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working-with-intervals

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Analyze time-bounded data with interval insights.

Data & Analytics#time-series#analyze#datasets#OPAL#intervals#durations
Authorrustomax
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This skill helps users analyze interval datasets (with start_time and end_time) to compute durations, filter by time ranges, and compare across grouping fields in OPAL. It clarifies the difference between Intervals, Events, and Resources, enabling accurate time-based analysis.

Core Features & Use Cases

  • Two-timestamp interval handling: operate on start_time and end_time to measure durations.
  • Duration-focused analytics: compute statistics (mean, percentiles) and distribution.
  • Grouping & filtering: aggregate by fields like service, host, or dataset; filter by duration windows or time ranges.
  • Real-world scenarios: analyze distributed traces, batch jobs, and CI/CD runs to identify slow intervals and throughput patterns.

Quick Start

Discover an interval dataset and compute a basic duration distribution in OPAL:

  • Discover: discover_context('pipeline runs')
  • Basic duration: make_col dur:duration / 1s
  • Stats: | statsby count:count(), avg:avg(dur), p95:percentile(dur, 0.95)
  • Long intervals: | filter dur > 5m

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: working-with-intervals
Download link: https://github.com/rustomax/observe-community-mcp/archive/main.zip#working-with-intervals

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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