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 rules/chatbotgang/ai-coding-workshop-250712/pythongit clone --depth 1 https://github.com/chatbotgang/ai-coding-workshop-250712What 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 | $0.05675 | $0.05675 |
| Opus 5 | $0.02838 | $0.02838 |
| Sonnet 5 | $0.01135 | $0.01135 |
| Haiku 4.5 | $0.00568 | $0.00568 |
Grade A, and why
python 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 today.
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 — 873 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Development Guide in CL
This document provides comprehensive guidance for Python development in Crescendo Lab, covering architecture patterns, coding style, and best practices for FastAPI applications.
Table of Contents
- Architecture Patterns
- Package Structure
- Code Organization
- Naming Conventions
- Types and Data Models
- Functions and Methods
- Error Handling
- Async/Await and Concurrency
- Testing
- Performance Considerations
- Logging and Observability
- Configuration Management
- FastAPI Patterns
- Dependencies and Dependency Injection
- Documentation
- Project Structure
Architecture Patterns
Clean Architecture
Python projects in CL follow a clean architecture pattern with distinct layers:
-
Entrypoint Layer (
entrypoint/)- Contains application entry points and configuration
- HTTP servers, CLI commands, and application bootstrapping
- Depends on router and domain layers
-
Domain Layer (
internal/domain/)- Contains business entities and logic
- Independent of external frameworks and databases
- Defines protocols (interfaces) that are implemented by outer layers
-
Router Layer (
internal/router/)- HTTP handlers, middleware, and routing logic
- Connects external HTTP requests to internal domain logic
- Depends on domain layer
-
Adapter Layer (
internal/adapter/) - Future implementation- Implements protocols defined in the domain layer
- Connects the application to external systems (databases, message brokers, etc.)
- Contains concrete implementations of repositories and services
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.
- today First seen · 873 lines · 5,675 tokens per session scan A ba0eb3bc6e96
python is a cursor rule published in the GitHub repository chatbotgang/ai-coding-workshop-250712 (66 stars, last pushed 1y ago), licensed Apache-2.0. It adds 5,675 tokens to every session, about $0.0284 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 2026-09-01.
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