code-patterns

A set of Python coding rules and recurring patterns for configuration, logging, errors, file paths, and asynchronous operations. It also defines naming, formatting, type-hinting, and configuration conventions.

In plain words
What is it for?
Use it when adding Python handlers, configuration fields, environment-variable handling, file-path logic, or code that runs asynchronously.
Why use it?
It gives contributors one shared standard for writing and checking code in the project, reducing inconsistent styles and common implementation mistakes.

Cursor rule for Cursor

Install

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.

agentmods
npx agentmods add rules/divar-ir/ai-doc-gen/code-patterns
Clone the repo
git clone --depth 1 https://github.com/divar-ir/ai-doc-gen

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 636 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00636
Opus 5 $0.00000 $0.00318
Sonnet 5 $0.00000 $0.00127
Haiku 4.5 $0.00000 $0.00064

Measured yesterday against content hash dbf6d957d057, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

code-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 yesterday.

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.

.cursor/rules/code-patterns.mdc · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Patterns

Style

  • Ruff: 120-char lines, 4-space indent, target-version = "py313"; import sorting enabled — run uv run ruff format src/ && uv run ruff check src/.
  • Type hints everywhere; pathlib.Path for all file paths (never strings); Pydantic BaseModel for all config/data structures.
  • Naming: snake_case files/functions, PascalCase classes, _private methods, UPPER_SNAKE constants, config classes end in Config.

Configuration

class MyHandlerConfig(BaseHandlerConfig, MyAgentConfig):
    exclude_feature: bool = Field(default=False, description="Exclude feature")  # description feeds CLI --help
  • Every Field needs a description — CLI arguments are generated from it (--exclude-feature, store_true, default None = "not specified").
  • Load order: Pydantic defaults < .ai/config.yaml (dot-notation section keys) < CLI args, merged with merge_dicts().
  • Env vars in src/config.py: required → os.environ["KEY"]; optional → os.getenv("KEY", "default") with int()/float()/str_to_bool() conversion.

Async and concurrency

  • All handler/agent operations are async; handlers implement async def handle(self).
  • Bounded concurrency: use WorkerPool from src/utils/worker_pool.py (WorkerPool(max_workers=0) = CPU count) for many tasks; asyncio.gather(*tasks, return_exceptions=True) for a fixed small set.
  • Always isolate errors: check each result with isinstance(result, Exception), log with exc_info=True, continue.

Error handling

  • Partial success is acceptable: warn and continue if some agents fail; raise ValueError only on complete failure.
  • Guarantee cleanup with try/finally (e.g., cronjob project cleanup).
  • Inside agent tools, raise ModelRetry (from pydantic_ai) for recoverable errors — file not found, permission denied.

Logging and tracing

from utils import Logger

Logger.init(logs_dir)                      # once per execution, before any use
Logger.info("Agent completed", {"total_tokens": usage.total_tokens})  # structured dict payload
Logger.error("Failed", exc_info=True)      # always exc_info=True for errors

Read the full file on GitHub · 55 lines

Changes

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.

  1. yesterday First seen · 55 lines · 0 tokens per session scan A dbf6d957d057

Subscribe to this mod's changes

code-patterns is a cursor rule published in the GitHub repository divar-ir/ai-doc-gen (752 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 636 tokens. 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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