Leaderboard: jeremyeder/reference (55.8/100 - Bronze)

[jeremyeder]

Leaderboard Submission

Repository: jeremyeder/reference
Score: 55.8/100
Tier: Bronze
Submitted by: @jeremyeder

Validation Checklist

• Repository exists and is public
• Submitter has commit access
• Assessment re-run passes (±2 points tolerance)
• JSON schema valid

Automated validation will run on this PR.

---

Submitted via agentready submit command.

[jeremyeder]

CI Workflow Fix

The validation workflow is failing because it cannot access the main branch for comparison when running git diff.

Root Cause

The checkout action fetches the PR branch but doesn't have the base main branch available, causing:

fatal: ambiguous argument 'origin/main...HEAD': unknown revision or path not in the working tree.

Fix Applied

I've updated the workflow in this PR branch to:

1. Add fetch-depth: 0 to the checkout step
2. Explicitly fetch the main branch before running git diff

See commits:

• c5029cc - fix: add fetch-depth to checkout for git diff to work
• 143dc39 - fix: fetch origin/main before git diff

Note

Since GitHub Actions uses the workflow file from the base branch (not the PR branch), these fixes won't take effect for this PR's validation. The workflow needs to be fixed in the main repo first.

The submission itself is valid - the assessment JSON file has been successfully added to this PR. The validation will complete once the workflow is fixed in main.

[github-actions]

Hi @jeremyeder! Thanks for your interest in AgentReady.

The /agentready assess command is currently restricted to repository maintainers.

To assess your own repository:

pip install agentready
agentready assess .

See AgentReady documentation for more information.

[github-actions]

🎉 This PR is included in version 2.22.1 🎉

The release is available on GitHub release

Your semantic-release bot 📦🚀

[github-actions]

🥇 AgentReady Assessment (Current Repository)

Score: 78.3/100 | Level: Gold

🎯 Next Steps (by impact)

1. Fix: Type Annotations
2. Fix: Concise Documentation
3. Fix: Conventional Commit Messages
4. Fix: File Size Limits
5. Fix: Architecture Decision Records (ADRs)

📦 Download Full Reports • 🔗 View Workflow

📄 Full Assessment Report (click to expand)

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: main | Commit: dad99553
Assessed: January 15, 2026 at 5:41 AM
AgentReady Version: 2.22.1
Run by: runner@runnervmi13qx

---

📊 Summary

Metric	Value
Overall Score	78.3/100 🥇 Gold (Tier Definitions)
Attributes Assessed	22/25
Attributes Not Assessed	3
Assessment Duration	1.5s

Languages Detected

• Python: 184 files
• Markdown: 119 files
• YAML: 28 files
• JSON: 23 files
• Shell: 9 files
• XML: 4 files

Repository Stats

• Total Files: 473
• Total Lines: 224,599

🎯 Priority Improvements

Focus on these high-impact fixes first:

1. Type Annotations (Tier 1) - +10.0 points potential
   • Add type annotations to function signatures
2. Conventional Commit Messages (Tier 2) - +3.0 points potential
   • Configure conventional commits with commitlint
3. File Size Limits (Tier 2) - +3.0 points potential
   • Refactor large files into smaller, focused modules
4. Concise Documentation (Tier 2) - +3.0 points potential
   • Make documentation more concise and structured
5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
   • Create Architecture Decision Records (ADRs) directory and document key decisions

📋 Detailed Findings

Findings sorted by priority (Tier 1 failures first, then Tier 2, etc.)

Type Annotations ❌ 42/100

📝 Remediation Steps

Measured: 33.6% (Threshold: ≥80%)

Evidence:

• Typed functions: 586/1746
• Coverage: 33.6%

Add type annotations to function signatures

1. For Python: Add type hints to function parameters and return types
2. For TypeScript: Enable strict mode in tsconfig.json
3. Use mypy or pyright for Python type checking
4. Use tsc --strict for TypeScript
5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y

// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

Dependency Security & Vulnerability Scanning ✅ 40/100

CLAUDE.md Configuration Files ✅ 100/100

README Structure ✅ 100/100

Standard Project Layouts ✅ 100/100

Dependency Pinning for Reproducibility ✅ 100/100

Conventional Commit Messages ❌ 0/100

📝 Remediation Steps

Measured: not configured (Threshold: configured)

Evidence:

• No commitlint or husky configuration

Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

Commands:

npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

File Size Limits ❌ 54/100

📝 Remediation Steps

Measured: 3 huge, 11 large out of 186 (Threshold: <5% files >500 lines, 0 files >1000 lines)

Evidence:

• Found 3 files >1000 lines (1.6% of 186 files)
• Largest: src/agentready/assessors/code_quality.py (1080 lines)

Refactor large files into smaller, focused modules

1. Identify files >1000 lines
2. Split into logical submodules
3. Extract classes/functions into separate files
4. Maintain single responsibility principle

Examples:

# Split large file:
# models.py (1500 lines) → models/user.py, models/product.py, models/order.py

Concise Documentation ❌ 64/100

📝 Remediation Steps

Measured: 330 lines, 53 headings, 33 bullets (Threshold: <500 lines, structured format)

Evidence:

• README length: 330 lines (good)
• Heading density: 16.1 per 100 lines (target: 3-5)
• 1 paragraphs exceed 10 lines (walls of text)

Make documentation more concise and structured

1. Break long README into multiple documents (docs/ directory)
2. Add clear Markdown headings (##, ###) for structure
3. Convert prose paragraphs to bullet points where possible
4. Add table of contents for documents >100 lines
5. Use code blocks instead of describing commands in prose
6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

• Fast repository scanning
• HTML and Markdown reports
• 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]

</details>

![T2](https://img.shields.io/badge/T2-.gitignore_Completeness_92--100-green) **.gitignore Completeness** ✅ 92/100

![T2](https://img.shields.io/badge/T2-Separation_of_Concerns_98--100-green) **Separation of Concerns** ✅ 98/100

![T2](https://img.shields.io/badge/T2-Test_Coverage_Requirements_100--100-green) **Test Coverage Requirements** ✅ 100/100

![T2](https://img.shields.io/badge/T2-Pre-commit_Hooks_%26_CI%2FCD_Linting_100--100-green) **Pre-commit Hooks & CI/CD Linting** ✅ 100/100

![T2](https://img.shields.io/badge/T2-One-Command_Build%2FSetup_100--100-green) **One-Command Build/Setup** ✅ 100/100

![T2](https://img.shields.io/badge/T2-Inline_Documentation_100--100-green) **Inline Documentation** ✅ 100/100

![T3](https://img.shields.io/badge/T3-Architecture_Decision_Records_%28ADRs%29_0--100-red) **Architecture Decision Records (ADRs)** ❌ 0/100
<details>
<summary>📝 Remediation Steps</summary>

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:
```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups

</details>

![T3](https://img.shields.io/badge/T3-Structured_Logging_0--100-red) **Structured Logging** ❌ 0/100
<details>
<summary>📝 Remediation Steps</summary>

**Measured**: not configured (Threshold: structured logging library)

**Evidence**:
- No structured logging library found
- Checked files: pyproject.toml
- Using built-in logging module (unstructured)

Add structured logging library for machine-parseable logs

1. Choose structured logging library (structlog for Python, winston for Node.js)
2. Install library and configure JSON formatter
3. Add standard fields: timestamp, level, message, context
4. Include request context: request_id, user_id, session_id
5. Use consistent field naming (snake_case for Python)
6. Never log sensitive data (passwords, tokens, PII)
7. Configure different formats for dev (pretty) and prod (JSON)

**Commands**:
```bash
# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

CI/CD Pipeline Visibility ❌ 70/100

📝 Remediation Steps

Measured: basic config (Threshold: CI with best practices)

Evidence:

• CI config found: .github/workflows/pr-review-auto-fix.yml, .github/workflows/agentready-dev.yml, .github/workflows/ci.yml, .github/workflows/update-docs.yml, .github/workflows/research-update.yml, .github/workflows/leaderboard.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/stale-issues.yml, .github/workflows/agentready-dev-issue-pr.yml, .github/workflows/docs.yml, .github/workflows/dependabot-auto-merge.yml, .github/workflows/security.yml, .github/workflows/coverage-comment.yml, .github/workflows/continuous-learning.yml
• Descriptive job/step names found
• No caching detected
• Parallel job execution detected

Add or improve CI/CD pipeline configuration

1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
2. Define jobs: lint, test, build
3. Use descriptive job and step names
4. Configure dependency caching
5. Enable parallel job execution
6. Upload artifacts: test results, coverage reports
7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

Cyclomatic Complexity Thresholds ✅ 100/100

Issue & Pull Request Templates ✅ 100/100

Semantic Naming ✅ 100/100

OpenAPI/Swagger Specifications ⊘

Code Smell Elimination ✅ 83/100

Branch Protection Rules ⊘

Container/Virtualization Setup ⊘

---

📝 Assessment Metadata

• AgentReady Version: v2.22.1
• Research Version: v1.0.1
• Repository Snapshot: dad9955
• Assessment Duration: 1.5s
• Assessed By: runner@runnervmi13qx
• Assessment Date: January 15, 2026 at 5:41 AM

🤖 Generated with Claude Code