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javierhbr
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observe-opal

Generate, explain, and refactor Observe OPAL queries and dataset shaping pipelines.

Updated Feb 17, 2026
Search Tags:#refactoring#logs#data analysis#query#observe#opal
One-click install
npx skills add https://github.com/javierhbr/random-poc --skill observe-opal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: observe-opal
Source: https://github.com/javierhbr/random-poc/tree/main/custom-skills/observe-opal-codex-skill
Command: npx skills add https://github.com/javierhbr/random-poc --skill observe-opal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of writing, understanding, and refactoring Observe OPAL queries, making complex data analysis more accessible.

Core Features & Use Cases

  • Query Generation: Create new OPAL queries based on user requirements.
  • Query Explanation: Break down existing OPAL queries into understandable steps.
  • Query Refactoring: Improve the readability, efficiency, and correctness of OPAL code.
  • Use Case: A user needs to analyze server logs for error rates over the last 24 hours. They can ask the agent to generate an OPAL query to achieve this, and the agent will use this skill to produce the correct query.

Quick Start

Use the observe-opal skill to generate an OPAL query that filters for HTTP 500 errors and shows a 5-minute timechart of counts by service.

Frequently Asked Questions about observe-opal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write OPAL queries to analyze server logs in Observe?▼

To analyze server logs with OPAL queries, you generate dataset shaping pipelines that filter for specific HTTP errors and visualize counts. This skill structures query generation for common log analysis use cases like error rate monitoring.

What is the best way to refactor complex OPAL queries for data shaping?▼

Refactoring OPAL queries involves improving the readability, efficiency, and correctness of your data shaping code. This skill provides a structured approach to break down and optimize existing dataset transformations.

How does an OPAL query pipeline handle time series transformations?▼

An OPAL query pipeline handles time series transformations by applying structured dataset shaping steps. This skill targets common time-based use cases, allowing you to generate queries that aggregate data over specific intervals like 5-minute timecharts.

Can I use this approach to explain existing OPAL queries I didn't write?▼

Yes, you can explain existing OPAL queries by breaking them down into understandable steps. This skill structures the explanation process to make complex data analysis pipelines more accessible for review and troubleshooting.

Do I need prior knowledge of dataset shaping to generate OPAL queries?▼

You do not need extensive prior knowledge of dataset shaping to generate OPAL queries. This skill simplifies complex data analysis by utilizing a 3-layer context model to manage prompts and produce correct query outputs.

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