ask-questions-if-underspecified
by trailofbitsask-questions-if-underspecified helps agents pause on unclear requests, ask the minimum clarifying questions, and avoid wrong work. This ask-questions-if-underspecified skill is useful for Skill Authoring, coding tasks, and any workflow where objective, scope, or constraints are missing.
This skill scores 70/100, which means it is a legitimate directory candidate with practical value, but users should expect a narrow, guidance-heavy workflow rather than a broadly automated capability. The repository clearly teaches when to trigger the skill and how to ask clarifying questions before acting, so it can reduce guesswork for agents handling ambiguous requests.
- Clear triggerability: it tells agents to use the skill when objectives, scope, constraints, environment, or safety are unclear.
- Operational workflow is explicit: it instructs agents to ask 1-5 must-have questions before implementing, and to avoid starting work until ambiguity is resolved or assumptions are approved.
- Good install decision value: the SKILL.md has substantial body content with headings, constraints, and stepwise guidance rather than placeholder text.
- Limited leverage beyond clarification: the skill is procedural and does not include scripts, references, or supporting assets, so it depends on the model to execute well.
- The workflow is intentionally narrow and may not be useful for clearly specified tasks or quick discovery reads, which limits when it should be triggered.
Overview of ask-questions-if-underspecified skill
What ask-questions-if-underspecified does
The ask-questions-if-underspecified skill helps an agent pause before acting when a request is missing critical details. It is designed to prevent wrong implementation by asking only the minimum clarifying questions needed to remove ambiguity.
Who should use it
Use the ask-questions-if-underspecified skill when you work on tasks where the objective, scope, environment, or acceptance criteria are unclear. It is especially useful for coding agents, refactoring tasks, multi-file changes, and anything where guessing would be expensive.
Why it matters for Skill Authoring
This skill is valuable because it turns uncertainty into a workflow, not a failure. Instead of improvising, it forces a decision point: ask, confirm assumptions, or stop. That makes it a strong default for ask-questions-if-underspecified for Skill Authoring when accuracy matters more than speed.
How to Use ask-questions-if-underspecified skill
Install and activate the skill
Use the repo’s skill install flow, then load plugins/ask-questions-if-underspecified/skills/ask-questions-if-underspecified/SKILL.md as the primary source. A typical ask-questions-if-underspecified install path is to add the skills repository first, then reference this skill by slug in your agent setup.
How to frame a good trigger
The skill works best when the prompt is incomplete in a way that affects output quality. A strong ask-questions-if-underspecified usage example is: “Update the auth flow for performance” or “Create tests for this module,” where the agent cannot safely infer scope, runtime, or success criteria. A weak fit is a request that already states exact files, behavior, and constraints.
Practical workflow and reading order
Start with SKILL.md to understand the decision rule, then check any linked repository context your environment provides. The ask-questions-if-underspecified guide is simple: identify missing must-have facts, ask 1-5 high-leverage questions, and do not implement until the gaps are resolved or the user approves assumptions. When reading the file, focus first on the “When to Use,” “When NOT to Use,” “Goal,” and “Workflow” sections.
What better inputs look like
Instead of a vague prompt, provide the task plus what is already known: target system, allowed files, risk tolerance, deadline, compatibility constraints, and examples of the expected result. The skill is strongest when it can narrow ambiguity quickly rather than rediscover basics through back-and-forth.
ask-questions-if-underspecified skill FAQ
Is this better than a normal prompt?
Yes, when the main risk is misunderstanding rather than execution. A normal prompt may let the model guess; ask-questions-if-underspecified makes the agent stop and verify before taking the wrong branch.
When should I not use it?
Do not use it when the request is already specific enough to execute, or when a quick discovery read can answer the open questions without asking the user. If the missing detail does not change the work, the skill adds friction instead of value.
Is it beginner-friendly?
Yes. The skill is easy to adopt because its behavior is simple: detect ambiguity, ask a small set of questions, then proceed only after clarification. Beginners benefit because it reduces accidental over-commitment and makes uncertainty visible early.
Does it fit every AI coding workflow?
No. It fits best in workflows where wrong assumptions are costly and user clarification is available. For fully autonomous batch tasks, you may want a different skill or policy that allows reasonable assumptions instead of blocking on questions.
How to Improve ask-questions-if-underspecified skill
Give it the missing decision points
To get better results, include the exact unknowns the skill should resolve: objective, scope, environment, constraints, and definition of done. The best inputs make it obvious which questions will eliminate whole branches of work.
Avoid vague prompts that force broad questioning
A common failure mode is asking the agent to “just handle it” while omitting acceptance criteria. That can trigger unnecessary clarification. Stronger prompts state what must stay unchanged, what can change, and what level of risk is acceptable.
Iterate on the first question set
If the first pass still leaves ambiguity, answer with concrete values rather than more narrative. For example, specify files, versions, rollout limits, or examples of acceptable output. That keeps ask-questions-if-underspecified usage efficient and helps the skill ask fewer follow-up questions next time.
Tune for the kind of work you do most
For feature work, prioritize behavior and UI scope. For refactors, prioritize compatibility and rollback. For automation, prioritize environment and permissions. That is the most practical way to improve ask-questions-if-underspecified skill results without changing the skill itself.