metrics

metrics is a cursor rule for Cursor from ApexIQ/skillsmith. It costs 102 tokens per session, scanned A, original, MIT.

A project workflow for examining how well the project's skills are used and how they perform over time. Metrics are measurements used to understand quality and usage.

In plain words
What is it for?
Use it to analyze skill quality and usage for Click, pytest, and the listed architecture libraries.
Why use it?
It helps identify weak, unused, or inefficient skills instead of relying on guesswork.

Cursor rule for Cursor

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/apexiq/skillsmith/metrics
Clone the repo
git clone --depth 1 https://github.com/ApexIQ/skillsmith

Made for: Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for metrics

README.md
[![agentmods](https://agentmods.dev/badge/rules/apexiq/skillsmith/metrics.svg)](https://agentmods.dev/rules/apexiq/skillsmith/metrics)
Your own site
<a href="https://agentmods.dev/rules/apexiq/skillsmith/metrics"><img src="https://agentmods.dev/badge/rules/apexiq/skillsmith/metrics.svg" alt="Measured on agentmods" height="20"></a>
Per session 102 This file is loaded in full into every session.
When invoked 102 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00102 $0.00102
Opus 5 $0.00051 $0.00051
Sonnet 5 $0.00020 $0.00020
Haiku 4.5 $0.00010 $0.00010

Measured yesterday against content hash 0d2e9ec2f170, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

metrics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/rules/workflows/metrics.mdc · 11 lines

What it actually says

  • Read .agent/workflows/metrics.md.
  • Goal: analyze skill quality and usage metrics for library click, pytest, arch-business-logic, arch-ui, arch-unknown
  • Skills: notebooklm, javascript_testing_patterns, makepad_skills, zapier_make_patterns, prompt_library
  • Follow the workflow steps, then verify with project tests or the closest validation command.
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 11 lines · 102 tokens per session scan A 0d2e9ec2f170

Subscribe to this mod's changes

metrics is a cursor rule published in the GitHub repository ApexIQ/skillsmith (5 stars, last pushed 5mo ago), licensed MIT. It adds 102 tokens to every session, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.