ResonanceEngine is a zero-cost algorithmic tool that analyzes conversational micro-signals to optimize AI bot responses for maximum engagement and conversion.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openpaw
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 openpaw 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 ResonanceEngine?
ResonanceEngine is a specialized conversational frequency matching engine built for Openclaw Skills that reads over 15 invisible micro-signals in every interaction. By analyzing patterns like message length trends, hedging language, and sentiment velocity, it computes real-time frequencies that dictate how a bot should respond to achieve peak impact. This tool applies the physics of persuasion to digital agents, allowing them to mirror user behavior and build trust programmatically.
Unlike traditional machine learning models, this component of Openclaw Skills operates with zero API costs and requires no external GPU resources. It provides an invisible advantage by making bots feel more intuitive and responsive. By integrating these Openclaw Skills, developers can transform standard LLM interactions into highly persuasive, resonance-tuned conversations that significantly increase user retention and revenue generation.
ResonanceEngine Use Cases
- High-stakes sales bots that need to identify the exact moment to close based on commitment language.
- Customer support systems utilizing Openclaw Skills to de-escalate tension through style matching and trust building.
- Personalized marketing assistants that adjust their vocabulary complexity to match the user's sophistication level.
- Engagement-focused companion bots designed to maintain long-term user interest by monitoring sentiment trends.
How ResonanceEngine Works
- The engine receives the conversation history and current user message within the Openclaw Skills environment.
- It extracts 15+ micro-signals including question density, topic persistence, and urgency markers.
- The system calculates scores for four primary frequencies: Engagement, Trust, Decision, and Style Match.
- A composite Resonance Level is assigned to the current state of the interaction, ranging from BUILDING to PEAK_RESONANCE.
- The engine generates a tuning recommendation and a prompt injection string to dynamically update the bot's system instructions.
ResonanceEngine Setup
To integrate this engine into your Openclaw Skills projects, install the package via pip:
pip install openpaw
For developers who prefer a manual installation or want to contribute to the core Openclaw Skills repository:
git clone https://github.com/jcools1977/Openpaw-.git
cd Openpaw-
pip install -e .
ResonanceEngine Data Schema & Taxonomy
The ResonanceEngine returns a structured result object that organizes conversation intelligence into five main categories:
| Data Point | Type | Description |
|---|---|---|
| Frequency Profile | Map | Scores (0.0 to 1.0) for Engagement, Trust, Decision, and Style Match. |
| Resonance Level | Enum | Qualitative labels such as PEAK_RESONANCE, BUILDING, or WEAK. |
| Tuning Recommendation | Object | Detailed guidance on response length, style, and specific techniques. |
| Yield Prediction | Object | Conversion probability, estimated value, and optimal turns remaining. |
| Prompt Injection | String | A pre-formatted string designed for system prompt injection in Openclaw Skills. |
ResonanceEngine Advanced Features
- Zero-cost algorithmic intelligence utilizing pure Python text analysis without external API dependencies.
- Real-time conversion probability forecasting and yield prediction to optimize business outcomes.
- Automated prompt injection that dynamically modifies LLM behavior based on resonance levels in Openclaw Skills.
- Sophisticated style matching that detects vocabulary complexity, formality levels, and emotional energy.
- Multi-category signal analysis covering engagement trajectories, trust indicators, and decision-making markers.
SKILL.md
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