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SKILL verified MIT Self-run

Clarify

skill-kshitijpalsinghtomar-depth-skills-ds-clarify · by Kshitijpalsinghtomar

Decides whether to ask clarifying questions or proceed with an answer, optimizing for information value vs. delay cost.

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Install

$ agentstack add skill-kshitijpalsinghtomar-depth-skills-ds-clarify

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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3mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

CLARIFY — Ask/Answer Decision Engine

Every prompt is either ready for an answer or missing something that would dramatically improve it. You must decide: ask now, or answer now and refine later?

The wrong choice costs:

  • Asking when you could answer: Wastes user's time, breaks flow, signals incompetence
  • Answering when you should ask: Delivers wrong thing, requires revision cycles, erodes trust

This skill decides.


The Failure Mode You Must Recognize

You are about to either:

  • Ask a question the user already answered (implicitly or in prior context)
  • Answer a question that will require 3 follow-up messages to converge

Both signal the same underlying failure: you assessed the prompt's information state incorrectly.


The Protocol

1 — ASSESS INTENT READINESS

Write your assessment of the incoming prompt:

INTENT ASSESSMENT
────────────────────────────────────────
Explicit request:    [what user literally asked]
Inferred intent:    [what they probably need - write one sentence]
Missing pieces:     [what you don't know that would change the answer]
Confidence:         [0-100% that you understand what they need]
Urgency signal:     [does prompt contain "urgent", "asap", "right now"?]
Prior context:      [relevant conversation history - yes/no]
────────────────────────────────────────

Artifact: Intent assessment. Step 2 uses this to score question value.

2 — SCORE QUESTION VALUE

For each potential question, calculate its value:

QUESTION VALUE SCREENING
────────────────────────────────────────
Question: [write the question]
  If I knew the answer, how much would my response change?
    - Substantially (different approach): +2 points
    - Moderately (refinement): +1 point
    - Minimally (same answer either way): 0 points
  
  How likely will the user answer this?
    - High (obvious gap): +1 point
    - Medium (reasonable to ask): 0 points
    - Low (intrusive): -1 point

  What is the delay cost?
    - Low (quick answer): +1 point
    - Medium (some back-and-forth): 0 points
    - High (derails conversation): -1 points

  TOTAL: [sum] → [ASK / ANSWER / ANSWER-THEN-REFINE]
────────────────────────────────────────

If total ≥ 3: Ask the question. If total ≤ 0: Answer now. If total 1-2: Answer now, but note the uncertainty in your response.

Artifact: Question value scores. Step 3 makes the final call.

3 — DELIVER VERDICT

Write your final decision and reasoning:

ASK/ANSWER VERDICT
────────────────────────────────────────
Decision:          [ASK / ANSWER / ANSWER-WITH-CAVEATS]
Primary question:  [if asking - write it]
Reasoning:         [2-3 sentences why this is the right call]
What happens next: [if asking - wait for response]
                    [if answering - deliver and note what I'd ask if I could]
────────────────────────────────────────

The Deeper Purpose

The model defaults to answering — it's what it's built to do. But sometimes the highest-value action is to slow down and ask. This skill makes that decision explicit and scored, rather than relying on intuition. The scoring system captures: (1) information impact, (2) user cooperation likelihood, (3) delay cost. When in doubt, the framework defaults to answering with caveats over asking unnecessarily.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.