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Decides whether to ask clarifying questions or proceed with an answer, optimizing for information value vs. delay cost.
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Baseado na classificação ocupacional SOC
Designs API contracts for consumer experience first — making correct usage obvious and incorrect usage impossible.
Replaces adjectives with evidence, claims with demonstrations, and promises with proof for conversion-oriented writing.
Forces the model to build a genuine prosecution case against its own answer before delivering it.
Detects and corrects objective drift during extended tasks by periodically checking work against the original request.
Self-selects and sequences the right depth-skills proportional to task consequence, preventing both under- and over-analysis.
Cross-compares every claim in an output to detect internal contradictions that sequential generation hides.
| 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"] |
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:
This skill decides.
You are about to either:
Both signal the same underlying failure: you assessed the prompt's information state incorrectly.
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.
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.
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 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.