file-spec-contractor 0.2.7

Token-saving contracts for your codebase

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

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

# Overwrite existing config
fsc init --yes

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

# 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, bulk by default)
fsc generate

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

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

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

# Force per-file mode with parallel requests
fsc generate --force-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

Mode	Flag	Behaviour
bulk	(default)	All files in a single LLM request. Consistent, cross-referenced specifications.
per-file sequential	--force-per-file	Each file separately, one at a time.
per-file parallel	--force-per-file -c N	N files simultaneously via thread pool. Fastest for large projects.

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

Commands

Command	Description
init [dir]	Create .fsc/ with config and prompt. Accepts optional target directory and all config flags. Warns if already configured.
deinit [dir]	Remove .fsc/ and all *.fsc.md files from the project. Accepts optional target directory.
reinit [dir]	deinit + init. Accepts all the same flags as init.
generate	Generate .fsc.md specifications.

Options

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

Option	Description
-y, --yes	Skip confirmations, overwrite existing files (init/reinit)
--file	Specific files to generate specs for (generate only, repeatable)
--extensions	File extensions to include (default: .py)
--exclude-dirs	Directories to skip
--exclude-files	File patterns to skip
--provider	LLM provider: openrouter (default) or deepseek
--model	Model name for the selected provider
--api-key	API key for the selected provider
--output-mode	mirror (default), adjacent, or batch
--output-dir	Output directory for mirror/batch mode (default: .fsc/specs)
--batch-size	Files per folder in batch mode (default: 50)
--prompt-file	Custom system prompt file
--language	Prompt language: en (default) or ru (init/reinit only)
-c, --concurrency	Parallel requests for per-file mode (default: 3)
--force-per-file	Force per-file generation instead of bulk
-f, --force	Regenerate all specs, ignoring cache
--dry-run	Preview without writing files or calling API (generate only)
--verbose	Detailed output (generate only)
--version	Show version and exit

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 = ""                     # model name; empty = 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
force_per_file = false     # skip bulk mode, use per-file

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

Provider	Model	Free	Env var
OpenRouter (default)	openai/gpt-oss-120b:free	yes	OPEN_ROUTER_API_KEY
DeepSeek	deepseek-chat	yes	DEEPSEEK_API_KEY

Switch provider via config or CLI:

fsc generate --provider deepseek

Output Modes

Mode	Behaviour
adjacent	Saves file.fsc.md right next to file.py
mirror	Saves to output_dir, preserving directory structure (e.g., src/machine.py → .fsc/specs/src/machine.fsc.md)
batch	Groups specs into numbered folders batch-1/, batch-2/, etc. File names encode the original path (e.g., src/machine.py → src__machine.fsc.md). Folder size controlled by batch_size (default: 50).

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 all files in a single request to the LLM (bulk mode, default) or one-by-one (per-file mode)
3. The LLM generates structured .fsc.md specifications
4. Saves the specifications - 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

Component	Library
CLI	Typer
Validation	Pydantic
Logging	Rich
HTTP	httpx
Config	python-dotenv
Testing	pytest

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
• fsc init <dir> - initialise in any directory
• CI pipeline with GitHub Actions
• PyPI publish automation
• --update flag for incremental regeneration
• Rich progress bars for large projects
• Local model support (Ollama, LM Studio)
• Publish to PyPI (pip install file_spec_contractor)
• VS Code extension (generate specs from context menu / command palette)