An intelligent tool to map, analyze, and compile project source code into a single, context-optimized text file for Large Language Models (LLMs). Version 2 brings advanced dependency graph analysis, strict whitelist-based file inclusion, zero-dependency lightweight execution, and progress tracking!

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📖 Table of Contents

• 🤔 Why ProjectScriber?
• ✨ Key Features
• 🚀 Quick Start
• 💾 Installation
• 🖥️ Command-Line Usage
• ⚙️ Configuration
• 🤝 Contributing & Development

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🤔 Why ProjectScriber?

When working with Large Language Models, providing the full context of a codebase is crucial for getting accurate analysis, documentation, or refactoring suggestions. However, blindly pasting an entire project wastes tokens and introduces noise.

ProjectScriber automates context building using a Whitelist-First philosophy and an Intelligent Scoring Engine. It analyzes your codebase's dependency graph (e.g., Python imports), determines which files are most relevant to the code you're working on, and bundles them into a single, clean markdown file, strictly respecting your token budgets and file-type configurations.

📁 Your Codebase → 📦 ProjectScriber → 📋 LLM-Ready Context

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✨ Key Features

Feature	Description
🌳 Smart Project Mapping	Generates a clear and intuitive tree view of your project's structure.
⚡ Native Rust Acceleration	Accelerates heavy I/O and directory scanning natively via a high-performance Rust backend, with a parallel directory walker for 3-5× faster scans on large repos.
🛡️ Whitelist Philosophy	By default, only recognized code and support files are included. Binary and lock files are automatically ignored.
🧠 Intelligent Scoring Engine	Analyzes import graphs and file proximity to prioritize code modules that are directly related to your provided seed files. Includes import-cycle detection (SCC), architectural layering (toposort), and degree centrality for hub detection.
🕸️ Dependency Graph Visualization	Every run auto-emits an interactive .scriber/graph.html (Canvas + force-directed, offline). Also export to Graphviz DOT and Mermaid via --graph-dot / --graph-mermaid.
💰 Token Budgets	Set a hard limit on --max-tokens. Scriber will fit the most relevant files within your budget to save API costs. Per-language calibrated token estimation keeps budgets accurate (±5% vs. real BPE).
🔧 Opt-in Build Features	Three Cargo feature flags unlock advanced capabilities: BPE tokenizer (exact token counts via tiktoken), tree-sitter AST (symbol-level relations: type_reference/inherits), and graph snapshot (incremental graph restore across runs). All off by default; opt in at build time.
📊 Live Progress & Stats	Built-in zero-dependency progress spinner and detailed statistics summary at the end of the run.

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🚀 Quick Start

1. Install Scriber:

   pip install project-scriber

2. Navigate to your project's root and initialize config:

   scriber --init

   (This appends a [tool.scriber] block to your pyproject.toml. Use --force to overwrite it.)

3. Pack your context! Just point it to a file, folder, or let it scan the whole project:

   scriber src/main.py --output context.md

4. Review your stats:

   Scriber build completed.
   ----------------------------------------
    Code files included:    15
    Support files included: 4
    Files omitted/skipped:  2
    Estimated tokens:       12500
   ----------------------------------------
   Scriber pack written to: context.md
   

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💾 Installation

ProjectScriber distributes pre-compiled binary wheels for Linux, macOS, and Windows. A simple pip command is all you need:

pip install project-scriber

Or if you use uv:

uv pip install project-scriber

Note

If a pre-compiled wheel is not available for your platform/architecture, the package will automatically build from source, which requires a Rust compiler toolchain (Rust 1.70+) installed on your machine.

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🖥️ Command-Line Usage

Basic Commands

• Scan the current directory:

  scriber .

• Scan a specific file and its dependencies:

  scriber src/my_module.py

• Interactive Setup: Create/Append a default configuration in pyproject.toml (use --force to overwrite it).

  scriber --init

CLI Options

Option	Description
paths	Project file/folder paths used as seeds. Defaults to current directory.
--profile	Preset configuration profile.
--config	Path to pyproject.toml. Its parent directory becomes the project root.
--path-base	Base directory for relative paths when --config is used.
--format	Output format.
--output	Output file path, relative to project root unless absolute. Use '-' for stdout.
--only-tree	Render only scored tree/map, without file contents.
--modules	Enable automatic related module selection.
--no-modules	Disable automatic related module selection.
--support	Enable support files.
--no-support	Disable support files.
--support-content	Override default support file content policy.
--max-files	Maximum number of files in the pack.
--max-tokens	Approximate token budget for included file contents. 0 disables budget.
--min-score	Minimum score for non-seed files.
--init	Append a default [tool.scriber] config to pyproject.toml and exit.
--force	Allow --init to append even if [tool.scriber] already exists.
--project	Force project snapshot mode.
--explain, --explain-selection	Explain reason for file selection in detail.
--explain-graph	Print relation graph statistics and relations.
--why	Print exactly which rules/edges pulled the specified file into the pack.
--graph-json	Export the RelationGraph as a JSON file to the specified path.
--graph-html	Export the RelationGraph as an interactive HTML visualization to the specified path.
--graph-dot	Export the RelationGraph as a Graphviz DOT file to the specified path.
--graph-mermaid	Export the RelationGraph as a Mermaid diagram file to the specified path.
--no-graph-html	Do not auto-emit an interactive graph.html alongside the pack output.
--validate-config	Validate pyproject.toml scriber config.
--dry-run	Perform a dry run without saving the pack file.
--open	Open the output file automatically after creation.
--timings	Show execution timings for each phase.
--version	Show version information and exit.

Profiles

ProjectScriber comes with several preset profiles to quickly bias the file scoring and inclusion criteria:

Profile	Description
default	Standard scoring behavior.
audit	Boosts tests, config files, CI environments, and dependency files. Assumes full support content inclusion.
debug	Boosts direct/reverse dependencies, tests, runtime support, and files close to the seed path.
refactor	Boosts files within the same package, related tests, and direct dependencies.
docs	Heavily boosts documentation files while suppressing test and code file scores. Assumes tree_only support content by default.
gpt	LLM-optimized: ranks context via rank_context + emits the XML-anchored LlmPack report.
focused-gpt	Like gpt but scoped to the seed paths (focused mode) for tight token budgets.
full	LLM-optimized over the whole project snapshot (project_snapshot mode).

🕸️ Dependency Graph & Visualization

ProjectScriber builds a rich relation graph of your codebase — not just imports, but type_reference, inherits, test_of, config_refs_code, env_key, doc_mentions_code, and same_package edges.

Auto-emitted interactive graph

By default, every pack run writes an interactive visualization to .scriber/graph.html next to the pack output — a self-contained, dependency-free HTML file with a Canvas force-directed layout. Open it in any browser:

.scriber/
├── scriber_pack.md    ← the context pack
└── graph.html         ← interactive dependency graph (drag, zoom, click-to-inspect)

Suppress it with --no-graph-html or emit_graph_html = false in config.

Graph export formats

Flag	Format	Use case
--graph-json PATH	JSON edge list	Programmatic consumption / custom tooling
--graph-html PATH	Interactive HTML	Standalone visualization anywhere
--graph-dot PATH	Graphviz DOT	Render with dot -Tpng / Graphviz tools
--graph-mermaid PATH	Mermaid diagram	Renders natively on GitHub, Notion, GitLab

Graph introspection (--explain-graph) reports import cycles (SCC), top hub files (weighted-degree centrality), and architectural layers (topological sort):

--- Import Cycles (SCC) ---
Detected 4 cyclic component(s):
 [1] src/app.py, src/models.py, src/db.py

--- Top 5 Hubs (weighted degree centrality) ---
 - src/core/models.py    : 49.50
 - src/packer/pack.py    : 24.00

--- Architectural Layers (3 layers) ---
 L0: pyproject.toml, src/core/__init__.py
 L1: src/engine/scorer.py, src/graph/builder.py (+24 more)

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🛠️ IDE Integrations

PyCharm / IntelliJ IDEA (External Tools)

You can integrate ProjectScriber directly into PyCharm's right-click context menu to quickly generate LLM context packs for any selected file or folder!

1. Open Settings / Preferences ➔ Tools ➔ External Tools.
2. Click the + button to add a new tool.
3. Configure it as follows:

• Name: Scriber
• Group: External Tools
• Description: Runs ProjectScriber on the selected directory and copies output to clipboard
• Program: scriber (or the absolute path to your scriber.exe e.g., C:\Tools\Python\Python313\Scripts\scriber.exe)
• Arguments: "$FilePath$" --config $ProjectFileDir$/pyproject.toml
• Working directory: $ProjectFileDir$

Now, you can simply right-click any file or directory in your Project tree, select External Tools ➔ Scriber, and the context pack will be generated instantly based on your project configuration!

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⚙️ Configuration

ProjectScriber 2.2.0 configures itself through the standard pyproject.toml using the [tool.scriber] table. Generate the default block using:

scriber --init

Example pyproject.toml

Note

This is a minimal example. Run scriber --init to generate the full default configuration.

[tool.scriber]
format = "md"
max_tokens = 0        # 0 means unlimited
max_files = 0         # 0 means unlimited
only_tree = false     # If true, file contents are omitted
allow_external_paths = false
emit_graph_html = true  # Auto-write .scriber/graph.html on every run

[tool.scriber.modules]
enabled = true
content_min_score = 50

[tool.scriber.tokens]
# "auto" uses per-language calibrated ratios (Python ~3.8, JS ~3.5, Rust ~3.6 chars/token)
# "chars" uses a flat chars_per_token divisor (legacy, backward-compatible default)
estimator = "chars"
chars_per_token = 4

[tool.scriber.code_files]
# Only files matching these are considered "Code"
patterns = [
    "**/*.py",
    "**/*.js",
    "**/*.ts",
    "**/*.tsx"
]

[tool.scriber.support_files]
enabled = true
# Only files matching these are considered "Support"
patterns = [
    "pyproject.toml",
    "Dockerfile",
    "**/*.svg"
]

[tool.scriber.support_files.content]
default = "auto"
auto_max_bytes = 10000
full = [
    "pyproject.toml",
    "requirements.txt",
    "README.md"
]
tree_only = [
    "**/*.svg"
]

[tool.scriber.hard_ignore]
# Folders ignored entirely during the initial scan
patterns = [
    ".git/**",
    "__pycache__/**",
    "node_modules/**",
    ".venv/**"
]

Whitelist Policy

ProjectScriber 2.2.0 uses a strict whitelist approach:

1. Files must match either a code_pattern or a support_pattern to be considered.
2. Unrecognized extensions and binary files are automatically excluded, keeping your LLM context safe from binary garbage.
3. Lock files are included in the tree by default, but their contents are omitted to save tokens.
4. Support files can be marked as tree_only (e.g., **/*.svg), meaning they'll show up in the project map but their contents won't be read.

🔧 Opt-in Build Features

Three advanced capabilities ship behind Cargo feature flags — off by default to keep the wheel lean, opt-in at build time. All degrade gracefully (fall back to the default behavior) when absent.

Feature	Flag	What it enables
BPE tokenizer	--features bpe	Exact token counting via tiktoken-rs (cl100k_base / o200k_base). Set [tool.scriber.tokens] encoding = "cl100k_base" to use it; otherwise the calibrated estimator runs.
tree-sitter AST	--features treesitter	Symbol-level relations (type_reference, inherits) on the native path via real AST parsing — currently Python; additional grammars pluggable.
Graph snapshot	(always on)	Whole-graph persistence to .scriber/cache/graph.json; restored on the next run when no file changed, skipping a full rebuild.

Build with one or more features:

maturin develop --features "bpe treesitter"

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🤝 Contributing & Development

🤝 Contributing & Development

Contributions are welcome!

Development Setup

1. Clone the Repository:

   git clone https://github.com/SunneV/ProjectScriber.git
   cd ProjectScriber

2. Install Dependencies & Compile Extension (using uv is recommended):

   uv sync --all-extras

   (This synchronizes the virtual environment and compiles the native Rust extension automatically!)

3. Run Tests:

   uv run pytest