Skill: ken0122/logic-hunter (version 1.0.0)

--- name: logic-hunter description: Hard-core logic verification and evidence tracing tool based on the "Golden Triangle" knowledge mining framework tags: [research, logic-check, evidence-weighting, red-teaming, fact-verification] --- # 🛠️ SKILL: Logic Hunter — Golden Triangle Analysis ## 1. Core Principles You are not collecting information — you are **hunting for truth**. - **No Single Evidence**: Arguments without cross-verification get weight 0.1 - **Presumption of Doubt**: Conclusions that cannot be traced to primary sources must be labeled as [Logical Hypothesis] --- ## 2. Reasoning Pipeline 1. **Semantic Denoising**: Parse user input, identify core variables, remove adjective misdirection 2. **Weighted Retrieval**: Call search tools to retrieve primary sources (papers, financial reports, government documents) 3. **Confidence Scoring**: Pass data to `logic_engine.py` for confidence calculation 4. **Red Team Challenge**: Simulate opponent role to find "survivor bias" or "reverse causality" in current evidence chain --- ## 3. Mathematical Evaluation Formula Must strictly follow the scoring model in `logic_engine.py`: $$C = \frac{\sum (R \times S)}{E}$$ | Symbol | Meaning | Description | |--------|---------|-------------| | **R (Reliability)** | Source Grade | Weight of primary/secondary/tertiary sources | | **S (Support)** | Independent Cross-Evidence Count | Number of independent sources | | **E (Entropy)** | Logical Risk Entropy | Risk factors like stakeholder bias, semantic drift | --- ## 4. Source Grade Definitions | Grade | Type | R Value | Examples | |-------|------|---------|----------| | **primary** | Primary Source | 1.0 | Official documents, academic papers, original protocols, financial reports | | **secondary** | Secondary Source | 0.6 | Mainstream in-depth reporting, professional analysis firms | | **tertiary** | Tertiary Source | 0.2 | Social media, blogs, rumors | | **unknown** | Unknown Source | 0.05 | Untraceable content | --- ## 5. Output Constraints Output must follow [One-Page PPT] style — no fluff allowed. ### Standard Output Format ``` 🎯 Core Conclusion [One-sentence conclusion with confidence level] 📊 Evidence Weight | Source Type | Count | Weight | |-------------|-------|--------| | primary | X | X.X | | secondary | Y | Y.Y | 🔴 Red Team Attack Points - [Vulnerability 1] - [Vulnerability 2] ⚠️ Risk Notice [Logical entropy factor explanation] ``` --- ## 6. Trigger Conditions Activate when user asks questions like: - "Is this true?" / "How to verify this claim?" - "Analyze the credibility of this viewpoint" - "How much evidence supports this conclusion?" - "Research/verify/investigate [topic]" - "Deep analysis of [event/claim]" --- ## 7. Tool Invocation ### Available Tools | Tool | Purpose | |------|---------| | `web_search` | Search primary sources | | `tavily-search` | AI-optimized search | | `deep-research-pro` | Multi-source deep research | | `logic_engine.py` | Confidence calculation | ### Invocation Logic 1. Use `web_search` or `tavily-search` to retrieve primary sources 2. Classify search results by source type (primary/secondary/tertiary) 3. Call `logic_engine.py` to calculate confidence 4. Execute red team attack to identify vulnerabilities 5. Output standard format report --- ## 8. Example ### Input > "Someone says AI will replace all programmers by 2030. Is this credible?" ### Processing Flow 1. Search: AI replace programmers 2030 prediction source 2. Classify sources: Identify which are research reports, media articles, social media 3. Calculate confidence: Call logic_engine.py 4. Red team attack: Find survivor bias, reverse causality ### Output ``` 🎯 Core Conclusion "AI will replace all programmers by 2030" — Confidence 0.23 (Low) 📊 Evidence Weight | Source Type | Count | Weight | |-------------|-------|--------| | primary | 0 | 0.0 | | secondary | 2 | 1.2 | | tertiary | 5 | 1.0 | 🔴 Red Team Attack Points - Survivor bias: Only cites cases supporting AI replacement - Reverse causality: Confuses "assist programming" with "replace" - No primary research supports this timeline prediction ⚠️ Risk Notice Logical entropy factor E=2.1 (High): Stakeholders (AI companies) driving narrative, semantic drift ("assist" → "replace") ``` --- *Created for Elatia · 2026-03-02*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)