writescore 6.5.0

AI writing pattern analysis and scoring tool

WriteScore

Analyze your writing quality and get actionable feedback to improve clarity, voice, and engagement.

Quick Start

uv sync
uv run python -m spacy download en_core_web_sm
uv run writescore analyze README.md

That's it! You'll see a detailed analysis with scores and improvement suggestions.

Requirements

Resource	Minimum	Recommended
Python	3.9	3.11+
RAM	4 GB	8 GB
Disk	2 GB	3 GB

Note: First run downloads transformer models (~500MB) and spaCy model (~50MB). Subsequent runs use cached models.

Getting Started

Quickest path: Install Just, then run just setup. See all options below.

Option	Local Install	CLI/IDE	Docker Required	Use WriteScore	Contribute
✓ Docker	No	CLI	Yes	Instructions	N/A
✓ pipx	No	CLI	No	Instructions	N/A
✓ Homebrew	No	CLI	No	Instructions	N/A
✓ Standalone	No	CLI	No	Instructions	N/A
Native (Just)	Yes	CLI	No	just install	just setup
Native (Just)	Yes	IDE	No	just install, open in any IDE	just setup, open in any IDE
Native (Manual)	Yes	CLI	No	Instructions	Instructions
Native (Manual)	Yes	IDE	No	Instructions, open in any IDE	Instructions, open in any IDE
Devcontainer	No	CLI	Yes	Instructions	Instructions
Devcontainer	No	IDE	Yes	VS Code → "Reopen in Container"	Same
Codespaces	No	CLI	No	Instructions	Instructions
Codespaces	No	IDE	No	GitHub → Code → Create codespace	Same

After setup, run just test (or uv run pytest for manual installs) to verify.

Installing Just

OS	Command
Windows	winget install Casey.Just (or choco install just / scoop install just)
macOS	brew install just
Ubuntu/Debian	sudo apt install just
Fedora	sudo dnf install just
Arch Linux	sudo pacman -S just
Via Cargo	cargo install just
Via Conda	conda install -c conda-forge just

Windows users: All just commands work in PowerShell and CMD. With uv, use uv run prefix instead of activating the venv.

Docker

Run WriteScore without any local installation using Docker. Models are pre-downloaded in the image.

# Analyze a file in current directory
docker run --rm -v "$(pwd):/work" -w /work ghcr.io/bohica-labs/writescore:latest analyze document.md

# With GPU support (NVIDIA)
docker run --rm --gpus all -v "$(pwd):/work" -w /work ghcr.io/bohica-labs/writescore:latest analyze document.md

Optional: Install wrapper script for native-like usage:

# Download and install
sudo curl -fsSL https://raw.githubusercontent.com/BOHICA-LABS/writescore/main/scripts/writescore-docker \
  -o /usr/local/bin/writescore
sudo chmod +x /usr/local/bin/writescore

# Now use like a native command
writescore analyze document.md

The wrapper auto-detects GPU (NVIDIA/AMD) and mounts files appropriately.

pipx

Install WriteScore in an isolated environment using pipx. No virtual environment management required.

# Install pipx if you don't have it
# macOS: brew install pipx && pipx ensurepath
# Linux: python3 -m pip install --user pipx && pipx ensurepath

# Install WriteScore
pipx install writescore

# Use immediately (spaCy model auto-downloads on first run)
writescore analyze document.md

Note: First run downloads spaCy model (~50MB) and transformer models (~500MB). Subsequent runs are faster.

Homebrew

Install WriteScore on macOS or Linux using Homebrew:

# Add the tap and install
brew tap bohica-labs/writescore
brew install writescore

# Or install directly
brew install bohica-labs/writescore/writescore

# Use immediately
writescore analyze document.md

The formula installs all dependencies including the spaCy language model.

Standalone Executable

Download a pre-built executable from GitHub Releases - no Python installation required.

Platform	Filename
Linux (x64)	writescore-linux-amd64
macOS (Intel)	writescore-darwin-amd64
macOS (Apple Silicon)	writescore-darwin-arm64
Windows (x64)	writescore-windows-amd64.exe

# Linux/macOS example
curl -LO https://github.com/BOHICA-LABS/writescore/releases/latest/download/writescore-linux-amd64
chmod +x writescore-linux-amd64
./writescore-linux-amd64 analyze document.md

# Move to PATH for easier access
sudo mv writescore-linux-amd64 /usr/local/bin/writescore
writescore analyze document.md

Note: Standalone executables are self-contained (~500MB) and include all models.

Native Manual

For users who prefer not to install Just. Requires uv.

Use WriteScore:

uv sync
uv run python -m spacy download en_core_web_sm

Contribute:

uv sync --extra dev
uv run python -m spacy download en_core_web_sm
uv run pre-commit install
uv run pre-commit install --hook-type commit-msg

Devcontainer CLI

devcontainer up --workspace-folder "$(pwd)" && \
devcontainer exec --workspace-folder "$(pwd)" just install

For contributors, replace just install with just dev.

Codespaces CLI

gh codespace create -r BOHICA-LABS/writescore && \
gh codespace ssh

Then run just install (users) or just setup (contributors).

Available Commands

Command	Description
just	List available commands
just install	Install package with all dependencies
just setup	Full dev setup (install + pre-commit hooks)
just test	Run fast tests (excludes slow markers)
just test-all	Run all tests including slow ones
just test-cov	Run tests with coverage report
just lint	Check code with ruff
just lint-fix	Auto-fix linting and format code
just typecheck	Run mypy type checking
just check	Run all checks (lint + typecheck)
just clean	Remove build artifacts and caches

Why WriteScore?

The Problem: Most writing feedback is vague ("needs improvement") or focuses only on grammar. Writers need specific, actionable guidance on what makes their writing feel mechanical, formulaic, or disengaging.

The Solution: WriteScore analyzes 17 linguistic dimensions to identify specific patterns that weaken writing quality, then provides actionable recommendations to improve clarity, voice, and reader engagement.

Key Differentiators:

• Actionable feedback — Know exactly what to fix with specific recommendations
• Multi-dimensional analysis — Examines vocabulary diversity, sentence variety, voice, structure, and more
• Quality-focused — Treats writing improvement as the goal, regardless of how content was created
• Transparent scoring — See how each dimension contributes to your overall score

When to use WriteScore:

• Improving drafts before publishing or submission
• Identifying mechanical or formulaic patterns in your writing
• Getting objective feedback on writing quality
• Polishing content for better reader engagement

What WriteScore is NOT:

• Not an AI detection tool — it analyzes writing quality, not authorship
• Not a grammar checker — use dedicated tools for spelling/grammar
• Not a plagiarism detector — use academic integrity tools for that

Features

• Comprehensive Scoring — Overall quality score with per-dimension breakdown
• 17 Analysis Dimensions — Vocabulary, sentence variety, voice, structure, readability, and more
• Content Type Presets — Optimized analysis for academic, technical, creative, and 10 other content types
• Multiple Modes — Fast checks to comprehensive analysis
• Actionable Insights — Specific recommendations ranked by impact
• Batch Processing — Analyze entire directories
• Score History — Track improvements over time
• Configurable — YAML-based configuration with layered overrides

Usage

# Basic analysis
writescore analyze document.md

# Detailed findings with recommendations
writescore analyze document.md --detailed

# Show detailed scores breakdown
writescore analyze document.md --show-scores

# Fast mode for quick checks
writescore analyze document.md --mode fast

# Full analysis for final review
writescore analyze document.md --mode full

# Analyze with content type (adjusts weights/thresholds)
writescore analyze document.md --content-type academic
writescore analyze document.md --content-type technical_book
writescore analyze document.md --content-type creative_fiction

# Batch process a directory
writescore analyze --batch docs/

# Validate your configuration
writescore validate-config --verbose

Analysis Modes

Mode	Speed	Best For
fast	Fastest	Quick checks, CI/CD
adaptive	Balanced	Default, most documents
sampling	Medium	Large documents
full	Slowest	Final review, maximum accuracy

See the Analysis Modes Guide for details.

Content Types

Optimize analysis for your document type with --content-type:

Content Type	Description
academic	Research papers, scholarly articles
technical_book	Technical books, accessible yet thorough
technical_docs	API docs, technical documentation
blog	Blog posts, articles
creative	Creative writing, general
creative_fiction	Fiction, stories
professional_bio	LinkedIn profiles, professional bios
personal_statement	Application essays, personal statements
business	Business documents, reports
news	News articles, journalism
marketing	Marketing copy, promotional content
social_media	Social posts, casual content
general	Default settings

Each content type adjusts dimension weights and thresholds for more accurate analysis.

Configuration

WriteScore uses YAML configuration files for customization without code changes.

Configuration Files

config/
├── base.yaml           # Default configuration (do not edit)
├── local.yaml          # Your overrides (git-ignored)
├── local.yaml.example  # Template for local.yaml
└── schema/             # JSON schema for validation

Customizing Settings

Create config/local.yaml to override defaults:

# Adjust dimension weights
dimensions:
  formatting:
    weight: 15.0  # Increase em-dash detection importance

# Adjust scoring thresholds
scoring:
  thresholds:
    ai_likely: 35  # More strict AI detection

Environment Variables

Override any setting via environment variables:

export WRITESCORE_DIMENSIONS_FORMATTING_WEIGHT=15
export WRITESCORE_SCORING_THRESHOLDS_AI_LIKELY=35

Validate Configuration

writescore validate-config --verbose

See the Configuration System Guide for details.

Troubleshooting

Slow First Run

This is normal. First analysis downloads transformer models (~500MB) and caches them. Subsequent runs are much faster.

Out of Memory

Quick fix: Use --mode fast for lower memory usage:

writescore analyze document.md --mode fast

On macOS Apple Silicon, if you see MPS memory errors:

export PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0
writescore analyze document.md

ModuleNotFoundError / Command Not Found

Quick fix: Use uv run prefix or activate the venv: source .venv/bin/activate

Diagnostic table:

Where did you install?	Current terminal	Fix
uv (.venv/)	Not using uv run	Prefix with uv run or activate venv
Devcontainer	Native terminal	Run inside container or install natively
Codespaces	Local terminal	Install natively
Unknown	—	Run diagnostic commands below

Diagnostic commands:

# Check if writescore is anywhere in PATH
which writescore

# Check if installed in current venv
uv pip show writescore

# Check common venv locations
ls -la .venv/bin/writescore 2>/dev/null || echo "Not in .venv"

Common fixes:

# Use uv run prefix
uv run writescore analyze README.md

# Or activate venv directly
source .venv/bin/activate  # Windows: .venv\Scripts\activate
writescore analyze README.md

# Run inside devcontainer (if installed there)
devcontainer exec --workspace-folder "$(pwd)" writescore analyze README.md

# Or reinstall natively
just install  # or: uv sync && uv run python -m spacy download en_core_web_sm

Can't find model 'en_core_web_sm'

python -m spacy download en_core_web_sm

NLTK Data Missing

If you see LookupError mentioning NLTK data:

python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger')"

Documentation

Document	Description
Architecture	System design, components, patterns
Configuration System	YAML config, content types, customization
Analysis Modes Guide	Mode comparison and usage
Development History	Project evolution and roadmap
Migration Guide	Upgrading from AI Pattern Analyzer
Changelog	Version history

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick links:

• Label taxonomy — How we categorize issues and PRs
• Secret scanning setup — Required before your first commit
• Code of Conduct — Community guidelines

Updating the Demo GIF

The README demo GIF is generated using VHS. To regenerate after feature changes:

# Install VHS (macOS)
brew install vhs

# Generate new demo
vhs docs/assets/demo.tape

The tape file is at docs/assets/demo.tape. Edit it to change the demo script.

License

MIT License - see LICENSE for details.