chore: Consolidate GitHub analysis scripts and update gitignore #140

jeremyeder · 2025-11-25T14:18:43Z

Summary

Merged analyze_github_org.py and analyze_github_org_rest.py into a single unified script
Added automatic fallback from GraphQL (gh CLI) to REST API for better reliability
Included utility scripts for filtering and querying cached repository data
Updated .gitignore to exclude generated cache files and temporary repository lists

Changes

Consolidated Script Features

Dual API support: Primary GraphQL via gh CLI with automatic REST API fallback
Comprehensive data: Fetches organization repos with activity metrics, languages, stars, forks
Smart caching: Saves results to .cache/github-org-analysis/ for efficient querying
Rich reporting: Activity-sorted repository lists with summary statistics

New Utility Scripts

filter_active_originals.py: Filter to original (non-fork) repos with recent activity
query_github_cache.py: Query cached data by language, activity, stars, etc.

Gitignore Updates

Added .cache/ directory (generated analysis data)
Added repository list patterns (*-repos.txt, test-repos.txt, etc.)

Test plan

Ran black, isort, ruff on all modified scripts
Verified consolidated script maintains all features from both original versions
Updated error messages to reference correct script name
Confirmed all generated files are properly excluded by gitignore

🤖 Generated with Claude Code

Consolidated analyze_github_org.py and analyze_github_org_rest.py into a single script with automatic fallback from GraphQL to REST API. Added supporting utility scripts for filtering and querying cached data. Updated .gitignore to exclude generated cache files and temporary repo lists. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

github-actions · 2025-11-25T14:19:25Z

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: cc75fef0
Assessed: November 25, 2025 at 2:19 PM
AgentReady Version: 2.7.1
Run by: runner@runnervmg1sw1

📊 Summary

Metric	Value
Overall Score	70.4/100
Certification Level	Silver
Attributes Assessed	19/31
Attributes Not Assessed	12
Assessment Duration	1.3s

Languages Detected

Python: 137 files
Markdown: 98 files
YAML: 20 files
JSON: 9 files
Shell: 6 files

Repository Stats

Total Files: 315
Total Lines: 174,816

🎖️ Certification Ladder

💎 Platinum (90-100)
🥇 Gold (75-89)
🥈 Silver (60-74) → YOUR LEVEL ←
🥉 Bronze (40-59)
⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute	Tier	Status	Score
OpenAPI/Swagger Specifications	T3	⊘ not_applicable	—

Build & Development

Attribute	Tier	Status	Score
One-Command Build/Setup	T2	✅ pass	100
One-Command Build/Setup	T2	⊘ not_applicable	—
Container/Virtualization Setup	T4	⊘ not_applicable	—

Code Organization

Attribute	Tier	Status	Score
Separation of Concerns	T2	✅ pass	98

Code Quality

Attribute	Tier	Status	Score
Type Annotations	T1	❌ fail	41
Cyclomatic Complexity Thresholds	T3	✅ pass	100
Semantic Naming	T3	✅ pass	100
Structured Logging	T3	❌ fail	0
Code Smell Elimination	T4	⊘ not_applicable	—

❌ Type Annotations

Measured: 32.6% (Threshold: ≥80%)

Evidence:

Typed functions: 445/1365
Coverage: 32.6%

📝 Remediation Steps

Add type annotations to function signatures

For Python: Add type hints to function parameters and return types
For TypeScript: Enable strict mode in tsconfig.json
Use mypy or pyright for Python type checking
Use tsc --strict for TypeScript
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
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

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

📝 Remediation Steps

Add structured logging library for machine-parseable logs

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

Commands:

# 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}")

Context Window Optimization

Attribute	Tier	Status	Score
CLAUDE.md Configuration Files	T1	✅ pass	100
File Size Limits	T2	⊘ not_applicable	—

Dependency Management

Attribute	Tier	Status	Score
Lock Files for Reproducibility	T1	❌ fail	0
Dependency Freshness & Security	T2	⊘ not_applicable	—

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

No lock files found

📝 Remediation Steps

Add lock file for dependency reproducibility

Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute	Tier	Status	Score
Concise Documentation	T2	❌ fail	70
Inline Documentation	T2	✅ pass	100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

README length: 276 lines (excellent)
Heading density: 14.5 per 100 lines (target: 3-5)
1 paragraphs exceed 10 lines (walls of text)

📝 Remediation Steps

Make documentation more concise and structured

Break long README into multiple documents (docs/ directory)
Add clear Markdown headings (##, ###) for structure
Convert prose paragraphs to bullet points where possible
Add table of contents for documents >100 lines
Use code blocks instead of describing commands in prose
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>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

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

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

<details><summary><strong>📝 Remediation Steps</strong></summary>


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>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

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

**Commands**:

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

Performance

Attribute	Tier	Status	Score
Performance Benchmarks	T4	⊘ not_applicable	—

Repository Structure

Attribute	Tier	Status	Score
Standard Project Layouts	T1	✅ pass	100
Issue & Pull Request Templates	T3	✅ pass	100
Separation of Concerns	T2	⊘ not_applicable	—

Security

Attribute	Tier	Status	Score
Security Scanning Automation	T4	⊘ not_applicable	—

Testing & CI/CD

Attribute	Tier	Status	Score
Test Coverage Requirements	T2	✅ pass	100
Pre-commit Hooks & CI/CD Linting	T2	✅ pass	100
CI/CD Pipeline Visibility	T3	✅ pass	80

🎯 Next Steps

Priority Improvements (highest impact first):

Lock Files for Reproducibility (Tier 1) - +10.0 points potential
- Add lock file for dependency reproducibility
Type Annotations (Tier 1) - +10.0 points potential
- Add type annotations to function signatures
Conventional Commit Messages (Tier 2) - +3.0 points potential
- Configure conventional commits with commitlint
Concise Documentation (Tier 2) - +3.0 points potential
- Make documentation more concise and structured
Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
- Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

Tool Version: AgentReady v1.0.0
Research Report: Bundled version
Repository Snapshot: cc75fef
Assessment Duration: 1.3s

🤖 Generated with Claude Code

chatgpt-codex-connector

💡 Codex Review

Here are some automated review suggestions for this pull request.

ℹ️ About Codex in GitHub

Your team has set up Codex to review pull requests in this repo. Reviews are triggered when you

Open a pull request for review
Mark a draft as ready
Comment "@codex review".

If Codex has suggestions, it will comment; otherwise it will react with 👍.

Codex can also answer questions or update the PR. Try commenting "@codex address that feedback".

chatgpt-codex-connector · 2025-11-25T14:21:57Z

scripts/query_github_cache.py

+    return [
+        r for r in repos if r.get("primaryLanguageName", "").lower() == language.lower()


Guard language filter against missing values

The language filter assumes every repo has a primaryLanguageName string and calls .lower() on it. Cached data from analyze_github_org.py sets this field to None for repos without a detected language, so running query_github_cache.py --language … will raise AttributeError as soon as such a repo is encountered, preventing any filtered output. Use a safe default (e.g., (value or '').lower()) before comparing to avoid crashing when repos lack a primary language.

Useful? React with 👍 / 👎.

Configure CodeRabbit with aggressive noise reduction: - Disable checks already handled locally (black, ruff, isort) - Set confidence threshold to 0.75 (only comment when certain) - Focus on security, bugs, breaking changes, performance - Ignore style preferences and trivial suggestions - Path-specific instructions for different code areas - Teach CodeRabbit about AgentReady patterns and conventions 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

github-actions · 2025-11-25T14:23:01Z

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: bb9650bb
Assessed: November 25, 2025 at 2:22 PM
AgentReady Version: 2.7.1
Run by: runner@runnervmg1sw1

📊 Summary

Metric	Value
Overall Score	70.4/100
Certification Level	Silver
Attributes Assessed	19/31
Attributes Not Assessed	12
Assessment Duration	1.5s

Languages Detected

Python: 137 files
Markdown: 98 files
YAML: 21 files
JSON: 9 files
Shell: 6 files

Repository Stats

Total Files: 316
Total Lines: 175,015

🎖️ Certification Ladder

💎 Platinum (90-100)
🥇 Gold (75-89)
🥈 Silver (60-74) → YOUR LEVEL ←
🥉 Bronze (40-59)
⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute	Tier	Status	Score
OpenAPI/Swagger Specifications	T3	⊘ not_applicable	—

Build & Development

Attribute	Tier	Status	Score
One-Command Build/Setup	T2	✅ pass	100
One-Command Build/Setup	T2	⊘ not_applicable	—
Container/Virtualization Setup	T4	⊘ not_applicable	—

Code Organization

Attribute	Tier	Status	Score
Separation of Concerns	T2	✅ pass	98

Code Quality

Attribute	Tier	Status	Score
Type Annotations	T1	❌ fail	41
Cyclomatic Complexity Thresholds	T3	✅ pass	100
Semantic Naming	T3	✅ pass	100
Structured Logging	T3	❌ fail	0
Code Smell Elimination	T4	⊘ not_applicable	—

❌ Type Annotations

Measured: 32.6% (Threshold: ≥80%)

Evidence:

Typed functions: 445/1365
Coverage: 32.6%

📝 Remediation Steps

Add type annotations to function signatures

For Python: Add type hints to function parameters and return types
For TypeScript: Enable strict mode in tsconfig.json
Use mypy or pyright for Python type checking
Use tsc --strict for TypeScript
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
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

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

📝 Remediation Steps

Add structured logging library for machine-parseable logs

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

Commands:

# 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}")

Context Window Optimization

Attribute	Tier	Status	Score
CLAUDE.md Configuration Files	T1	✅ pass	100
File Size Limits	T2	⊘ not_applicable	—

Dependency Management

Attribute	Tier	Status	Score
Lock Files for Reproducibility	T1	❌ fail	0
Dependency Freshness & Security	T2	⊘ not_applicable	—

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

No lock files found

📝 Remediation Steps

Add lock file for dependency reproducibility

Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute	Tier	Status	Score
Concise Documentation	T2	❌ fail	70
Inline Documentation	T2	✅ pass	100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

README length: 276 lines (excellent)
Heading density: 14.5 per 100 lines (target: 3-5)
1 paragraphs exceed 10 lines (walls of text)

📝 Remediation Steps

Make documentation more concise and structured

Break long README into multiple documents (docs/ directory)
Add clear Markdown headings (##, ###) for structure
Convert prose paragraphs to bullet points where possible
Add table of contents for documents >100 lines
Use code blocks instead of describing commands in prose
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>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

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

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

<details><summary><strong>📝 Remediation Steps</strong></summary>


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>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

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

**Commands**:

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

Performance

Attribute	Tier	Status	Score
Performance Benchmarks	T4	⊘ not_applicable	—

Repository Structure

Attribute	Tier	Status	Score
Standard Project Layouts	T1	✅ pass	100
Issue & Pull Request Templates	T3	✅ pass	100
Separation of Concerns	T2	⊘ not_applicable	—

Security

Attribute	Tier	Status	Score
Security Scanning Automation	T4	⊘ not_applicable	—

Testing & CI/CD

Attribute	Tier	Status	Score
Test Coverage Requirements	T2	✅ pass	100
Pre-commit Hooks & CI/CD Linting	T2	✅ pass	100
CI/CD Pipeline Visibility	T3	✅ pass	80

🎯 Next Steps

Priority Improvements (highest impact first):

Lock Files for Reproducibility (Tier 1) - +10.0 points potential
- Add lock file for dependency reproducibility
Type Annotations (Tier 1) - +10.0 points potential
- Add type annotations to function signatures
Conventional Commit Messages (Tier 2) - +3.0 points potential
- Configure conventional commits with commitlint
Concise Documentation (Tier 2) - +3.0 points potential
- Make documentation more concise and structured
Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
- Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

Tool Version: AgentReady v1.0.0
Research Report: Bundled version
Repository Snapshot: bb9650b
Assessment Duration: 1.5s

🤖 Generated with Claude Code

github-actions · 2025-11-25T15:49:12Z

🎉 This PR is included in version 2.8.0 🎉

The release is available on GitHub release

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chatgpt-codex-connector bot reviewed Nov 25, 2025

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jeremyeder merged commit 244ba8b into main Nov 25, 2025
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github-actions bot added the released label Nov 25, 2025

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chore: Consolidate GitHub analysis scripts and update gitignore #140

chore: Consolidate GitHub analysis scripts and update gitignore #140

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Features

Documentation

Bad: Verbose prose

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Features

Documentation

Bad: Verbose prose

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		return [
		r for r in repos if r.get("primaryLanguageName", "").lower() == language.lower()

chore: Consolidate GitHub analysis scripts and update gitignore #140

chore: Consolidate GitHub analysis scripts and update gitignore #140

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jeremyeder commented Nov 25, 2025

Summary

Changes

Consolidated Script Features

New Utility Scripts

Gitignore Updates

Test plan

Uh oh!

github-actions bot commented Nov 25, 2025

🤖 AgentReady Assessment Report

📊 Summary

Languages Detected

Repository Stats

🎖️ Certification Ladder

📋 Detailed Findings

API Documentation

Build & Development

Code Organization

Code Quality

❌ Type Annotations

❌ Structured Logging

Context Window Optimization

Dependency Management

❌ Lock Files for Reproducibility

Documentation

❌ Concise Documentation

Features

Documentation

Bad: Verbose prose

Performance

Repository Structure

Security

Testing & CI/CD

🎯 Next Steps

📝 Assessment Metadata

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💡 Codex Review

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github-actions bot commented Nov 25, 2025

🤖 AgentReady Assessment Report

📊 Summary

Languages Detected

Repository Stats

🎖️ Certification Ladder

📋 Detailed Findings

API Documentation

Build & Development

Code Organization

Code Quality

❌ Type Annotations

❌ Structured Logging

Context Window Optimization

Dependency Management

❌ Lock Files for Reproducibility

Documentation

❌ Concise Documentation

Features

Documentation

Bad: Verbose prose

Performance

Repository Structure

Security

Testing & CI/CD

🎯 Next Steps

📝 Assessment Metadata

Uh oh!

Uh oh!

github-actions bot commented Nov 25, 2025

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Reviewers

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