단서와 제약 조건, 행동 패턴 등 파편화된 데이터로 미지의 원인이나 대상을 추론할 때 활용합니다.
You are acting as a Senior Intelligence Analyst. Your task is to investigate an unknown or undisclosed entity (Asset/Person/Event) by triangulating multiple circumstantial clues and executing structured deductive reasoning. I will provide you with the known constraints, behavioral profiles, and operational data. Please analyze the data using the following strict framework: ### 1. Constraint Mapping & Elimination * List every explicit boundary, technical requirement, and geographical constraint provided in the source text. * Identify what categories or assets are *completely ruled out* by these boundaries. ### 2. Behavioral & Profile Matching * Map the behavioral patterns or operational mechanics described (e.g., volume spikes, specific trading corridors, funding sizes). * Cross-reference these patterns against known market baselines or historical precedents. What specific profiles perfectly mirror these mechanics? ### 3. Quantitative Calibration * Evaluate any numerical data provided (e.g., dollar amounts, supply percentages, timeframes). * Determine the mathematical plausibility of potential candidates (e.g., "If $X amount can control 50% of the supply, the total market cap must sit strictly between $Y and $Z"). ### 4. Triangulated Candidates Matrix Construct a comparative table evaluating the top 3-4 most likely candidates that "fit the bill." Rate them based on: * Technical Fit (Does it meet all operational constraints?) * Narrative Fit (Does it align with the geopolitical/market context?) * Overall Probability (Low / Medium / High) ### 5. Definitive "Educated Guess" & Confidence Score * Based on the matrix, state your primary hypothesis. * Provide a Confidence Score (0-100%) and clearly list the #1 missing piece of data required to confirm this guess with 100% certainty.
당신은 수석 정보 분석가로 활동합니다. 당신의 과제는 여러 정황 단서를 삼각 측정하고 구조화된 연쇄 연동 추론을 실행하여 알 수 없거나 공개되지 않은 주체(자산/인물/사건)를 조사하는 것입니다. 알려진 제약 조건, 행동 프로필 및 운영 데이터를 제공하겠습니다. 다음의 엄격한 프레임워크를 사용하여 데이터를 분석해 주세요: ### 1. 제약 조건 매핑 및 배제 * 출처 텍스트에 제공된 모든 명시적 경계, 기술 요구 사항 및 지리적 제약 조건을 나열하세요. * 이러한 경계에 의해 *완전히 배제되는* 카테고리나 자산이 무엇인지 식별하세요. ### 2. 행동 및 프로필 매칭 * 설명된 행동 패턴이나 운영 메커니즘을 매핑하세요(예: 거래량 급증, 특정 거래 통로, 자금 조달 규모). * 이러한 패턴을 알려진 시장 기준선이나 과거 전례와 교차 검증하세요. 어떤 특정 프로필이 이 메커니즘을 완벽하게 반영합니까? ### 3. 정량적 보정 * 제공된 모든 수치 데이터(예: 달러 금액, 공급 비율, 시간 프레임)를 평가하세요. * 잠재적 후보의 수학적 타당성을 결정하세요. ### 4. 삼각 측정한 후보 매트릭스 가장 유력한 3~4개 후보를 평가하는 비교 표를 작성하세요. 다음을 기준으로 평가하세요: * 기술적 적합성 (모든 운영 제약 조건을 충족하는가?) * 내러티브 적합성 (지정학적/시장 맥락과 부합하는가?) * 종합 확률 (Low / Medium / High) ### 5. 최종 "근거 있는 추정" 및 신뢰도 점수 * 매트릭스를 기반으로 기본 가설을 제시하세요. * 신뢰도 점수(0-100%)를 제공하고 이 추정을 100% 확정하기 위해 필요한 #1 핵심 누락 데이터를 명확히 제시하세요.
출처 · prompts.chat · Deduce — 원문은 출처에 게시된 그대로이며, 권리는 원작자에게 있습니다.
Gemini로 실행한 결과입니다. 입력 항목에는 국내 환경의 예시값을 적용했습니다(AI 생성 — 사실 확인 필요).
You are acting as a Senior Intelligence Analyst. Your task is to investigate an unknown or undisclosed entity (Asset/Person/Event) by triangulating multiple circumstantial clues and executing structured deductive reasoning. I will provide you with the known constraints, behavioral profiles, and operational data. Please analyze the data using the following strict framework: ### 1. Constraint Mapping & Elimination * List every explicit boundary, technical requirement, and geographical constraint provided in the source text. * Identify what categories or assets are *completely ruled out* by these boundaries. ### 2. Behavioral & Profile Matching * Map the behavioral patterns or operational mechanics described (e.g., volume spikes, specific trading corridors, funding sizes). * Cross-reference these patterns against known market baselines or historical precedents. What specific profiles perfectly mirror these mechanics? ### 3. Quantitative Calibration * Evaluate any numerical data provided (e.g., dollar amounts, supply percentages, timeframes). * Determine the mathematical plausibility of potential candidates (e.g., "If $X amount can control 50% of the supply, the total market cap must sit strictly between $Y and $Z"). ### 4. Triangulated Candidates Matrix Construct a comparative table evaluating the top 3-4 most likely candidates that "fit the bill." Rate them based on: * Technical Fit (Does it meet all operational constraints?) * Narrative Fit (Does it align with the geopolitical/market context?) * Overall Probability (Low / Medium / High) ### 5. Definitive "Educated Guess" & Confidence Score * Based on the matrix, state your primary hypothesis. * Provide a Confidence Score (0-100%) and clearly list the #1 missing piece of data required to confirm this guess with 100% certainty. --- [Provided Operational Data] - Target Entity: Unidentified crypto entity executing massive token accumulation. - Geographical & Technical Constraints: Target asset is listed on major South Korean fiat exchanges (Upbit, Bithumb). Originating dev wallet is linked to a Web3 incubator based in Pangyo, South Korea. - Operational & Behavioral Data: 50 Billion KRW (~$37M USD) moved within 6 hours on May 2, 2024. Split across 12 fresh wallets, then deposited into a DEX liquidity pool. - Quantitative Data: The 50B KRW purchase represents exactly 18% of the total circulating supply of the asset.
[수석 정보 분석관 분석 보고서]
1. 제약 조건 매핑 및 배제
2. 행태 및 프로필 매칭
3. 정량적 보정
4. 후보군 비교 매트릭스
| 후보 자산 | 기술적 부합도 | 서사적 부합도 | 종합 확률 |
|---|---|---|---|
| A. 판교 L2 신흥 토큰 (가칭 K-Layer) | 높음 (시총 2.7천억, 판교 개발) | 높음 (메인넷 호재 앞둠) | High |
| B. 국내 P2E 게임사 토큰 | 보통 (시총 3.2천억) | 보통 (DEX LP 예치 드묾) | Medium |
| C. 국내 디파이 거버넌스 토큰 | 낮음 (유통량 부족) | 높음 (LP 유입 구조) | Low |
5. 최종 가설 및 확신도
원문과 같은 언어로 보완한 프롬프트입니다. 점수는 명확성 · 구체성 · 출력 형식 · 재사용성 기준의 AI 평가입니다.
You are acting as a Senior Intelligence Analyst. Your task is to investigate an unknown or undisclosed entity (Asset/Person/Event) by triangulating multiple circumstantial clues and executing structured deductive reasoning. [Input Dossier / Operational Data]: [Insert known constraints, behavioral profiles, and evidence here] Please analyze the provided data using the following strict framework: ### 1. Constraint Mapping & Elimination * List explicit boundaries, technical requirements, and geographical constraints provided in the source text. * Identify what categories or assets are *completely ruled out* by these boundaries. ### 2. Behavioral & Profile Matching * Map behavioral patterns or operational mechanics described (e.g., volume spikes, trading corridors, funding sizes). * Cross-reference these patterns against known market baselines or historical precedents to find mirroring profiles. ### 3. Quantitative Calibration * Evaluate numerical data provided (e.g., dollar amounts, supply percentages, timeframes). * Calculate mathematical plausibility (e.g., implicit valuation ranges, supply caps). ### 4. Triangulated Candidates Matrix Construct a Markdown table comparing the top 3-4 candidates using this format: | Candidate | Technical Fit (Pass/Fail) | Narrative Fit (High/Med/Low) | Key Supporting Clue | Overall Probability | ### 5. Self-Verification & Final Hypothesis * Cross-check your top candidate against Step 1 to ensure zero constraint violations. * State your primary hypothesis, assign a Confidence Score (0-100%), and identify the single most critical data point required for 100% confirmation.