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skill-analytics

Aggregate and rank Aeon skills by weekly run counts for fleet analytics.

Updated Jun 3, 2026
Search Tags:#dashboard#observability#cron-scheduling#skill-analytics#run-history#fleet-analytics#exit-taxonomy
One-click install
npx skills add https://github.com/swarm-ai-research/aeon --skill skill-analytics-swarm-ai-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-analytics
Source: https://github.com/swarm-ai-research/aeon/tree/main/skills/skill-analytics
Command: npx skills add https://github.com/swarm-ai-research/aeon --skill skill-analytics-swarm-ai-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Fleet-wide analytics of Aeon skill runs, ranking skills by a specified window and surfacing deploys of success, failures, and anomalies to improve observability.

Core Features & Use Cases

  • Fleet-wide ranking of skill runs by window (e.g., 7d/168h) to identify busiest and most unreliable skills.
  • Anomaly detection by exit taxonomy distribution and silent-scheduled runs to flag degraded fleet health.
  • Dashboard-ready outputs including a fleet article, JSON spec, and a dashboard payload for observability.

Quick Start

Run the skill-analytics to generate the latest fleet analytics report for the default 7-day window.

Frequently Asked Questions about skill-analytics

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

FAQPage Schema
How do I monitor cron-driven skill health across an entire fleet?▼

You monitor fleet health by aggregating weekly run counts and analyzing exit taxonomy from logs. This process cross-references cron schedules and memory state to surface top runners, failure rates, and silent-scheduled skills within a configurable window.

What is exit taxonomy analysis and how does it detect fleet anomalies?▼

Exit taxonomy analysis evaluates log distributions from skill runs to detect degraded fleet health. By categorizing exit statuses, it flags silent-scheduled runs and identifies non-firing skills, providing anomaly detection for fleet-wide observability.

How do I generate a dashboard JSON spec from run history data?▼

Generate a dashboard JSON spec by reading per-skill run snapshots and cross-referencing cron schedules. The output includes a dashboard payload and a fleet article that rank skills by a configurable window to improve observability.

Can I identify silent-scheduled and non-firing skills within a custom time window?▼

Yes, you can identify silent-scheduled and non-firing skills within a configurable window like the default 7-day or 168-hour period. The analytics tool cross-references cron schedules with memory state to flag these anomalies.

Does fleet analytics work without external dependencies for observability?▼

Fleet analytics operates without external dependencies, reading per-skill run snapshots internally. It analyzes exit taxonomy and memory state directly to output a dashboard payload and fleet article for self-contained observability.

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