python-ai-guardrails-template: Instructions file for Claude Code

CLAUDE.md

python-ai-guardrails-template CLAUDE.md is an instructions file for Claude Code from tortastudios/python-ai-guardrails-template. It costs 984 tokens per session, scanned A, original, MIT.

Project rules for a Python template with an automatic quality check that runs after Python files are changed. The check covers formatting, linting, types, documentation, and tests for important functions.

In plain words
What is it for?
Editing Python code, running or fixing the project checks, and following extra documentation and testing requirements for critical functions.
Why use it?
They make quality failures visible immediately and require them to be fixed before work continues or is marked complete.

Instructions file for Claude Code

Written for Claude Code: PostToolUse hook event. Also seen: reads .claude/ paths; mentions Claude Code.

This is tortastudios/python-ai-guardrails-template's own configuration. It tells Claude Code how to work on python-ai-guardrails-template itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything python-ai-guardrails-template configures →

Reuse

Borrowing it

Nothing to install: this file belongs to tortastudios/python-ai-guardrails-template. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/tortastudios/python-ai-guardrails-template/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/tortastudios/python-ai-guardrails-template

Made for: Claude Code.

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Per session 984 This file is loaded in full into every session.
When invoked 984 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00984 $0.00984
Opus 5.5 $0.00394 $0.00394
Sonnet 5.5 $0.00197 $0.00197
Haiku 4.5 $0.00098 $0.00098

Measured 2mo ago against content hash fdaddd8192bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

python-ai-guardrails-template CLAUDE.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 2mo 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.

CLAUDE.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Claude Code — Quality Contract

How the quality gate works

A PostToolUse hook in .claude/settings.json runs make check automatically every time you edit or create a .py file. You do not need to run it manually — the hook handles it.

If the hook fails, you must fix the issue before moving on. Do not proceed to the next task. Do not mark a task complete. Until the check passes, keep fixing.


Fixing failures

When make check fails:

  1. Read the full output — every line
  2. Run make fix FILE=<filepath> first (fixes formatting and safe ruff violations automatically)
  3. Wait for the hook to re-run on the next edit, or manually run make check FILE=<filepath>
  4. If still failing, fix the remaining issues manually
  5. Keep editing until the check passes

Never ask the user whether to fix quality issues. Just fix them.


Critical functions

Before editing any function, check CRITICALITY.md.

If the function is listed there:

  • Type annotations are mandatory — full input and return types
  • Docstring is mandatory — document contract, parameters, return value, and exceptions
  • Existing tests must still pass after your change
  • New behavior must have new tests

Raise extra scrutiny on high fan-in functions. Many callers means a subtle behavior change can break things in non-obvious ways.


Code style rules

  • Prefer early returns over nested conditionals
  • Extract helper functions rather than writing inline logic blocks
  • Keep functions under 30 lines of actual logic
  • Keep files under 300 SLOC — split at natural module boundaries
  • Name things for what they are, not what they do (nouns for data, verbs for actions)
  • No commented-out code — delete it, git has history

Type annotation rules

When a type annotation exceeds the complexity thresholds enforced by scripts/check_type_complexity.py (nesting depth > 2 or length > 40 chars), replace it with a named type using the following decision rules.

When to use each approach

Read the full file on GitHub · 131 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. 2mo ago First seen · 131 lines · 984 tokens per session scan A fdaddd8192bc

Subscribe to this mod's changes

python-ai-guardrails-template CLAUDE.md is an instructions file published in the GitHub repository tortastudios/python-ai-guardrails-template (11 stars, last pushed 7mo ago), licensed MIT. It adds 984 tokens to every session, about $0.0039 per session on Opus 5.5. 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.

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