What is measure-ai-proficiency?

A tool that assesses how ready a code repository is for AI-assisted development. It checks guidance files such as CLAUDE.md, .cursorrules, and Copilot instruction files.

Use it to scan a GitHub repository or organisation, measure its AI-coding readiness, and produce recommendations or reports.

It shows whether an AI coding assistant has enough project context and identifies gaps that can lead to weaker or inconsistent changes.

How pskoett/measuring-ai-proficiency configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/measure-ai-proficiency/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/pskoett/measuring-ai-proficiency

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/measure-ai-proficiency/github.svg)](https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/measure-ai-proficiency)

Your own site

<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/measure-ai-proficiency"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/measure-ai-proficiency/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/measure-ai-proficiency"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/measure-ai-proficiency.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 109 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,952 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00109	$0.01952
Opus 5.5	$0.00044	$0.00781
Sonnet 5.5	$0.00022	$0.00390
Haiku 4.5	$0.00011	$0.00195

Measured 2mo ago against content hash bcae0805e14b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

measure-ai-proficiency 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 2mo ago.

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.

.claude/skills/measure-ai-proficiency/SKILL.md · 239 lines

How it starts

The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Measure AI Proficiency

Assess repository context engineering maturity and provide actionable recommendations for improving AI collaboration.

This skill works with Claude Code, GitHub Copilot, Cursor, and OpenAI Codex (via the Agent Skills open standard).

Prerequisites

Install the measure-ai-proficiency tool:

pip install measure-ai-proficiency

Workflow

1. Choose Your Scanning Method

Option A: Scan GitHub Directly (No Cloning Required!)

Scan GitHub repositories without cloning them:

# Scan a single GitHub repository
measure-ai-proficiency --github-repo owner/repo

# Scan entire GitHub organization
measure-ai-proficiency --github-org your-org-name

# Limit number of repos scanned
measure-ai-proficiency --github-org your-org-name --limit 50

# Output to file
measure-ai-proficiency --github-org your-org --format json --output report.json

Requirements: GitHub CLI (gh) authenticated with gh auth login

How it works:

Uses GitHub API to fetch repository file tree
Downloads only AI proficiency files (CLAUDE.md, .cursorrules, skills, etc.)
Scans in temporary directories
Cleans up automatically
Much faster than cloning!

Option B: Discover Then Clone (Traditional Method)

For organizations wanting more control, first discover which repos have AI context artifacts:

# Find active repos (commits in last 90 days) with AI context files
./scripts/find-org-repos.sh your-org-name

# JSON output for automation
./scripts/find-org-repos.sh your-org-name --json > repos.json

What you get:

Total repos in organization
Active repos (with recent commits)
Repos with AI context artifacts (CLAUDE.md, AGENTS.md, .cursorrules, etc.)
Percentage baseline for your org
List of repos to scan

Requirements: GitHub CLI (gh) and jq

Then clone and scan the identified repos.

Option C: Scan Local Repositories

Read the full file on GitHub · 239 lines

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.

2mo ago First seen · 239 lines · 0 tokens per session scan A bcae0805e14b

Subscribe to this mod's changes

measure-ai-proficiency is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (13 stars, last pushed 3mo ago), licensed MIT. It adds 109 tokens to every session and 1,952 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-31.

Building agents

measure-ai-proficiency next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
measure-ai-proficiencypskoett/measuring-ai-proficiency	Skill	13	3mo ago	MIT	A	109	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	today	MIT	A	103	Claude Code

measuring-ai-proficiency: Skill for Claude Code