What is measuring-ai-proficiency copilot-instructions.md?

Contributor instructions for measure-ai-proficiency, a command-line tool that scans repositories for AI instruction files and rates their maturity across eight levels. It can report results as terminal text, JSON, Markdown, or CSV.

Updating repository scanning, cross-reference detection, maturity scoring, output formats, remote GitHub scanning, and automatic tool detection.

They explain the scanner's structure and supported inputs so changes fit the existing design. They also make it easier to add new instruction-file patterns without breaking reports.

How pskoett/measuring-ai-proficiency configures GitHub Copilot

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/.github/copilot-instructions.md

Clone the repo

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

Made for: GitHub Copilot.

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/instructions/pskoett/measuring-ai-proficiency/copilot-instructions/github.svg)](https://agentmods.dev/instructions/pskoett/measuring-ai-proficiency/copilot-instructions)

Your own site

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

Per session 1,459 This file is loaded in full into every session.

When invoked 1,459 The same file — it is already loaded in full.

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	$0.01459	$0.01459
Opus 5.5	$0.00584	$0.00584
Sonnet 5.5	$0.00292	$0.00292
Haiku 4.5	$0.00146	$0.00146

Measured 2mo ago against content hash c6a476a55b7b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

measuring-ai-proficiency copilot-instructions.md 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.

.github/copilot-instructions.md · 165 lines

How it starts

The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Copilot Instructions for measure-ai-proficiency

This is a CLI tool for measuring AI coding proficiency based on context engineering artifacts.

Project Overview

The tool scans repositories for AI context files (like CLAUDE.md, .cursorrules, .github/copilot-instructions.md, and skill files) and calculates a maturity score based on an 8-level model (1-8) aligned with Steve Yegge's stages.

Key Features:

8-level maturity scoring
Cross-reference detection between AI instruction files
Content quality evaluation (sections, commands, constraints)
Multiple output formats (terminal, JSON, markdown, CSV)
GitHub CLI integration for scanning repos without cloning (--github-repo, --github-org)

Architecture

measure_ai_proficiency/
├── __init__.py        # Package exports
├── __main__.py        # CLI entry point
├── config.py          # Level definitions and file patterns
├── scanner.py         # Repository scanning logic + cross-reference detection
├── github_scanner.py  # GitHub CLI integration for remote scanning
├── reporter.py        # Output formatting (terminal, JSON, markdown, CSV)
└── repo_config.py     # Repository configuration and tool auto-detection

scripts/
├── find-org-repos.sh  # GitHub org discovery script (uses gh CLI + jq)
└── README.md          # Script documentation

Key Patterns

Adding New File Patterns

To add detection for new AI context files, edit measure_ai_proficiency/config.py:

# Add to appropriate level (1-8) in the LevelConfig
file_patterns=[
    "your-new-pattern.md",
    ".your-tool/config/*.md",
]

Skill Locations (Agent Skills Standard)

All major AI tools now support the Agent Skills open standard:

Claude Code: .claude/skills/*/SKILL.md
GitHub Copilot: .github/skills/*/SKILL.md
Cursor: .cursor/skills/*/SKILL.md
OpenAI Codex: .codex/skills/*/SKILL.md

Output Formats

The tool supports: terminal (default), JSON, markdown, CSV. Add new formats in measure_ai_proficiency/reporter.py.

Read the full file on GitHub · 165 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 · 165 lines · 1,459 tokens per session scan A c6a476a55b7b

Subscribe to this mod's changes

measuring-ai-proficiency copilot-instructions.md is an instructions file published in the GitHub repository pskoett/measuring-ai-proficiency (13 stars, last pushed 3mo ago), licensed MIT. It adds 1,459 tokens to every session, about $0.0058 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

measuring-ai-proficiency copilot-instructions.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
measuring-ai-proficiency copilot-instructions.mdpskoett/measuring-ai-proficiency	Instructions file	13	3mo ago	MIT	A	1,459	GitHub Copilot
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

measuring-ai-proficiency: Instructions file for GitHub Copilot