agentmods.dev

mnemonic python.instructions.md

A set of rules for writing Python code, including formatting, imports, type hints, and error handling. It also defines expectations for executable hook scripts and their JSON output.

In plain words
What is it for?
Use it when creating or editing Python code, organizing imports, adding function types, handling exceptions, or writing command-line hook scripts.
Why use it?
It gives coding agents consistent standards for producing and checking Python files.

Instructions file for GitHub Copilot

22 1mo ago A scan 178 tokens original MIT
View the source file modeled-information-format/mnemonic
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 instructions/modeled-information-format/mnemonic/python
Clone the repo
git clone --depth 1 https://github.com/modeled-information-format/mnemonic

Made for: GitHub Copilot.

Per session 178 This file is loaded in full into every session.
When invoked 178 The same file — it is already loaded in full.
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 $0.00178 $0.00178
Opus 5 $0.00089 $0.00089
Sonnet 5 $0.00036 $0.00036
Haiku 4.5 $0.00018 $0.00018

Measured 2d ago against content hash 2b81592d1dc7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mnemonic python.instructions.md 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.

.github/instructions/python.instructions.md · 31 lines

What it actually says

Python Code Guidelines

Style

  • Use ruff for linting and formatting
  • Line length: 120 characters
  • Target: Python 3.8+
  • Use double quotes for strings

Imports

  • Follow isort ordering (handled by ruff)
  • Group: stdlib, third-party, local

Type Hints

  • Prefer type hints for function signatures
  • Use from __future__ import annotations for forward references

Error Handling

  • Use specific exception types
  • Log errors before re-raising
  • Avoid bare except: clauses

Hook Files (hooks/*.py)

  • Must be executable Python scripts
  • Should handle --test flag for validation
  • Use JSON output for structured data
  • Exit with appropriate codes (0=success, 1=error)
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. 2d ago First seen · 31 lines · 178 tokens per session scan A 2b81592d1dc7

Subscribe to this mod's changes

mnemonic python.instructions.md is an instructions file published in the GitHub repository modeled-information-format/mnemonic (22 stars, last pushed 1mo ago), licensed MIT. It adds 178 tokens to every session, about $0.0009 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 2026-08-30.

LanguagesMemory and contextWriting and docsai-memoryclaude-codeclaude-pluginknowledge-managementmarkdownmemory-system
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