using-kpi

Analyze specialist KPI data from observability SQLite databases.

4|1|Updated Nov 1, 2025
One-click install
npx skills add https://github.com/xtrm-dev/specialists --skill using-kpi
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: using-kpi
Source: https://github.com/xtrm-dev/specialists/tree/main/config/skills/using-kpi
Command: npx skills add https://github.com/xtrm-dev/specialists --skill using-kpi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you analyze specialist KPI data from observability SQLite so you can understand runtime cost, payload growth, waiting behavior, and outliers without manually stitching together queries.

Core Features & Use Cases

  • Leaderboard analysis: Rank specialists and models by active runtime, total runtime, turns, tools, and payload size.
  • Outlier detection: Identify jobs above p95 for runtime, tool calls, turns, or payload bloat.
  • Operational hygiene: Inspect waiting-state issues, payload-to-runtime correlation, and tool-call distributions for performance tuning.
  • Use case: Use it to compare which specialist/model combinations are most expensive and which jobs need prompt or workflow optimization.

Quick Start

Ask the skill to analyze specialist KPI data from sp db stats or sp db extract and return the most important performance, waiting, payload, and outlier findings.

Frequently Asked Questions about using-kpi

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

FAQPage Schema
How do I analyze specialist KPI data from SQLite observability databases?

To analyze specialist KPI data from SQLite observability databases, you can query sp db stats or sp db extract workflows to rank active runtime, detect outliers, and inspect tool-call distributions for performance tuning.

What is the best way to detect runtime outliers and payload bloat in observability data?

Detecting runtime outliers and payload bloat involves identifying jobs above the p95 threshold for runtime, tool calls, turns, and payload size using SQLite query patterns applied to your observability database.

Can I compare specialist and model performance by active runtime and total turns?

Yes, you can compare specialist and model performance by generating a leaderboard that ranks combinations by active runtime, total runtime, turns, tools, and payload size extracted from SQLite.

How do I inspect waiting-state issues and payload-to-runtime correlation in SQLite?

Inspecting waiting-state issues and payload-to-runtime correlation requires applying SQLite query patterns to observability data to evaluate operational hygiene and identify areas needing prompt or workflow optimization.

Does KPI runtime analysis require specific token estimation methods?

Yes, specialist KPI runtime analysis requires cl100k_base-style token estimation alongside active runtime ranking and SQLite query patterns to accurately evaluate leaderboard, hygiene, and component breakdown metrics.

When should I use SQLite query patterns for specialist KPI performance tuning?

Use SQLite query patterns for specialist KPI performance tuning when you need to understand runtime cost, payload growth, and waiting behavior without manually stitching together complex database queries.