Measure AI Proficiency

A CLI tool for measuring AI coding proficiency based on context engineering artifacts.

The Problem

Adoption ≠ proficiency. Some developers save 10+ hours a week with AI. Others get slower with the same tools.

How do you measure actual AI proficiency?

The Solution

Measure context engineering. Look at whether teams are creating files like CLAUDE.md, .cursorrules, .github/copilot-instructions.md, and AGENTS.md. These artifacts indicate that someone has moved beyond treating AI as fancy autocomplete and started deliberately shaping how AI understands their work.

This tool scans repositories for context engineering artifacts and calculates a maturity score based on an 8-level model aligned with Steve Yegge's stages.

Since v0.6.0 it also detects 2026 context-engineering signals — verification, hooks, eval loops, telemetry, anti-drift maintenance hygiene, dynamic-workflow orchestration, plugins, and harness engineering — and uses them to gate Levels 6-8 (file coverage alone is no longer enough). It also flags bloated always-on files (a permanent context tax). See docs/SIGNALS.md and docs/AGENTS_FILE_GUIDANCE.md (how to write concise, behavioral CLAUDE.md/AGENTS.md). All detection is grounded in official documentation.

Since v0.7.0 it can also prove what your context actually does, not just what exists: measure-ai-proficiency --prove runs a report-only efficacy pass (do documented commands resolve? are hooks wired? what's the always-on token budget?) and reports a separate Efficacy Score with reproducible green/red evidence — resolve-only by default; --prove-exec runs commands/hooks in a hardened sandbox (local repos only). See docs/EFFICACY.md.

Quick Start

# Install from PyPI
pip install measure-ai-proficiency

# Run on any repository
cd /path/to/your-project
measure-ai-proficiency

That's it! The tool scans for files like CLAUDE.md, .cursorrules, .github/copilot-instructions.md, and calculates a maturity score.

MCP Server - AI Context Awareness 🚀

NEW: Make your AI assistant aware of its own proficiency! The MCP server brings real-time AI context analysis directly into Claude Code, Cursor, and other MCP-compatible tools.

Quick Setup

# 1. Install (if not already installed)
pip install measure-ai-proficiency

# 2. Add to your .mcp.json
{
  "mcpServers": {
    "measure-ai-proficiency": {
      "command": "measure-ai-proficiency-mcp"
    }
  }
}

# 3. Restart Claude Code

What You Can Do

Ask your AI assistant:

"What's my AI proficiency level?" → Real-time analysis
"Are my cross-references valid?" → Validate CLAUDE.md links
"How can I reach Level 6?" → Get specific recommendations
"Scan all repos in my org" → Organization-wide assessment

Available MCP Tools

scan_current_repo - Analyze current repository
get_recommendations - Get improvement suggestions
check_cross_references - Validate AI context file links
get_level_requirements - Show next level requirements
scan_github_repo - Analyze remote GitHub repo
scan_github_org - Scan entire GitHub organization
validate_file_quality - Check specific file quality
check_harness_orchestration_quality - 2026 harness/orchestration maturity + L6-L8 gates
scan_for_maintenance_hygiene - Anti-drift maintenance hygiene
get_dynamic_workflow_recommendations - Adopt Dynamic Workflows + verification
curricula_alignment - Official learning on-ramp references
cheapest_primitive_decision_tree_report - Primitive decision discipline
prove_efficacy - Prove the repo's artifacts work (Efficacy Score; resolve-only or sandboxed)

📖 Full MCP Documentation - Setup, examples, troubleshooting

Why use MCP? Creates a meta-improvement loop where the tool that measures AI proficiency becomes AI-accessible, enabling real-time feedback and guided improvements.

What You'll See

============================================================
 AI Proficiency Report: my-project
============================================================

  Overall Level: Level 5: Multi-Agent Ready
  Overall Score: 59.8/100
  AI Tools: Claude Code, GitHub Copilot

  Level Breakdown:
    ✓ Level 1: Zero AI            [████████████████████] 100.0%
    ✓ Level 2: Basic Instructions [█████████████░░░░░░░] 66.7%
    ✓ Level 3: Comprehensive      [███|░░░░░░░░░░░░░░░░] 19.0%/15% ✓
    ✓ Level 4: Skills & Automation[██|░░░░░░░░░░░░░░░░░] 14.5%/12% ✓
    ✓ Level 5: Multi-Agent Ready  [██|░░░░░░░░░░░░░░░░░] 13.9%/10% ✓
    ○ Level 6: Fleet Infrastructure [░░░░░░░░░░░░░░░░░░] 0.0%/8% needs +8%

  Validation Warnings:
    📋 TEMPLATE: CLAUDE.md contains template markers (your-org-name)
    🪶 BLOAT: CLAUDE.md is 2100 words (> 1500) — large always-on files are a
       permanent context tax; move detail into scoped files/skills

    Penalty: -2.7 points

  Context Engineering Signals (2026):

    Structural quality: 8.0/10  (skills:17 hooks:yes subagents:6 workflows:no plugins:no)

    ✓ Verification Discipline — verify, adversarial, asserts
    ✓ Deterministic Hooks — PreToolUse, PostToolUse
    ✓ Eval / Regression Loops — .evals/
    ✓ Anti-Drift Maintenance Hygiene — sentinel, audit
    ○ Plugin Distribution
    ○ Measured Outcomes

    ✓ L6 signal gate satisfied
    ✓ L7 signal gate satisfied
    ○ L8 signal gate: missing plugins, measured_outcomes

    Signal Bonus: +10.0

  Recommendations:
    → ✅ For Level 8: add plugin distribution (.claude-plugin manifest). See https://docs.claude.com/en/docs/claude-code/plugins
    → 🪶 Trim your always-on files (CLAUDE.md): every always-loaded line must change
         behavior, or cut it. See docs/AGENTS_FILE_GUIDANCE.md
    → 🔄 Add workflows/ for multi-step process definitions

Note: since v0.6.0, Levels 6–8 require the matching context-engineering signals (not just file coverage) — so a repo can have strong signals (L6/L7 gates satisfied) yet still sit at Level 5 until its higher-level file coverage catches up, as above.

Installation

pip install measure-ai-proficiency

Or install from source:

git clone https://github.com/pskoett/measuring-ai-proficiency.git
cd measuring-ai-proficiency
pip install -e .

Usage

The tool supports two scanning modes - choose the one that fits your workflow:

Mode 1: Local Scanning (Default)

Scan repositories on your local disk. Works offline, no authentication needed.

# Scan current directory
measure-ai-proficiency

# Scan specific repository
measure-ai-proficiency /path/to/repo

# Scan multiple repositories
measure-ai-proficiency /path/to/repo1 /path/to/repo2

# Scan all repos in a directory (cloned GitHub org)
measure-ai-proficiency --org /path/to/cloned-org

Best for:

Local development
Offline scanning
Repos already on disk
No GitHub authentication needed

Mode 2: GitHub CLI Scanning (Optional)

Scan GitHub repositories directly without cloning them using the GitHub CLI:

# Scan a single GitHub repository
measure-ai-proficiency --github-repo owner/repo

# Scan an entire GitHub organization
measure-ai-proficiency --github-org anthropic

# Limit the number of repos scanned
measure-ai-proficiency --github-org anthropic --limit 50

# Combine with output formats
measure-ai-proficiency --github-org your-org --format json --output report.json

Best for:

Scanning organizations without cloning
Large-scale analysis
Private repos (with authentication)
Quick assessments of remote repos

Requirements:

GitHub CLI (gh) must be installed
Must be authenticated: gh auth login

How it works:

Uses GitHub API to fetch repository file tree
Downloads only relevant AI proficiency files (no full clone!)
Scans the files locally in a temporary directory
Cleans up automatically after scanning

Advantages over cloning:

✅ Much faster (only downloads relevant files)
✅ No disk space needed for full repositories
✅ Works with hundreds of repos efficiently
✅ Automatic cleanup after scanning

Choosing Between Modes

Feature	Local Scanning	GitHub CLI Scanning
Setup	None	Requires `gh` CLI + auth
Network	Works offline	Requires internet
Speed	Fast for local repos	Fast for remote repos
Disk Space	Uses existing repos	Minimal (temp files only)
Authentication	Not needed	GitHub auth required
Private Repos	Must clone first	Works if authenticated
Best Use	Local development	Organization-wide analysis

Note: Both scanning modes support all features:

All output formats (terminal, JSON, markdown, CSV)
All CLI flags (--format, --output, -q, --min-level)
Cross-reference detection and quality scoring
Tool auto-detection
Custom configuration (.ai-proficiency.yaml)

Output Formats

Both scanning modes work with all output formats:

# Terminal output (default, with colors)
measure-ai-proficiency

# JSON output
measure-ai-proficiency --format json

# Markdown report
measure-ai-proficiency --format markdown

# CSV (for spreadsheets)
measure-ai-proficiency --format csv

# Save to file
measure-ai-proficiency --format markdown --output report.md

Other Options

# Quiet mode (summary only)
measure-ai-proficiency -q

# Filter by minimum level
measure-ai-proficiency --org /path/to/org --min-level 2

Maturity Levels

The tool measures maturity on an 8-level scale aligned with Steve Yegge's stages:

Level	Name	Description
1	Zero AI	No AI-specific files (baseline)
2	Basic Instructions	Basic context files (CLAUDE.md, .cursorrules, etc.)
3	Comprehensive Context	Architecture, conventions, patterns documented
4	Skills & Automation	Hooks, commands, memory files, workflows
5	Multi-Agent Ready	Multiple agents, MCP configs, handoffs
6	Fleet Infrastructure	Beads, shared context, workflow pipelines
7	Agent Fleet	Governance, scheduling, 10+ agents
8	Custom Orchestration	Gas Town, meta-automation, frontier

Supported Tools

The tool auto-detects and supports all major AI coding tools:

Claude Code: CLAUDE.md, AGENTS.md, .claude/
GitHub Copilot: .github/copilot-instructions.md, .github/agents/, .github/skills/
Cursor: .cursorrules, .cursor/rules/, .cursor/skills/
OpenAI Codex CLI: CODEX.md, .codex/, AGENTS.md
VSCode AI: .vscode/*.md

Smart Scanning: Automatically excludes node_modules/, venv/, dist/, build/, and other dependency folders.

Example Output

Terminal

============================================================
 AI Proficiency Report: measuring-ai-proficiency
============================================================

  Overall Level: Level 5: Multi-Agent Ready
  Overall Score: 60.1/100
  AI Tools: Claude Code, GitHub Copilot, OpenAI Codex

  Level Breakdown:

    ✓ Level 1: Zero AI
      [████████████████████] 100.0%  (1 files)
        ● README.md

    ✓ Level 2: Basic Instructions
      [███████████████░░░░░] 75.0%  (3 files)
        ● CLAUDE.md
        ● AGENTS.md
        ● .github/copilot-instructions.md

    ✓ Level 3: Comprehensive Context
      [███|░░░░░░░░░░░░░░░░] 18.8%/15% ✓ (62 files)
        ● .github/PULL_REQUEST_TEMPLATE.md
        ● .github/copilot-instructions.md
        ... and 60 more

    ✓ Level 4: Skills & Automation
      [██|░░░░░░░░░░░░░░░░░] 13.7%/12% ✓ (20 files)
        ● .claude/skills/measure-ai-proficiency/SKILL.md
        ● .claude/skills/agentic-workflow/SKILL.md
        ... and 18 more

    ✓ Level 5: Multi-Agent Ready
      [██|░░░░░░░░░░░░░░░░░] 13.9%/10% ✓ (8 files)
        ● .github/agents/debug-agentic-workflow.agent.md
        ● .github/agents/improve-ai-context.agent.md
        ... and 6 more

    ○ Level 6: Fleet Infrastructure
      [░|░░░░░░░░░░░░░░░░░░] 0.0%/8% needs +8.0% (0 files)

  Cross-References & Quality:

    References: 126 found in 11 files
    Unique targets: 34
    Resolved: 82/126 (65%)

    Content Quality:
      CLAUDE.md: 10.0/10 (797 words) [§ ⌘ $ ! ↻12]
      AGENTS.md: 10.0/10 (513 words) [§ ⌘ $ !]
      .github/copilot-instructions.md: 10.0/10 (755 words) [§ ⌘ $ ↻9]
      .claude/skills/measure-ai-proficiency/SKILL.md: 10.0/10 (1099 words) [§ ⌘ $ ! ↻11]

    Quality indicators:
      §=sections  ⌘=paths  $=commands  !=constraints  ↻N=commits

    Bonus: +9.1 points

  Recommendations:

    → 🔍 Detected AI tools: Claude Code, GitHub Copilot, OpenAI Codex.
    → 🎯 FLEET READY: You have multi-agent setup. Now add fleet infrastructure.
    → 🧠 Set up Beads: Create .beads/ for persistent memory across agent sessions.

============================================================

JSON

{
  "repo_name": "measuring-ai-proficiency",
  "overall_level": 5,
  "overall_score": 60.09,
  "detected_tools": ["claude-code", "github-copilot", "openai-codex"],
  "level_scores": {
    "1": {"name": "Level 1: Zero AI", "coverage_percent": 100.0, "file_count": 1},
    "2": {"name": "Level 2: Basic Instructions", "coverage_percent": 75.0, "file_count": 3},
    "3": {"name": "Level 3: Comprehensive Context", "coverage_percent": 18.75, "file_count": 62},
    "4": {"name": "Level 4: Skills & Automation", "coverage_percent": 13.73, "file_count": 20},
    "5": {"name": "Level 5: Multi-Agent Ready", "coverage_percent": 13.89, "file_count": 8}
  },
  "cross_references": {
    "total_count": 126,
    "resolved_count": 82,
    "resolution_rate": 65.08,
    "bonus_points": 9.08
  },
  "recommendations": ["..."]
}

Markdown

The markdown format produces a detailed report with tables:

Level	Coverage	Files	Status
Level 1: Zero AI	100.0%	1	✓
Level 2: Basic Instructions	75.0%	3	✓
Level 3: Comprehensive Context	18.8%	62	✓
Level 4: Skills & Automation	13.7%	20	✓
Level 5: Multi-Agent Ready	13.9%	8	✓
Level 6: Fleet Infrastructure	0.0%	0	○

Content Quality:

File	Score	Words	Commits
`CLAUDE.md`	10.0/10	797	12
`AGENTS.md`	10.0/10	513	-
`.github/copilot-instructions.md`	10.0/10	755	9
`.claude/skills/measure-ai-proficiency/SKILL.md`	10.0/10	1099	11

Files Detected by Level

Level 2: Basic Instructions

Tool	Files
Claude Code	`CLAUDE.md`, `AGENTS.md`
GitHub Copilot	`.github/copilot-instructions.md`, `.github/AGENTS.md`
Cursor	`.cursorrules`, `.cursor/*.md`
OpenAI Codex	`CODEX.md`, `.codex/*.md`

Level 3: Comprehensive Context

Category	Files
Architecture	`ARCHITECTURE.md`, `docs/ARCHITECTURE.md`, `DESIGN.md`
API & Data	`API.md`, `docs/API.md`, `DATA_MODEL.md`
Standards	`CONVENTIONS.md`, `STYLE.md`, `CONTRIBUTING.md`, `PATTERNS.md`, `PR_REVIEW.md`
Development	`DEVELOPMENT.md`, `TESTING.md`, `DEBUGGING.md`, `DEPLOYMENT.md`

Level 4: Skills & Automation

Category	Files
Skills	`.claude/skills//SKILL.md`, `.github/skills//SKILL.md`, `.cursor/skills/*/SKILL.md`
Workflows	`.claude/commands/`, `WORKFLOWS.md`, `scripts/`
Memory	`MEMORY.md`, `LEARNINGS.md`, `DECISIONS.md`
Hooks	`.claude/hooks/`, `.claude/settings.json`

Level 5+: Multi-Agent & Fleet

Category	Files
Agents	`.github/agents/*.agent.md`, `agents/HANDOFFS.md`
MCP	`.mcp.json`, `.mcp/*.json`
Orchestration	`orchestration.yaml`, `workflows/*.yaml`

Cross-Reference Detection & Quality

The tool analyzes the content of your AI instruction files, not just their existence.

Quality Scoring (0-10)

Indicator	What We Look For	Points
Sections	Markdown headers (`##`) - 5+ headers = full points	0-2
Paths	Concrete file paths (`/src/`, `~/config/`)	0-2
Commands	CLI commands in backticks (`npm test`)	0-2
Constraints	"never", "avoid", "don't", "must not", "always"	0-2
Substance	Word count (200+ = 2pts, 50-200 = 1pt)	0-2
Commits	Git history (5+ = 2pts, 3-4 = 1pt)	0-2

Quality score is capped at 10.

Bonus Points (up to +10)

Cross-reference bonus (up to 5 pts): References between docs, resolution rate
Quality bonus (up to 5 pts): Half of average quality score

Customization

For Your Team

Different teams use different file names. The tool works best when customized:

Run the tool to see what it detects
Review patterns in measure_ai_proficiency/config.py
Add your team's specific file names
Adjust thresholds if needed

📖 Read the full customization guide

Configuration File

Create .ai-proficiency.yaml in your repository:

# Specify which AI tools your team uses
tools:
  - claude-code
  - github-copilot

# Adjust level thresholds (lower = easier to advance)
thresholds:
  level_3: 10   # Default: 15
  level_4: 8    # Default: 12

# Skip certain recommendations
skip_recommendations:
  - hooks
  - gastown

Add Custom Patterns

# In config.py, add your team's files:
file_patterns=[
    "SYSTEM_DESIGN.md",      # Instead of ARCHITECTURE.md
    "documentation/*.md",    # Instead of docs/
    "CODING_STANDARDS.md",   # Instead of CONVENTIONS.md
]

GitHub Action

Automatically assess AI proficiency on every PR:

# Quick setup with GitHub Agentic Workflows
gh extension install githubnext/gh-aw
gh aw add pskoett/measuring-ai-proficiency/.github/workflows/ai-proficiency-pr-review --create-pull-request

📖 Full GitHub Action documentation

Agent Skills

Want AI to help improve your context engineering? Add skills to your repository:

Available Skills

Skill	Description
measure-ai-proficiency	Assess repository AI maturity
customize-measurement	Generate a customized `.ai-proficiency.yaml`
plan-interview	Structured requirements gathering
agentic-workflow	Create natural language GitHub Actions

Install Skills

For Claude Code:

mkdir -p .claude/skills/measure-ai-proficiency
curl -o .claude/skills/measure-ai-proficiency/SKILL.md \
  https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/skill-template/measure-ai-proficiency/SKILL.md

For GitHub Copilot:

mkdir -p .github/skills/measure-ai-proficiency
curl -o .github/skills/measure-ai-proficiency/SKILL.md \
  https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/skill-template/measure-ai-proficiency/SKILL.md

Then ask your AI: "Assess my AI proficiency" or "What context files should I add?"

AI Agents

For systematic improvement workflows, use the AI Context Improvement Agent:

Available in this repo:

.github/agents/improve-ai-context.agent.md - For GitHub Copilot
.claude/agents/improve-ai-context.agent.md - For Claude Code

Integrated workflow using three skills:

plan-interview - Gathers requirements about your team's goals and constraints
customize-measurement - Creates tailored .ai-proficiency.yaml configuration
measure-ai-proficiency - Assesses and systematically improves your context

What it does:

Interviews you about team goals, tools, and target maturity level
Creates custom configuration matching your needs
Runs AI proficiency assessment with your config
Analyzes gaps and quality scores
Creates missing context files systematically
Improves existing files with low quality scores
Re-scans to verify improvements
Works with single repos or entire organizations

How to use:

Ask your AI assistant:

"Improve my AI proficiency" (full workflow with interview and config)
"Help me understand what context I need" (starts with plan-interview)
"Configure measurement for my team" (uses customize-measurement)
"Create missing context files" (quick assessment and improvement)
"Fix my low quality score" (focused on quality improvements)
"Advance to Level 4" (goal-oriented improvement)

The agent will guide you through requirements gathering, create a custom configuration, scan your repo, identify gaps, and systematically create or improve files to advance your maturity level.

Discover & Scan GitHub Organizations

You can now scan entire GitHub organizations without cloning any repositories:

# Scan all repos in an organization
measure-ai-proficiency --github-org your-org-name --format json --output report.json

Alternatively, use the discovery script to first see which repos have AI artifacts:

./scripts/find-org-repos.sh your-org-name

# Example output:
# Organization: anthropics
# Active repositories: 45
# Repos with AI context artifacts: 12 (26.7%)

Both methods require GitHub CLI (gh) to be installed and authenticated. The discovery script also requires jq.

Use Cases

Engineering Leadership

# Assess AI proficiency across your organization
measure-ai-proficiency --org /path/to/all-repos --format csv --output proficiency.csv

CI/CD Integration

- name: Check AI Proficiency
  run: |
    pip install git+https://github.com/pskoett/measuring-ai-proficiency.git
    measure-ai-proficiency --min-level 1

Team Onboarding

# Show new team members what context engineering looks like
measure-ai-proficiency

Scoring Algorithm

File Detection: Scan for patterns at each level (1-8)
Substantiveness Check: Files must have >100 bytes to count
Coverage Calculation: Percentage of patterns matched per level
Level Achievement:
- Level 2: At least one AI context file
- Level 3: Level 2 + ≥15% coverage
- Level 4: Level 3 + ≥12% coverage
- Level 5+: Progressive thresholds (≥10%, ≥8%, ≥6%, ≥5%)
Minimum Score Guarantees: Each level achieved guarantees a minimum score:
- Level 2: 15, Level 3: 30, Level 4: 45, Level 5: 55
- Level 6: 70, Level 7: 85, Level 8: 95
Bonus: Up to +10 points from cross-references and quality
Validation Penalties: Up to -10 points for issues (see below)

Content Validation

The tool validates your context files beyond just counting them:

Validation	What It Checks	Penalty
Freshness	Files updated within 90 days of code changes	-2 pts per stale file (max -6)
Alignment	Referenced files actually exist	-1 pt per missing ref (max -4)
Templates	Detects copy-pasted boilerplate markers	-2 pts if majority are templates

Warnings displayed:

STALE: CLAUDE.md last updated 120 days ago - Your context is outdated
MISSING REF: CLAUDE.md references 'src/old.ts' (deleted) - Broken references
TEMPLATE: CLAUDE.md contains template markers - Uncustomized boilerplate

Behavioral Indicators (Levels 6-8)

Higher levels now have concrete behavioral requirements beyond file patterns:

Level	Indicator	What It Checks
L6	CI/CD Integration	GitHub Actions workflows that invoke agents
L7	Agent Handoffs	2+ agents with documented handoff protocols
L8	Measured Outcomes	Metrics files, logs, or success tracking

These indicators are displayed in the output but don't affect scoring (yet).

Understanding Your Score

Low score but lots of files? This is normal! The tool includes hundreds of patterns. Your team likely uses different file names - customize the patterns for accurate scoring.

Got validation warnings? These identify issues that reduce AI effectiveness:

Stale context files mislead AI with outdated information
Broken references confuse AI about your codebase structure
Template content provides generic rather than project-specific guidance

Contributing

Contributions welcome! Areas of interest:

Additional file patterns for new tools
✅ ~~Integration with GitHub API for remote scanning~~ (implemented via GitHub CLI)
Historical tracking and trend analysis
IDE extensions

See FEATURE_BACKLOG.md for advanced metrics and architectural patterns we're exploring—including semantic drift detection, progressive disclosure scoring, and security governance indicators.

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 266 Commits
.claude		.claude
.entire		.entire
.evals		.evals
.github		.github
.learnings		.learnings
docs		docs
measure_ai_proficiency		measure_ai_proficiency
scripts		scripts
skill-template		skill-template
tests		tests
.ai-proficiency.yaml		.ai-proficiency.yaml
.ai-proficiency.yaml.example		.ai-proficiency.yaml.example
.gitattributes		.gitattributes
.gitignore		.gitignore
.mcp.json.example		.mcp.json.example
.pre-commit-config.yaml		.pre-commit-config.yaml
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
pyproject.toml		pyproject.toml
todo.md		todo.md
uv.lock		uv.lock

Folders and files

Latest commit

History

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Measure AI Proficiency

The Problem

The Solution

Quick Start

MCP Server - AI Context Awareness 🚀

Quick Setup

What You Can Do

Available MCP Tools

What You'll See

Installation

Usage

Mode 1: Local Scanning (Default)

Mode 2: GitHub CLI Scanning (Optional)

Choosing Between Modes

Output Formats

Other Options

Maturity Levels

Supported Tools

Example Output

Terminal

JSON

Markdown

Files Detected by Level

Level 2: Basic Instructions

Level 3: Comprehensive Context

Level 4: Skills & Automation

Level 5+: Multi-Agent & Fleet

Cross-Reference Detection & Quality

Quality Scoring (0-10)

Bonus Points (up to +10)

Customization

For Your Team

Configuration File

Add Custom Patterns

GitHub Action

Agent Skills

Available Skills

Install Skills

AI Agents

Discover & Scan GitHub Organizations

Use Cases

Engineering Leadership

CI/CD Integration

Team Onboarding

Scoring Algorithm

Content Validation

Behavioral Indicators (Levels 6-8)

Understanding Your Score

Contributing

License

Related

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