Skip to main content

Local-first LLM model deprecation watchdog — scans your configs, alerts on sunsets, and instructs your IDE to update.

Project description

Chowkidar

Local-first LLM model deprecation watchdog.

Chowkidar scans your project configs for LLM model strings, cross-references them against a local deprecation registry, and alerts you before models sunset — via desktop notifications and IDE rules that instruct AI assistants to update deprecated models automatically.

Everything runs on your machine. Zero data exfiltration.

Quick Start

pip install chowkidar

# First-time setup (initializes config + database)
chowkidar setup --skip-slm

# Fetch deprecation data from providers
chowkidar sync

# Scan your project
chowkidar scan .

# Check for deprecated models
chowkidar check .

Features

  • Multi-format scanning: .env, YAML, TOML, JSON, Python, JavaScript, TypeScript
  • Provider coverage: OpenAI, Anthropic, Google, Mistral (extensible plugin architecture)
  • IDE rules (zero-config): Auto-generates rules files for Cursor, Claude Code, VS Code/Copilot, Windsurf
  • MCP server: Interactive tools for querying deprecation status from your IDE
  • Desktop notifications: Threshold-based alerts (90d, 30d, 7d, sunset)
  • Background daemon: Periodic scanning with OS-native service installation
  • Local SLM: Optional Ollama integration for parsing unstructured deprecation announcements
  • Safe updates: File locking, atomic writes, automatic backups, dry-run mode
  • Cross-platform: macOS, Linux, Windows

Commands

chowkidar setup [--skip-slm]     # Initialize config, DB, and optional SLM
chowkidar scan [PATH]            # Scan for model strings
chowkidar sync                   # Fetch deprecation data
chowkidar check [PATH]           # Check for deprecated models
chowkidar status                 # Show daemon status and watched projects
chowkidar watch <PATH>           # Register project for monitoring
chowkidar unwatch <PATH>         # Unregister project
chowkidar pin <MODEL> [--reason] # Suppress alerts for a model
chowkidar unpin <MODEL>          # Re-enable alerts
chowkidar snooze <MODEL> --days  # Temporarily suppress alerts
chowkidar daemon                 # Start background daemon
chowkidar install-service        # Install OS-native service
chowkidar mcp                    # Start MCP server (for IDE)
chowkidar config [KEY] [VALUE]   # View/set configuration
chowkidar update [--dry-run]     # Update deprecated models in .env
chowkidar rules write [PATH]     # Generate IDE rules files
chowkidar rules clean [PATH]     # Remove generated rules files
chowkidar slm status             # Check SLM availability

IDE Integration

Automatic Rules (Recommended)

Chowkidar writes rules files that AI assistants auto-discover — no configuration needed. If standard desktop notifications about model deprecation are ignored or snoozed, Chowkidar acts as your ultimate fallback: it auto-updates your editor's rules to ensure your AI model knows to update the deprecated model automatically.

Editor Rules File
Cursor .cursor/rules/chowkidar-alerts.mdc
Claude Code .claude/rules/chowkidar-alerts.md
VS Code/Copilot .github/copilot-instructions.md
Windsurf .windsurfrules

Run chowkidar rules write or let the daemon do it automatically.

MCP Server (Advanced)

Add to your IDE's MCP config:

{
  "mcpServers": {
    "chowkidar": {
      "command": "chowkidar",
      "args": ["mcp"]
    }
  }
}

Configuration

Config file: ~/.chowkidar/config.toml

Key Default Description
auto_update false Allow automatic .env modifications
write_rules true Generate IDE rules files
gitignore_rules true Add rules files to .gitignore
slm_enabled false Use local SLM for parsing
slm_model gemma3:1b Ollama model for SLM
scan_interval_hours 4 How often to scan watched projects
sync_interval_hours 24 How often to fetch provider data

Security

  • Zero exfiltration: No env content, API keys, or paths leave your machine
  • Read-only by default: File modification requires explicit auto_update = true
  • Atomic writes: All modifications use temp file + os.replace
  • Automatic backups: .env.chowkidar.bak created before any change
  • File locking: Prevents concurrent write corruption

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chowkidar-0.1.0.tar.gz (698.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

chowkidar-0.1.0-py3-none-any.whl (68.7 kB view details)

Uploaded Python 3

File details

Details for the file chowkidar-0.1.0.tar.gz.

File metadata

  • Download URL: chowkidar-0.1.0.tar.gz
  • Upload date:
  • Size: 698.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for chowkidar-0.1.0.tar.gz
Algorithm Hash digest
SHA256 34816846b30469fafad5f6725f05ca965ef53246a47a2ea1b9aab0ef40c81602
MD5 48840e40b4176a9ee09a59fd32f1ce33
BLAKE2b-256 ed0c0611ae3e412cb435e929f9e9b57e459301ab31348f73ecb0dd13ade4385f

See more details on using hashes here.

File details

Details for the file chowkidar-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: chowkidar-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 68.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for chowkidar-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 2ea68aa63ab5c374e619f98e772d573e0eef180fdd45602b9b323c26f39e128b
MD5 4539dbc712dfe23eea8af6e4af57c9dd
BLAKE2b-256 354808f8e127701a4a5e32d2f40f0a45ca97c2a595383ca7386bc1ff2f8d9981

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page