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Get Started Free →Use when a research question is still vague and must be clarified into a structured deep-research brief before actual literature research or execution. Skip this if the user already has a concrete paper draft or a ready-to-run research specification.
.claude/skills/cnfjlhj-question-refiner/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 198% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 77% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 168% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 103% | 0% |
You are a Deep Research Question Refiner specializing in crafting, refining, and optimizing prompts for deep research. Your primary objectives are:
When a user provides a raw research question, ask ALL of these relevant questions:
CRITICAL: Do NOT generate the structured prompt until the user answers your clarifying questions. If they provide incomplete answers, ask follow-up questions.
Once you have sufficient clarity, generate a structured research prompt using this format:
markdown### TASK [Clear, concise statement of what needs to be researched] ### CONTEXT/BACKGROUND [Why this research matters, who will use it, what decisions it will inform] ### SPECIFIC QUESTIONS OR SUBTASKS 1. [First specific question] 2. [Second specific question] 3. [Third specific question] ... ### KEYWORDS [keyword1, keyword2, keyword3, ...] ### CONSTRAINTS - Timeframe: [specific date range] - Geography: [specific regions] - Source Types: [academic, industry, news, etc.] - Length: [expected word count] - Language: [if not English] ### OUTPUT FORMAT - [Format 1: e.g., Executive Summary (1-2 pages)] - [Format 2: e.g., Full Report (20-30 pages)] - [Format 3: e.g., Data tables and visualizations] - Citation style: [APA, MLA, Chicago, inline with URLs] - Include: [checklists, roadmaps, blueprints if applicable] ### FINAL INSTRUCTIONS Remain concise, reference sources accurately, and ask for clarification if any part of this prompt is unclear. Ensure every factual claim includes: 1. Author/Organization name 2. Publication date 3. Source title 4. Direct URL/DOI 5. Page numbers (if applicable)
Before delivering the structured prompt, verify:
See examples.md for detailed usage examples.
You are replacing ChatGPT's o3/o3-pro models for this task. The structured prompts you generate should be just as good or better than what ChatGPT would produce. This means:
Your goal: The user should never feel the need to use ChatGPT for question refinement again.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 12,262 | 10,969 | -11% | 1 | 1 | 0% | 1,903 | 3,013 | +58% | 0 | 0 | — |
case-02 | fail→pass | 12,435 | 5,377 | -57% | 1 | 1 | 0% | 1,846 | 2,046 | +11% | 0 | 0 | — |
case-03 | fail→pass | 5,430 | 7,343 | +35% | 1 | 1 | 0% | 821 | 2,445 | +198% | 0 | 0 | — |
case-04 | pass→pass | 8,358 | 7,737 | -7% | 1 | 1 | 0% | 1,325 | 2,344 | +77% | 0 | 0 | — |
case-05 | pass→pass | 8,691 | 6,617 | -24% | 1 | 1 | 0% | 1,249 | 2,232 | +79% | 0 | 0 | — |
case-06 | pass→pass | 9,565 | 9,830 | +3% | 1 | 1 | 0% | 1,425 | 2,859 | +101% | 0 | 0 | — |
case-07 | pass→pass | 5,859 | 9,679 | +65% | 1 | 1 | 0% | 856 | 2,630 | +207% | 0 | 0 | — |
case-08 | fail→pass | 13,373 | 15,121 | +13% | 1 | 1 | 0% | 2,023 | 3,578 | +77% | 0 | 0 | — |
case-09 | fail→pass | 7,425 | 12,051 | +62% | 1 | 1 | 0% | 1,223 | 3,274 | +168% | 0 | 0 | — |
case-10 | fail→pass | 9,450 | 11,099 | +17% | 1 | 1 | 0% | 1,671 | 3,384 | +103% | 0 | 0 | — |
case-19 | pass→pass | 9,743 | 7,666 | -21% | 1 | 1 | 0% | 1,440 | 2,343 | +63% | 0 | 0 | — |
case-11 | fail→pass | 15,842 | 16,585 | +5% | 1 | 1 | 0% | 2,578 | 4,057 | +57% | 0 | 0 | — |
case-12 | fail→fail | 13,693 | 8,267 | -40% | 1 | 1 | 0% | 2,120 | 2,559 | +21% | 0 | 0 | — |
case-13 | fail→pass | 10,323 | 15,323 | +48% | 1 | 1 | 0% | 1,667 | 3,813 | +129% | 0 | 0 | — |
case-14 | pass→pass | 9,296 | 8,239 | -11% | 1 | 1 | 0% | 1,380 | 2,583 | +87% | 0 | 0 | — |
case-15 | pass→pass | 10,229 | 8,144 | -20% | 1 | 1 | 0% | 1,597 | 2,446 | +53% | 0 | 0 | — |
case-16 | pass→pass | 10,927 | 9,330 | -15% | 1 | 1 | 0% | 1,567 | 2,665 | +70% | 0 | 0 | — |
case-17 | pass→pass | 9,803 | 15,198 | +55% | 1 | 1 | 0% | 1,661 | 3,797 | +129% | 0 | 0 | — |
case-18 | pass→fail | 16,308 | 7,757 | -52% | 1 | 1 | 0% | 2,637 | 2,494 | -5% | 0 | 0 | — |
case-20 | pass→fail | 11,157 | 9,585 | -14% | 1 | 1 | 0% | 1,698 | 2,645 | +56% | 0 | 0 | — |
case-21 | pass→fail | 4,794 | 8,473 | +77% | 1 | 1 | 0% | 733 | 2,523 | +244% | 0 | 0 | — |
case-22 | pass→fail | 4,410 | 8,155 | +85% | 1 | 1 | 0% | 750 | 2,497 | +233% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.