Install
$ agentstack add skill-kshitijpalsinghtomar-depth-skills-ds-clarify ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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.
- Author: Kshitijpalsinghtomar
- Source: Kshitijpalsinghtomar/depth-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.