What is customize-measurement?

A guided setup skill that creates or adjusts an .ai-proficiency.yaml file, which configures how a repository measures AI coding proficiency. It asks about the team's tools, documentation, project structure, and relevant capabilities.

Use it when setting up measurement for a repository, changing thresholds, excluding irrelevant recommendations, or mapping custom documentation files to expected categories.

It prevents the measurement from using unsuitable defaults for your repository. It can hide checks you do not use and recognize your team's own file names and documentation locations.

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/customize-measurement/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/customize-measurement/github.svg)](https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/customize-measurement)

Your own site

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

Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,706 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	$0.00053	$0.01706
Opus 5.5	$0.00021	$0.00682
Sonnet 5.5	$0.00011	$0.00341
Haiku 4.5	$0.00005	$0.00171

Measured 2mo ago against content hash ce2306e5e02d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

customize-measurement 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/customize-measurement/SKILL.md · 248 lines

How it starts

The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Customize Measurement

Generate a customized .ai-proficiency.yaml configuration through a guided interview process.

When to Use

First time setting up measure-ai-proficiency for a repository
Current scoring doesn't match your team's structure
Want to hide irrelevant recommendations (e.g., no MCP, no Gas Town)
Your team uses different file names (e.g., SYSTEM_DESIGN.md instead of ARCHITECTURE.md)

Workflow

Phase 1: Interview

Ask questions in thematic batches of 2-3 questions using AskUserQuestion.

Batch 1: AI Tools

1. Which AI coding assistants does your team use?
   - Claude Code
   - GitHub Copilot
   - Cursor
   - OpenAI Codex
   - Multiple (specify)

2. Is one tool primary, or do you use them equally?

Batch 2: Documentation Conventions

1. Do you use different file names for documentation?
   Examples:
   - SYSTEM_DESIGN.md instead of ARCHITECTURE.md
   - CODING_STANDARDS.md instead of CONVENTIONS.md
   - docs/api/README.md instead of API.md

2. Where does your team store documentation?
   - Root level (ARCHITECTURE.md)
   - docs/ folder
   - documentation/ folder
   - Other location

Batch 3: Focus & Scope

1. Which capabilities are NOT relevant to your team? (Select all that apply)
   - hooks (Claude hooks)
   - commands (Claude slash commands)
   - skills (Agent skills)
   - memory (Memory files like LEARNINGS.md)
   - agents (Multi-agent setup)
   - mcp (MCP server configs)
   - beads (Beads memory system)
   - gastown (Gas Town orchestration)

2. What's your priority focus?
   - documentation (ARCHITECTURE.md, CONVENTIONS.md)
   - skills (Agent skills)
   - testing (Test documentation)
   - architecture (System design docs)
   - All equally

Batch 4: Thresholds & Industry

1. Is the default scoring appropriate for your repo?
   - Too strict (small team/startup - lower thresholds)
   - About right (default thresholds)
   - Too lenient (enterprise - higher thresholds)

2. Any industry-specific patterns to include?
   - FinTech (COMPLIANCE.md, PCI_DSS.md, SECURITY_STANDARDS.md)
   - Healthcare (HIPAA.md, PHI_HANDLING.md)
   - Open Source (GOVERNANCE.md, MAINTAINERS.md)
   - Enterprise (SOC2.md, SECURITY_AUDIT.md)
   - None / General

Read the full file on GitHub · 248 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 · 248 lines · 53 tokens per session scan A ce2306e5e02d

Subscribe to this mod's changes

customize-measurement is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (13 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 1,706 once invoked, about $0.0002 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.

Analytics and metrics Building agents

customize-measurement next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
customize-measurementpskoett/measuring-ai-proficiency	Skill	13	3mo ago	MIT	A	53	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