skill-usage_skill

This skill analyzes installed skills usage over a chosen period and presents a concise, visually appealing report.
  • Python

0

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill akira82-ai/skill-usage --skill skill-usage

  • README.md2.2 KB
  • SKILL.md3.3 KB

Overview

This skill counts how often installed skills were used during a chosen time range and displays the results in a clean TUI-style report. It produces per-skill call counts, ranked frequency, totals, and simple trend visuals so you can quickly see which skills you rely on most. The output is formatted as a readable table with inline bar charts for immediate insight.

How this skill works

The skill scans installed skill definitions and parses available conversation history to find records that indicate skill invocations. It filters events by timestamp to the requested time window, aggregates calls per skill, computes totals and relative frequencies, and sorts results by usage. The final report is rendered as a tidy table with ASCII/Unicode bar visuals and a summary line.

When to use it

  • After a period of active use to discover which skills you use most
  • Before cleaning up or reorganizing installed skills to identify candidates for removal
  • When auditing activity across projects to compare per-project versus global usage
  • To generate periodic usage snapshots (daily, weekly, monthly, quarterly, or all-time)

Best practices

  • Choose a time window that matches the cadence you want to analyze (week, month, quarter)
  • Include both global and project conversation history so usage counts are comprehensive
  • Run the report regularly to track trends and detect shifts in which skills are favored
  • Treat zero-use entries as candidates for pruning, but validate before removing
  • Keep history data intact long enough to analyze multi-month trends if needed

Example use cases

  • Run a 30-day report to see which skill handled the most tasks this month
  • Compare past-7-days vs past-30-days to detect rising or falling usage patterns
  • Produce an all-time leaderboard before deciding which community skills to keep
  • Audit usage across project sessions to allocate maintenance effort where it matters

FAQ

Common ranges include today, past 7 days, past 30 days, past 90 days, and all-time, so you can focus on short-term or long-term usage.

How are skill invocations identified?

Invocations are detected by matching skill invocation markers in conversation records and mapping them to installed skill names, then aggregating by timestamp.

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