A high-precision logic verification tool for Openclaw Skills that uses the Golden Triangle framework to hunt for truth and score evidence credibility.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install logic-hunter
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install logic-hunter using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Logic Hunter?
Logic Hunter is a specialized verification skill designed for high-stakes research and investigative tasks. Built on the Golden Triangle knowledge mining framework, it moves beyond simple information retrieval to actively hunt for truth. It operates under a presumption of doubt, requiring rigorous cross-verification for all claims to ensure maximum accuracy.
By integrating this into Openclaw Skills, developers and researchers can transform their AI agents into rigorous fact-checkers. The tool is engineered to identify logical fallacies, survivor bias, and semantic drift, providing a mathematical basis for trust in an era of information overload.
Logic Hunter Use Cases
- Verifying the credibility of specific claims, viewpoints, or viral news
- Investigating primary sources behind financial reports or official documents
- Conducting deep academic research with weighted evidence scoring
- Red-teaming arguments to identify logical gaps and reverse causality
- Evaluating the risk of narrative manipulation in large datasets using Openclaw Skills
How Logic Hunter Works
- Semantic Denoising: The skill parses user input to identify core variables while removing misleading adjectives.
- Weighted Retrieval: It triggers search tools to retrieve primary sources such as papers, financial reports, and original protocols.
- Source Grading: Evidence is automatically classified into primary, secondary, or tertiary categories.
- Confidence Scoring: The system passes data to a logic engine to calculate a mathematical confidence score ($C$) based on reliability, support, and entropy.
- Red Team Challenge: It simulates an opponent role to find survivor bias or logical vulnerabilities in the current evidence chain.
- Standardized Reporting: Generates a concise, one-page summary with evidence weights and risk notices for Openclaw Skills users.
Logic Hunter Setup
To deploy Logic Hunter in your Openclaw Skills environment, ensure you have the logic engine dependencies installed and your search API credentials configured.
# Add the logic hunter skill to your agent
openclaw install logic-hunter
# Ensure the logic engine environment is ready
python -m pip install -r requirements_logic_engine.txt
Logic Hunter Data Schema & Taxonomy
Logic Hunter organizes its analysis using a strict taxonomy of evidence reliability and mathematical entropy.
| Attribute | Description | Value Range |
|---|---|---|
| Primary Source | Official docs, academic papers, original protocols | 1.0 Reliability |
| Secondary Source | Professional analysis, mainstream in-depth reporting | 0.6 Reliability |
| Tertiary Source | Social media, blogs, rumors | 0.2 Reliability |
| Logical Entropy (E) | Measure of risk factors like stakeholder bias | Variable |
| Support (S) | Number of independent cross-evidence sources | Integer |
Logic Hunter Advanced Features
- Golden Triangle knowledge mining framework for high-fidelity truth hunting
- Automated red team simulation to identify survivor bias and semantic drift
- Mathematical evaluation formula for objective confidence scoring
- Multi-tool orchestration involving web search and deep research protocols within Openclaw Skills
- Real-time logical risk entropy calculation to flag stakeholder bias
SKILL.md
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