ask-questions-if-underspecified
Clarify requirements before implementing. Use when serious doubts arise.
Best use case
ask-questions-if-underspecified is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Clarify requirements before implementing. Use when serious doubts arise.
Clarify requirements before implementing. Use when serious doubts arise.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "ask-questions-if-underspecified" skill to help with this workflow task. Context: Clarify requirements before implementing. Use when serious doubts arise.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ask-questions-if-underspecified/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ask-questions-if-underspecified Compares
| Feature / Agent | ask-questions-if-underspecified | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Clarify requirements before implementing. Use when serious doubts arise.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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) If multiple plausible interpretations exist, assume it is underspecified. ### 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 `defaults` to 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 asks you to proceed without answers: - State your assumptions as a short numbered list - Ask for confirmation; proceed only after they confirm or correct them ### 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 ```text 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.
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