# Pskoett Measuring AI Proficiency Customize Measurement > Customize Measurement - Skill: `tomevault-io/pskoett-measuring-ai-proficiency-customize-measurement` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/pskoett-measuring-ai-proficiency-customize-measurement` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/pskoett-measuring-ai-proficiency-customize-measurement/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/pskoett-measuring-ai-proficiency-customize-measurement --- # 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 ``` #### Batch 5: Validation (Optional) ``` 1. Does your documentation mention example file names that don't exist? - Yes (meta-tools, template repos, documentation showing patterns) - No (all file references point to real files) 2. If yes, which patterns should be skipped during validation? - Best practices not yet adopted (.mcp.json, .claude/settings.json) - Industry examples (HIPAA.md, COMPLIANCE.md) - Custom examples (ask user to specify) ``` ### Phase 2: Generate Configuration Based on interview responses, generate `.ai-proficiency.yaml`: ```yaml # .ai-proficiency.yaml # Generated by customize-measurement skill # Customized for: [repo name] # AI tools in use tools: - claude-code # or detected tools # Custom file locations (if different from defaults) documentation: # architecture: "docs/SYSTEM_DESIGN.md" # conventions: "CODING_STANDARDS.md" # api: "docs/api/README.md" # testing: "docs/TESTING_GUIDE.md" # Level thresholds (adjusted for team size) thresholds: level_3: 15 # Comprehensive Context level_4: 12 # Skills & Automation level_5: 10 # Multi-Agent Ready level_6: 8 # Fleet Infrastructure level_7: 6 # Agent Fleet level_8: 5 # Custom Orchestration # Skip irrelevant recommendations skip_recommendations: # - hooks # - commands # - skills # - memory # - agents # - mcp # - beads # - gastown # Focus areas (optional - show only these) # focus_areas: # - documentation # - skills # - testing # Skip validation warnings for documentation examples # skip_validation_patterns: # - "COMPLIANCE.md" # Example mentioned in docs # - ".mcp.json" # Best practice not yet adopted ``` ### Phase 3: Validate After generating the config: ```bash measure-ai-proficiency ``` Verify the output matches expectations. Adjust config if needed. ## Threshold Guidelines | Team Size | Suggested Thresholds | |-----------|---------------------| | Startup (1-5 devs) | level_3: 8, level_4: 6 | | Small (5-20 devs) | level_3: 12, level_4: 10 | | Medium (20-100 devs) | level_3: 15, level_4: 12 (default) | | Enterprise (100+ devs) | level_3: 20, level_4: 15 | ## Industry Patterns ### FinTech ```yaml documentation: compliance: "COMPLIANCE.md" security: "SECURITY_STANDARDS.md" pci: "PCI_DSS.md" ``` ### Healthcare ```yaml documentation: hipaa: "HIPAA.md" phi: "PHI_HANDLING.md" security: "SECURITY_COMPLIANCE.md" ``` ### Open Source ```yaml documentation: governance: "GOVERNANCE.md" maintainers: "MAINTAINERS.md" contributing: "CONTRIBUTING.md" ``` ## Example Session ``` User: /customize-measurement Claude: I'll help you create a customized .ai-proficiency.yaml for your repository. Let me ask a few questions to understand your setup. [AskUserQuestion - AI Tools batch] 1. Which AI coding assistants does your team use? 2. Is one the primary tool? User: We use Claude Code primarily, with some GitHub Copilot usage. [AskUserQuestion - Documentation batch] 1. Do you use different file names for documentation? 2. Where do you store docs? User: We use docs/DESIGN.md instead of ARCHITECTURE.md. Everything is in docs/. [AskUserQuestion - Focus batch] 1. Which capabilities aren't relevant? 2. What's your priority focus? User: We don't use MCP or Gas Town. Focus on documentation and testing. [AskUserQuestion - Thresholds batch] 1. Is default scoring appropriate? 2. Any industry patterns? User: We're a small startup, defaults are too strict. No special industry. Claude: Based on your responses, here's your customized config: [Generates .ai-proficiency.yaml with: - tools: claude-code, github-copilot - documentation.architecture: docs/DESIGN.md - thresholds lowered for startup - skip_recommendations: mcp, gastown - focus_areas: documentation, testing] Run `measure-ai-proficiency` to validate. ``` ## Quick Reference Triggers: - "Customize AI proficiency measurement" - "Set up .ai-proficiency.yaml" - "Configure measurement for my repo" - "The scoring doesn't match my setup" --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/pskoett) — claim your Tome and manage your conversions.