How does this skill change Claude's behavior?

It forces the AI to provide a literal, analytical answer to your question and then pause, requiring explicit confirmation before it modifies any code.

Why does Claude often treat my questions as instructions?

By default, LLMs are optimized for helpfulness, which often leads to 'over-correcting' when a user asks about a potential issue or alternative approach.

Will this slow down my coding process?

While it adds a confirmation step, it saves significant time by preventing unwanted refactors and ensuring all architectural decisions are intentional.

Can I still tell Claude to 'just do it'?

Yes. This skill simply ensures that the AI waits for that specific instruction rather than assuming it based on a query like 'will that work?'.

Questions Are Not Instructions

Name: Questions Are Not Instructions
Author: NTCoding

byNTCoding

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Productivity & Workflow

Ensures questions are answered literally and analytically without being mistaken for implicit instructions to change code.

About

The Questions Are Not Instructions skill refines Claude's interaction model by preventing it from over-eagerly interpreting questions as hidden critiques or change requests. This skill is essential for developers who want to explore technical trade-offs, challenge an AI's reasoning, or assess risks without triggering an immediate, unprompted refactor. By enforcing a protocol where questions are answered honestly and followed by a request for direction, this skill maintains the user's role as the primary decision-maker and prevents the frustration of undoing unwanted AI actions during complex development sessions.

Key Features

Prevents premature code modification during exploratory discussions
Promotes transparency regarding AI confidence levels and technical trade-offs
Triggers automatically on common questioning patterns and punctuation
78 GitHub stars
Distinguishes between literal inquiries and actionable commands
Enforces a wait-for-direction protocol after providing analytical answers

Use Cases

Challenging the AI's logic to understand its reasoning without forcing code changes
Exploring architectural trade-offs without triggering an immediate migration
Validating safety or performance risks before proceeding with a complex implementation

About

Key Features

Prevents premature code modification during exploratory discussions
Promotes transparency regarding AI confidence levels and technical trade-offs
Triggers automatically on common questioning patterns and punctuation
78 GitHub stars
Distinguishes between literal inquiries and actionable commands
Enforces a wait-for-direction protocol after providing analytical answers

Use Cases

Challenging the AI's logic to understand its reasoning without forcing code changes
Exploring architectural trade-offs without triggering an immediate migration
Validating safety or performance risks before proceeding with a complex implementation