clarify — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited clarify (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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
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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
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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
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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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.