A systematic diagnostic tool to identify prompt failure patterns, A/B test variations, and generate high-performance optimized instructions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompt-debugger
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 prompt-debugger 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 Prompt Debugger?
The Prompt Debugger is a specialized utility designed to bridge the gap between unexpected AI outputs and desired results. It analyzes the structural integrity of prompts to identify root causes of failure such as ambiguity, conflicting instructions, and missing context. By applying proven engineering principles, this tool helps developers refine their interactions with LLMs for more predictable and accurate performance.
As part of the Openclaw Skills ecosystem, it provides a structured framework for prompt engineering that goes beyond simple trial and error. It offers a static analysis of instructions, allowing users to categorize failure modes—like hallucination or instruction following—and apply targeted fixes to improve the quality of system prompts and agent instructions.
Prompt Debugger Use Cases
- Diagnosing why an AI model is ignoring specific constraints or formatting requirements.
- Iterating on complex system prompts for AI agents to ensure high-fidelity outputs.
- A/B testing different prompt structures to determine which version yields the most consistent results.
- Improving legacy prompts that were designed for older models to work with modern LLMs.
- Scoring prompt quality before deployment to production environments.
How Prompt Debugger Works
- The user provides a failing prompt and, optionally, an example of the undesired output produced by the AI.
- The skill performs a structural analysis to check for ambiguity, internal conflicts, and missing context or constraints.
- It classifies the issue into specific failure modes such as Tone Mismatch, Scope Creep, or Format Violation.
- It generates multiple hypotheses for improvement, creating varied versions of the prompt for A/B testing.
- The tool produces a final, optimized rewrite following industry best practices like the Constraint Sandwich and Chain of Thought patterns.
- A quality score is assigned to the prompt based on dimensions like clarity, specificity, and testability.
Prompt Debugger Setup
To integrate this prompt engineering utility into your workflow, ensure you have the Openclaw Skills environment configured. You can then use the following commands to analyze your prompts:
# Diagnose a failing prompt
openclaw run prompt-debugger diagnose --prompt "[your prompt here]" --output "[failing output]"
# Generate an optimized rewrite
openclaw run prompt-debugger rewrite --prompt "[your prompt here]"
# Score the quality of a prompt
openclaw run prompt-debugger score --prompt "[your prompt here]"
Prompt Debugger Data Schema & Taxonomy
The Prompt Debugger organizes its analysis using structured reports and metadata. The following schema defines the output for diagnostic data:
| Field | Description | Type |
|---|---|---|
| diagnosis | Contains identified issues, failure modes, and proposed fixes | Object |
| rewrite | The final optimized version of the prompt | String |
| score | Numerical rating (0-10) across multiple quality dimensions | Object |
| anti_patterns | List of detected problematic language or structural flaws | Array |
It supports multiple output formats including text for readability, json for programmatic integration, and markdown for documentation.
Prompt Debugger Advanced Features
- Multi-Variation A/B Testing: Automatically generates up to 5 variations of a prompt targeting different fix strategies.
- Anti-Pattern Detection: Scans for hedge language, vague quantifiers, and double negatives that weaken AI instruction following.
- Prompt Pattern Library: Built-in reference for Chain of Thought, Few-Shot, and Persona-Task-Format templates.
- Model-Agnostic Static Analysis: Analyzes the logical structure of prompts without requiring active API calls or execution tokens.
- Constraint Sandwiching: Automatically applies primacy and recency effects by placing critical instructions at the beginning and end of the prompt.
SKILL.md
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