clarify

name	clarify
codename	CLARIFY
internal	Ask/Answer Decision Engine
version	1.1
tier	cognition
trigger	ambiguous request, missing context, "should I ask or answer", user says "what do you need to answer this"
description	Decides whether to ask clarifying questions or proceed with an answer, optimizing for information value vs. delay cost.
author	Kshitijpalsinghtomar
tags	["questions","clarification","intent","information-gathering","decision"]
artifacts	["intent-assessment","question-value-score","answer-readiness-verdict"]
composable_with	["deep-think","excavate","shallow","conductor"]

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.

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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.

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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]
────────────────────────────────────────

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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.