FileSpecContractor (fsc)

Token-saving contracts for your codebase.

fsc is a command-line tool that generates descriptive specifications for your code files - compact "contracts" that help LLMs understand your project without burning through free-tier token limits.

Why?

Free LLM models have strict context limits. Feeding them your entire codebase is expensive and often impossible. fsc creates lightweight .fsc.md files that capture the public API and critical implementation details of each file - enough for an agent to work with, small enough to fit in context.

Born from the frustration of trying to vibe-code on a student laptop.

Installation

# Install with uv
uv tool install file_spec_contractor

# Or with pip
pip install file_spec_contractor

After installation, the fsc command is available globally:

fsc --help

For Scala users

The fsc command may conflict with the Scala Fast Offline Compiler which also uses the fsc name. If both are installed, use the full package name instead:

file-spec-contractor init
file-spec-contractor generate
file-spec-contractor --help

# or with underscore
file_spec_contractor init
file_spec_contractor generate

fsc automatically detects Scala environments and shows a warning if a conflict is possible.

Usage

# Set up configuration with defaults
fsc init

# Init in a specific directory
fsc init /path/to/project

# Init with custom settings
fsc init --extensions .py --extensions .kt --language ru

# Init with a different provider
fsc init --provider deepseek

# Recreate from scratch (removes existing .fsc/ and specs)
fsc init --force

# Same without confirmation prompt
fsc init --force -y

# Remove all fsc artifacts (.fsc/ and *.fsc.md files)
fsc deinit

# Remove only generated specs, keep config
fsc clean

# Skip confirmation
fsc clean -y

# Recreate configuration from scratch (deinit + init)
fsc reinit

# Reinit with custom flags
fsc reinit --extensions .py --extensions .kt --language ru

# Init with custom model
fsc init --model deepseek-reasoner

# Generate specifications for current directory (scan mode, per-file by default)
fsc generate

# Generate with a specific model
fsc generate --model openai/gpt-4o-mini

# Generate for specific files
fsc generate --files src/machine.py

# Generate with custom extensions
fsc generate --extensions .py .kt

# Force per-file mode with parallel requests
fsc generate --gen-mode per-file -c 5

# Preview what would be generated (no files written)
fsc generate --dry-run --verbose

# Regenerate all specs ignoring cache
fsc generate -f

# Check version
fsc --version

Generation Modes

If bulk mode fails to produce parsable output, fsc automatically falls back to per-file generation.

Commands

Options

All options below are available on generate, init, and reinit (except --files, --dry-run, --verbose which are generate-only).

Configuration

fsc looks for configuration in this order (later sources override earlier ones):

1. CLI arguments (highest priority)
2. .fsc/config.toml in your project root
3. ~/.config/fsc/config.toml for user-wide settings

Creating config

fsc init

This creates:

• .fsc/config.toml - project configuration
• .fsc/PROMPT.md - custom system prompt (optional, built-in prompt is used as fallback)

Example .fsc/config.toml

# Which files to scan and which to skip
[project]
extensions = [".py", ".kt"]
exclude_dirs = [".venv", "venv", ".git", "__pycache__", "tests"]
exclude_files = ["setup.py", "conftest.py"]

# Output language and mode
[output]
language = "en"          # "en" or "ru"
output_mode = "mirror"   # "mirror", "adjacent", or "batch"
output_dir = ".fsc/specs"
batch_size = 50          # files per folder (batch mode)

# LLM provider
[api]
provider = "openrouter"        # "openrouter" or "deepseek"
# model = "deepseek-chat"      # optional; omit to use provider default

# Custom system prompt file (relative to project root)
[prompt]
file = ".fsc/PROMPT.md"

# Generation runtime settings
[runtime]
concurrency = 3            # parallel threads for per-file mode
generation_mode = "per-file" # "per-file", "bulk", or "per-file-parallel"

API Key

API keys are never stored in config files. Three ways to provide them (in priority order):

1. CLI flag - --api-key (highest priority)
2. Environment variable - OPEN_ROUTER_API_KEY / DEEPSEEK_API_KEY
3. .env file in project root (lowest priority)

OpenRouter (default):

# Option 1: CLI flag
fsc generate --api-key sk-or-v1-...

# Option 2: environment variable
export OPEN_ROUTER_API_KEY=sk-or-v1-...

# Option 3: .env file
echo "OPEN_ROUTER_API_KEY=sk-or-v1-..." > .env

DeepSeek (alternative):

fsc generate --provider deepseek --api-key sk-...
# or: export DEEPSEEK_API_KEY=sk-...
# or: echo "DEEPSEEK_API_KEY=sk-..." > .env

Providers

Switch provider via config or CLI:

fsc generate --provider deepseek

Output Modes

Batch mode example (batch_size = 50, 120 files):

.fsc/batches/
├── batch-1/
│   ├── src__controllers__UserController.fsc.md
│   └── ... (49 more files)
├── batch-2/
│   ├── src__models__Product.fsc.md
│   └── ... (49 more files)
└── batch-3/
    └── ... (20 files)

Configure via CLI or config:

fsc init --output-mode batch --batch-size 100
fsc generate --output-mode batch --batch-size 50

When output mode is changed, existing specs are automatically moved to the new location instead of being regenerated.

# .fsc/config.toml
[output]
output_mode = "batch"
output_dir = ".fsc/batches"
batch_size = 50

Prompt

fsc sends a system prompt to the LLM that defines the specification format. Built-in prompts are versioned per language: fsc_en_5.md, fsc_ru_5.md. The latest version is always used. Resolution order:

1. --prompt-file CLI argument
2. .fsc/PROMPT.md in project root
3. Built-in prompt from the package

If no prompt file is found, a warning is shown and the built-in prompt is used.

How It Works

1. Scans your project for files matching configured extensions
2. Sends files to the LLM one-by-one (per-file mode, default) or all in a single request (bulk mode)
3. The LLM generates structured .fsc.md specifications
4. Saves the specifications as file.<ext>.fsc.md - ready to be fed to any LLM agent
5. On subsequent runs, skips unchanged files. If output mode changed, moves specs instead of regenerating.

Specification Format

Each generated spec follows this structure:

• Purpose - what this file does
• Dependencies - external libs and internal modules
• Public API - all public methods with signatures and notes
• Implementation Notes - sentinels, patterns, non-obvious details
• Handle with Care - contracts that are easy to break
• Code Style - conventions used in this file

Requirements

• Python 3.12+
• uv for dependency management
• OpenRouter or DeepSeek API key (see API Key)
• .env file support via python-dotenv

Tech Stack

Development

# Clone and install in editable mode
git clone https://github.com/UmbrellaLeaf5/file_spec_contractor.git
cd file_spec_contractor
uv sync

# Run all tests (63 tests)
uv run pytest

# Run specific test file
uv run pytest tests/test_deepseek.py -v

# Run CLI in dev
uv run fsc --help

# Build package
uv build

Roadmap

• Core CLI with init, generate, deinit, reinit commands
• DeepSeek API integration
• OpenRouter API integration (free gpt-oss-120b model)
• Multi-provider support with --provider flag
• Bulk generation mode (all files in one request with fallback)
• Parallel per-file generation (--force-per-file -c N)
• Spec caching with --force to regenerate
• Configuration file support (TOML) with Pydantic validation
• .env file support for API keys (via python-dotenv)
• All config flags available on init, reinit, and generate
• Batch output mode, mirror, and adjacent
• Prompt resolution (project file → built-in fallback, per-language)
• Multi-language prompt support (en, ru)
• Installable CLI entry point (fsc)
• Graceful shutdown on Ctrl+C
• 63 tests (unit, integration, CLI)
• Spec auto-move on output mode change (no wasted regeneration)
• fsc --version and setuptools-scm versioning
• fsc init <dir> - initialise in any directory
• CI pipeline with GitHub Actions
• --force / --yes / confirmation prompts for destructive commands
• PyPI publish automation
• --update flag for incremental regeneration
• clean command just to delete all specs
• Rich progress bars for large projects
• Local model support (Ollama, LM Studio)
• check if Scala is used and make warning not to use short name
• add long name usage (file-spec-contractor or file_spec_contractor instead of fsc)
• Publish to PyPI (pip install file_spec_contractor)
• VS Code extension (generate specs from context menu / command palette)