050-careful-code-modification

050-careful-code-modification is a cursor rule for Cursor from synaptiai/prompt-decorators. It costs 356 tokens per session, scanned A, original, Apache-2.0.

A collection of software-development prompt decorators, which are +++ annotations that guide an AI's approach to coding tasks.

In plain words
What is it for?
Applying options for algorithms, complexity, implementation style, and other coding-related response preferences.
Why use it?
It lets developers request specific approaches when generating, debugging, designing, or reviewing software.

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/synaptiai/prompt-decorators/050-careful-code-modification
Clone the repo
git clone --depth 1 https://github.com/synaptiai/prompt-decorators

Made for: Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for 050-careful-code-modification

README.md
[![agentmods](https://agentmods.dev/badge/rules/synaptiai/prompt-decorators/050-careful-code-modification.svg)](https://agentmods.dev/rules/synaptiai/prompt-decorators/050-careful-code-modification)
Your own site
<a href="https://agentmods.dev/rules/synaptiai/prompt-decorators/050-careful-code-modification"><img src="https://agentmods.dev/badge/rules/synaptiai/prompt-decorators/050-careful-code-modification.svg" alt="Measured on agentmods" height="20"></a>
Per session 356 This file is loaded in full into every session.
When invoked 356 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 $0.00356 $0.00356
Opus 5 $0.00178 $0.00178
Sonnet 5 $0.00071 $0.00071
Haiku 4.5 $0.00036 $0.00036

Measured 4d ago against content hash 0d175cc15494, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

050-careful-code-modification 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 4d 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.

.cursor/rules/050-careful-code-modification.mdc · 54 lines

What it actually says

Careful Code Modification

1.0.0

Context

  • When modifying existing code in this codebase
  • When fixing bugs or implementing new features
  • When the codebase is fragile or has complex dependencies

Requirements

  • Make surgical, precise modifications targeting only the specific issue
  • Implement proper validation procedures before and after changes
  • Consider the scope and potential side effects of all changes
  • Ensure changes are appropriate for the sensitivity level of the codebase
  • Add thorough tests for any modifications made
  • Document changes clearly with comments explaining the rationale

Examples

# Fix specific calculation issue only
corrected_value = data['value'] * 1.05  # Apply 5% correction

# Return with minimal changes to data structure
result = data.copy()
result['value'] = corrected_value
return result
return new_data  # Returns completely different structure
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. 4d ago First seen · 54 lines · 356 tokens per session scan A 0d175cc15494

Subscribe to this mod's changes

050-careful-code-modification is a cursor rule published in the GitHub repository synaptiai/prompt-decorators (43 stars, last pushed yesterday), licensed Apache-2.0. It adds 356 tokens to every session, about $0.0018 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.