Error Message Improver for Openclaw

Transform vague application errors into clear, actionable diagnostics that explain what failed, why it failed, and how to fix it.

kyro-ma
v0.1.0
Jun 20, 2026
0
390
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install error-message-improver-151347

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install error-message-improver-151347 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 Error Message Improver?

The Error Message Improver is designed to eliminate the frustration and lost time caused by cryptic application errors. Acting as an essential tool among Openclaw Skills, this agent-driven capability guides developers, support engineers, and SaaS operators through transforming raw, vague exceptions into structured, user-friendly feedback. By delivering the critical context of what failed, why it broke, and the next immediate resolution step, it bridges the gap between engineering complexities and clear customer success.

Built upon validated software troubleshooting demand, this skill emphasizes local-hardware-friendly implementations. Instead of forcing heavy cloud computing dependencies, it prioritizes resource-efficient workflows, repeatable checklists, and precise templates to quickly overhaul debugging pipelines and dramatically reduce support ticket escalation.

Error Message Improver Use Cases

  • Standardizing unhelpful legacy stack traces into user-friendly error boundaries.
  • Creating precise developer guidelines and checklists for consistent error tracking.
  • Optimizing SaaS application feedback to lower the volume of repetitive customer support requests.
  • Generating local-hardware-safe validation scripts to catch poorly formatted logs before deployment.

How Error Message Improver Works

  1. Analyze Inputs & Constraints: The skill restates the user's specific debugging outcome, environmental constraints, and available logs or exception structures.
  2. Formulate a Lean Work Plan: It outlines a concise template, automation path, or decision framework to mitigate manual coordination overhead.
  3. Assess Information Gaps: The agent flags missing contextual details that would materially alter the output, making logical engineering assumptions for the rest to maintain velocity.
  4. Execute Local-First Optimizations: Using CPU-safe approaches, it builds the refined script, pattern, or error handling architecture locally.
  5. Generate Artifacts: The final operational code blocks, checklists, or error schemas are produced.
  6. Validate and Risk-Assess: The output is run against standard production safety criteria, explicitly mapping remaining limits or next steps.

Error Message Improver Setup

To install and enable this capability within your Openclaw Skills workspace, reference the skill in your project definition or trigger it directly via CLI configurations:

# Initialize the skill directory within your local ecosystem
openclaw install error-message-improver

# Run a local validation test with a sample cryptic error log
openclaw run error-message-improver --input="An unhandled exception of type 'NullReferenceException' occurred."

Error Message Improver Data Schema & Taxonomy

The skill manages localized metadata and tracking logs using standard Markdown and schema layouts to remain transparent:

Attribute Format Purpose
demand_score Metric String (100/100) Measures feature justification and local feasibility.
triggers Array of Strings Keywords like debugging or error messages that activate the skill.
references/ Markdown Directory Storage for source evidence, planning details, and local review criteria.

Error Message Improver Advanced Features

  • Local Hardware Prioritization: Fully optimized to leverage small models and CPU-bound execution environments without remote cloud dependencies.
  • Signal-Validated Automation: Formulated on real-world engineering friction data to solve widespread diagnostic blind spots.
  • Self-Verifying Workflows: Every execution includes embedded verification notes detailing exactly how the final output satisfies production criteria.

SKILL.md


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