dvcrn 🛠️ 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
- Semantic Denoising: Parse user input, identify core variables, remove adjective misdirection
- Weighted Retrieval: Call search tools to retrieve primary sources (papers, financial reports, government documents)
- Confidence Scoring: Pass data to
logic_engine.pyfor confidence calculation - 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
- Use
web_searchortavily-searchto retrieve primary sources - Classify search results by source type (primary/secondary/tertiary)
- Call
logic_engine.pyto calculate confidence - Execute red team attack to identify vulnerabilities
- Output standard format report
8. Example
Input
"Someone says AI will replace all programmers by 2030. Is this credible?"
Processing Flow
- Search: AI replace programmers 2030 prediction source
- Classify sources: Identify which are research reports, media articles, social media
- Calculate confidence: Call logic_engine.py
- 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