An intelligent emotional support skill that detects user frustration and provides personalized, motivating responses to keep you productive.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cheer
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 cheer 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 Cheer?
Cheer is a specialized emotional intelligence layer designed for AI coding agents. It serves as a digital companion that monitors user interactions for signs of stress, frustration, or exhaustion and intervenes with tailored encouragement. By analyzing keywords, emojis, and punctuation patterns in real-time, it ensures that developers feel supported during difficult debugging sessions or complex architectural tasks. This utility is a vital addition for users looking to humanize their suite of Openclaw Skills.
The skill operates with a sophisticated detection engine that calculates emotional intensity. Whether a user is experiencing minor annoyance or deep despair, Cheer selects from multiple response styles—including comfort, motivation, humor, and empowerment—to deliver the right message at the right time. It is designed to be lightweight, stateless, and high-performance, fitting seamlessly into existing workflows.
Cheer Use Cases
- Automatically boosting morale when a developer expresses frustration with a persistent bug.
- Providing a manual mental health break via the /cheer command during long work sessions.
- Reducing developer burnout by acknowledging difficult tasks with humor or empowerment.
- Detecting subtle signs of being overwhelmed through emoji and punctuation analysis before a user gives up.
- Enhancing user retention in AI-driven platforms by providing a more empathetic and supportive interface.
How Cheer Works
- Monitoring: The skill scans incoming user messages for a database of 35+ emotion-linked keywords and 15+ emojis.
- Intensity Scoring: It calculates an emotion intensity score on a 0-1 scale based on weighted keyword matching (High, Medium, and Low tiers).
- Categorization: The system classifies the emotional state into specific categories such as despair, confusion, exhaustion, or uncertainty.
- Style Selection: Based on the intensity score, it selects an appropriate response mode. High intensity triggers Comfort mode, while lower scores might trigger Humor or Empowerment.
- Delivery: It returns a structured JSON object containing a warm message, a matching emoji, a follow-up phrase, and detection metadata.
Cheer Setup
To integrate Cheer into your agent environment, ensure the core files (detector.js, index.js, and manifest.json) are present in your skills directory. You can verify the installation and test the emotion detection logic using the included demo script:
node demo.js
Configuration can be adjusted in detector.js to change the 0.3 intensity threshold or to modify keyword weights to better suit your specific Openclaw Skills deployment.
Cheer Data Schema & Taxonomy
The skill generates a structured response object for every trigger, ensuring consistent integration with other Openclaw Skills. The schema is organized as follows:
| Field | Description | Type |
|---|---|---|
message |
The primary encouragement or validation text | String |
emoji |
Visual indicator matching the response style (💚, 🚀, 😄, or 💪) | String |
followUp |
A secondary motivational phrase to keep the user moving | String |
emotionDetected |
The calculated intensity score (normalized 0 to 1) | Number |
responseCategory |
The selected response style (comfort, motivation, humor, or empowerment) | String |
timestamp |
ISO 8601 formatted timestamp of the event | String |
Cheer Advanced Features
- Multi-tier keyword matching with three distinct intensity weights (0.3, 0.6, 0.9) for precise sentiment analysis.
- Adaptive response logic that prioritizes soothing, validating messages in high-distress scenarios (intensity ≥ 0.8).
- Punctuation pattern analysis capable of detecting frustration through excessive exclamation or question marks (3+).
- A diverse message library with over 20 distinct responses per style to prevent repetitive or robotic interactions.
- Thread-safe and stateless design, ensuring low latency (<5ms) and high scalability within Openclaw Skills environments.
SKILL.md
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