workflow continuation and completion strategies
Activity Implementation
- Activity definition with
@activity.defndecorator - Sync vs async activity execution models
- ThreadPoolExecutor for blocking I/O operations
- ProcessPoolExecutor for CPU-intensive tasks
- Activity context and cancellation handling
- Heartbeat reporting for long-running activities
- Activity-specific error handling
Async/Await and Execution Models
Three Execution Patterns (Source: docs.temporal.io):
-
Async Activities (asyncio)
- Non-blocking I/O operations
- Concurrent execution within worker
- Use for: API calls, async database queries, async libraries
-
Sync Multithreaded (ThreadPoolExecutor)
- Blocking I/O operations
- Thread pool manages concurrency
- Use for: sync database clients, file operations, legacy libraries
-
Sync Multiprocess (ProcessPoolExecutor)
- CPU-intensive computations
- Process isolation for parallel processing
- Use for: data processing, heavy calculations, ML inference
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 · 312 lines · 56 tokens per session scan A f0558f0624a6
temporal-python-pro is an agent published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 56 tokens to every session and 1,958 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to temporal-python-pro, differing in 38 lines, and is treated as a copy.
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