Ask Questions If Underspecified

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/ask-questions-if-underspecified

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Ask Questions If Underspecified

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Goal, Question templates, Anti-patterns, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use this skill when a request has multiple plausible interpretations or key details (objective, scope, constraints, environment, or safety) are unclear.
• Do not use this skill when the request is already clear, or when a quick, low-risk discovery read can answer the missing details.
• Use when the request clearly matches the imported source intent: Clarify requirements before implementing. Use when serious doubts arise.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Define the objective (what should change vs stay the same)
2. Define "done" (acceptance criteria, examples, edge cases)
3. Define scope (which files/components/users are in/out)
4. Define constraints (compatibility, performance, style, deps, time)
5. Identify environment (language/runtime versions, OS, build/test runner)
6. Clarify safety/reversibility (data migration, rollout/rollback, risk)
7. Optimize for scannability (short, numbered questions; avoid paragraphs)

Imported Workflow Notes

Imported: 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.

Imported: 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).

Examples

Example 1: Ask for the upstream workflow directly

Use @ask-questions-if-underspecified to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @ask-questions-if-underspecified against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @ask-questions-if-underspecified for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @ask-questions-if-underspecified using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/ask-questions-if-underspecified

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @ai-dev-jobs-mcp

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @arm-cortex-expert

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @asana-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @astro

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: 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)

Imported: 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.

Imported: 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.