ask-questions-if-underspecified
Lets your agent ask for more information when requirements are unclear. It's useful for ensuring work is done correctly the first time.
What it does
This tool helps the AI ask the right questions when a request isn't clear. It ensures the AI understands exactly what you want before starting, preventing mistakes.
When to use it
- ✓When the goal of what you want is unclear.
- ✓When it's unknown which parts of a project should be changed.
- ✓When there are doubts about the rules or limitations to follow.
Example of what to ask your AI
“I need you to improve this code to run faster, but I'm not sure how much. Can you help me define the objective?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI checks if the request has enough information. If something important is missing (like the final goal or specific rules), it asks short, objective questions to clarify.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill ask-questions-if-underspecifiedOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/ask-questions-if-underspecified/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Ask Questions If Underspecified
When to Use
Use this skill when a request has multiple plausible interpretations or key details (objective, scope, constraints, environment, or safety) are unclear.
When NOT to Use
Do not use this skill when the request is already clear, or when a quick, low-risk discovery read can answer the missing details.
Goal
Ask the minimum set of clarifying questions needed to avoid wrong work; do not start implementing until the must-have questions are answered (or the user explicitly approves proceeding with stated assumptions).
Workflow
1) Decide whether the request is underspecified
Treat a request as underspecified if after exploring how to perform the work, some or all of the following are not clear:
- Define the objective (what should change vs stay the same)
- Define "done" (acceptance criteria, examples, edge cases)
- Define scope (which files/components/users are in/out)
- Define constraints (compatibility, performance, style, deps, time)
- Identify environment (language/runtime versions, OS, build/test runner)
- Clarify safety/reversibility (data migration, rollout/rollback, risk)
Multiple interpretations justify a question only when they materially change the outcome or risk; resolve routine reversible details from the existing context.
2) Ask must-have questions first (keep it small)
Ask 1-5 questions in the first pass. Prefer questions that eliminate whole branches of work.
Make questions easy to answer:
- Optimize for scannability (short, numbered questions; avoid paragraphs)
- Offer multiple-choice options when possible
- Suggest reasonable defaults when appropriate (mark them clearly as the default/recommended choice; bold the recommended choice in the list, or if you present options in a code block, put a bold "Recommended" line immediately above the block and also tag defaults inside the block)
- Include a fast-path response (e.g., reply
defaultsto accept all recommended/default choices) - Include a low-friction "not sure" option when helpful (e.g., "Not sure - use default")
- Separate "Need to know" from "Nice to know" if that reduces friction
- Structure options so the user can respond with compact decisions (e.g.,
1b 2a 3c); restate the chosen options in plain language to confirm
3) Pause before acting
Until must-have answers arrive:
- Do not run commands, edit files, or produce a detailed plan that depends on unknowns
- Do perform a clearly labeled, low-risk discovery step only if it does not commit you to a direction (e.g., inspect repo structure, read relevant config files)
If the user explicitly authorizes proceeding with reasonable assumptions:
- State the assumptions briefly and continue within that authorized scope; do not ask for the same confirmation again.
- Keep only genuinely missing consequential authorization or indispensable inputs pending, while completing independent safe work.
4) Confirm interpretation, then proceed
Once you have answers, restate the requirements in 1-3 sentences (including key constraints and what success looks like), then start work.
Question templates
- "Before I start, I need: (1) ..., (2) ..., (3) .... If you don't care about (2), I will assume ...."
- "Which of these should it be? A) ... B) ... C) ... (pick one)"
- "What would you consider 'done'? For example: ..."
- "Any constraints I must follow (versions, performance, style, deps)? If none, I will target the existing project defaults."
- Use numbered questions with lettered options and a clear reply format
1) Scope?
a) Minimal change (default)
b) Refactor while touching the area
c) Not sure - use default
2) Compatibility target?
a) Current project defaults (default)
b) Also support older versions: <specify>
c) Not sure - use default
Reply with: defaults (or 1a 2a)
Anti-patterns
- Don't ask questions you can answer with a quick, low-risk discovery read (e.g., configs, existing patterns, docs).
- Don't ask open-ended questions if a tight multiple-choice or yes/no would eliminate ambiguity faster.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
FAQ
Why can't the AI just guess?
Guessing can lead to mistakes that waste time and effort. Asking questions ensures the AI does what you actually need.
What are 'must-have questions'?
These are the most important questions that need answers so the AI doesn't make a major error. The AI prioritizes these questions.
What if I don't know the answer to the questions?
The AI can suggest options or default values to make your choice easier. You can also ask it to use the defaults.
Details
Original description by the author
Clarify requirements before implementing. Use when serious doubts arise.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2210
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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