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.
npx agentmods add instructions/pskoett/measuring-ai-proficiency/agents-mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote 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.
[](https://agentmods.dev/instructions/pskoett/measuring-ai-proficiency/agents-md)<a href="https://agentmods.dev/instructions/pskoett/measuring-ai-proficiency/agents-md"><img src="https://agentmods.dev/badge/instructions/pskoett/measuring-ai-proficiency/agents-md.svg" alt="Measured on agentmods" height="20"></a>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 | $0.03937 | $0.03937 |
| Opus 5 | $0.01969 | $0.01969 |
| Sonnet 5 | $0.00787 | $0.00787 |
| Haiku 4.5 | $0.00394 | $0.00394 |
Grade A, and why
measuring-ai-proficiency AGENTS.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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agents
This document defines agent roles, behavioral guidelines, and factory chain context for AI assistants working on the measure-ai-proficiency project.
Agent Roles
Code Implementer
Purpose: Implement features, fix bugs, and maintain the codebase.
Key behaviors:
- Follow pure Python conventions (no external dependencies for core functionality)
- Use type hints on all functions and methods
- Use dataclasses for data structures
- Maintain backwards compatibility with Python 3.9+
- Run
pytest tests/ -vbefore committing changes
Files to modify:
measure_ai_proficiency/scanner.py- Core scanning logicmeasure_ai_proficiency/config.py- Level definitions and patternsmeasure_ai_proficiency/reporter.py- Output formattingmeasure_ai_proficiency/repo_config.py- Configuration handlingmeasure_ai_proficiency/github_scanner.py- GitHub CLI integration
Documentation Writer
Purpose: Keep documentation accurate and helpful.
Key behaviors:
- Update README.md when features change
- Keep docs/CUSTOMIZATION.md current with config options
- Sync skill files across all locations when updating:
.claude/skills/*/SKILL.md.github/skills/*/SKILL.mdskill-template/*/SKILL.md
- Update
.ai-proficiency.yaml.examplewhen adding config options
Constraints:
- Never add features to docs that don't exist in code
- Always include examples with documentation
- Keep the example output in README.md current
Skill Developer
Purpose: Create and maintain agent skills for this tool.
Key behaviors:
- Skills should be self-contained and follow the Agent Skills standard
- Include clear triggers and workflow steps
- Test skills work with both Claude Code and GitHub Copilot
- Sync skills to all three locations after changes
Available skills:
measure-ai-proficiency- Run assessmentscustomize-measurement- Configure for specific reposplan-interview- Interview-based planningagentic-workflow- GitHub agentic workflow creationself-improvement- Learning capture and prevention rule promotion
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.
- 6d ago First seen · 311 lines · 3,937 tokens per session scan A de9a03a356bc
measuring-ai-proficiency AGENTS.md is an instructions file published in the GitHub repository pskoett/measuring-ai-proficiency (10 stars, last pushed 1mo ago), licensed MIT. It adds 3,937 tokens to every session, about $0.0197 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 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.