Complete AI TrainingYourJobSkills for your job

Skills / workflow

ask-questions-if-underspecified

Clarify requirements before implementing. Use when serious doubts arise.

newProject & Program ManagementOperations & Supply ChainAI & Automation

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 eas

Subscribers only

The full skill, its 1 bundled files and every download is included with every paid Complete AI plan.

Details

Sourcecommunity
License
Risk labelsafe ("critical" means the skill may run commands or touch files — read before use)
FilesSKILL.md
Added2026-09-04

Related skills

acceptance-orchestrator

Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

address-github-comments

Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.

ai-loop

Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

airflow-dag-patterns

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

antigravity-workflows

Use when asked to ship a SaaS MVP, audit application security, build an AI agent, run browser QA, or design a domain model with multiple skills and verified checkpoints.

bitbucket-automation

Automate Bitbucket repositories, pull requests, branches, issues, and workspace management via Rube MCP (Composio). Always search tools first for current schemas.