A sophisticated tool designed to detect emotional charge in user prompts and translate them into clear, actionable technical instructions for AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install frustration-translator
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 frustration-translator 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 Frustration Translator?
The Frustration Translator is a critical utility for developers utilizing Openclaw Skills to manage complex AI interactions. It serves as a bridge between high-stress user communication and efficient agent execution by filtering out emotional noise. By identifying signals such as vague blame or compressed expectations, the skill prevents agents from wasting tokens on irrelevant apologies or misinterpreting the core technical request.
This skill is particularly valuable in finite context windows where precision is paramount. It prioritizes urgency and direct action over conversational fluff, ensuring that the Openclaw Skills ecosystem remains responsive even when users are at their most demanding. It transforms emotional outbursts into structured data that the agent can use to find and fix flaws immediately.
Frustration Translator Use Cases
- Managing users who provide vague feedback like this is garbage or this is broken during development.
- Handling situations where compressed expectations such as just fix it obscure the actual technical requirement.
- Optimizing token usage when users repeat complaints about the same issue across multiple messages.
- Interpreting ALL CAPS messages or excessive punctuation as a signal for increased urgency and faster execution.
How Frustration Translator Works
- The skill analyzes the user prompt for specific frustration signals like vague blame, wasted resources, or excessive punctuation.
- It assigns points based on a predefined signal matrix to calculate a frustration score from 0 to 10.
- Based on the score, the skill determines the response mode: normal interaction, translation required, or high-speed execution.
- It utilizes a translation map to find the real meaning behind phrases like do it right this time by scanning previous message history for specific flaws.
- The agent executes a brief acknowledgment and moves immediately to the fix, providing evidence of the resolution to satisfy user urgency.
Frustration Translator Setup
To deploy this logic within your Openclaw Skills implementation, ensure the frustration-translator markdown is accessible to your agent's system prompt. No external API keys are required as the logic relies on pattern matching and scoring thresholds.
# Example of integrating the translation logic into your agent directory
cp frustration-translator.md ./skills/logic/
Ensure your agent's context window is configured to provide at least the last 5 messages to the translation layer for accurate flaw identification.
Frustration Translator Data Schema & Taxonomy
The Frustration Translator organizes its logic through a scoring system and a mapping taxonomy described below:
| Data Component | Format | Description |
|---|---|---|
| Signal Points | Integer | Weights assigned to emotional triggers (e.g., +3 for vague blame, +1 for CAPS). |
| Frustration Score | Range (0-10) | Quantitative measure of user emotional charge to determine response strategy. |
| Translation Map | Key-Value Pair | Correlates emotional phrases (They Say) with technical intentions (They Mean). |
| Response Pattern | Workflow | A sequence of: Brief Ack -> Real Problem Identification -> Fix -> Evidence. |
Frustration Translator Advanced Features
- Contextual flaw identification that scans the previous 3 messages to resolve vague complaints.
- Automated response streamlining that removes caveats and psychoanalysis to match user urgency.
- Dynamic resource conservation by preventing token waste on lengthy apologies in Openclaw Skills workflows.
- Pattern-based logging of frustration triggers to improve long-term agent understanding of user expectations.
SKILL.md
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