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.
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/customize-measurement/SKILL.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/skills/pskoett/measuring-ai-proficiency/customize-measurement)<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.
<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>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 | $0.00026 | $0.00853 |
| Sonnet 5 | $0.00011 | $0.00341 |
| Haiku 4.5 | $0.00005 | $0.00171 |
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 10d 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 — 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.mdinstead ofARCHITECTURE.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
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.
- 10d ago First seen · 248 lines · 53 tokens per session scan A ce2306e5e02d
customize-measurement is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (11 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 1,706 once invoked, about $0.0003 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.
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