grepai-watch-daemon▌
yoanbernabeu/grepai-skills · updated Apr 8, 2026
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This skill covers the grepai watch command and daemon management for real-time code indexing.
GrepAI Watch Daemon
This skill covers the grepai watch command and daemon management for real-time code indexing.
When to Use This Skill
- Starting initial code indexing
- Setting up real-time file monitoring
- Running the daemon in background
- Troubleshooting indexing issues
What the Watch Daemon Does
The watch daemon:
- Scans all source files in your project
- Chunks code into segments (~512 tokens)
- Generates embeddings via your configured provider
- Stores vectors in your configured backend
- Monitors for file changes in real-time
- Updates the index when files change
Basic Usage
Start Watching (Foreground)
cd /your/project
grepai watch
Output:
🔍 GrepAI Watch
Scanning files...
Found 245 files
Processing chunks...
████████████████████████████████ 100%
Indexed 1,234 chunks
Watching for changes...
Press Ctrl+C to stop.
Start in Background
grepai watch --background
Output:
🔍 GrepAI Watch (background)
Daemon started with PID 12345
Log file: ~/.grepai/daemon.log
Check Daemon Status
grepai watch --status
Output:
✅ GrepAI Daemon Running
PID: 12345
Started: 2025-01-28 10:30:00
Project: /path/to/project
Statistics:
- Files indexed: 245
- Chunks: 1,234
- Last update: 2 minutes ago
Stop the Daemon
grepai watch --stop
Output:
✅ Daemon stopped (PID 12345)
Command Reference
| Command | Description |
|---|---|
grepai watch |
Start daemon in foreground |
grepai watch --background |
Start daemon in background |
grepai watch --status |
Check daemon status |
grepai watch --stop |
Stop running daemon |
Configuration
Watch Settings
# .grepai/config.yaml
watch:
# Debounce delay in milliseconds
# Groups rapid file changes together
debounce_ms: 500
Debounce Explained
When you save a file, editors often write multiple times quickly. Debouncing waits for changes to settle:
| Value | Behavior |
|---|---|
100 |
More responsive, more reindexing |
500 |
Balanced (default) |
1000 |
Less responsive, fewer reindexing |
Initial Indexing
What Gets Indexed
The daemon indexes files:
- Matching supported extensions (.go, .js, .ts, .py, etc.)
- Not in ignore patterns (node_modules, .git, etc.)
- Respecting
.gitignore
Indexing Progress
Large codebases show progress:
Scanning files...
Found 10,245 files
Processing chunks...
████████████████░░░░░░░░░░░░░░░░ 50% (5,122/10,245)
Indexing Time Estimates
| Codebase | Files | Time (Ollama) | Time (OpenAI) |
|---|---|---|---|
| Small | 100 | ~30s | ~10s |
| Medium | 1,000 | ~5min | ~1min |
| Large | 10,000 | ~30min | ~5min |
Real-Time Monitoring
After initial indexing, the daemon watches for:
- File creation
- File modification
- File deletion
- File renames
File Change Detection
Uses OS-native file watching:
- macOS: FSEvents
- Linux: inotify
- Windows: ReadDirectoryChangesW
What Triggers Reindexing
| Action | Result |
|---|---|
| Save existing file | Re-embed file chunks |
| Create new file | Index new chunks |
| Delete file | Remove from index |
| Rename file | Update path, keep vectors |
Background Daemon Management
Starting on System Boot
macOS (launchd)
Create ~/Library/LaunchAgents/com.grepai.watch.plist:
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>com.grepai.watch</string>
<key>ProgramArguments</key>
<array>
<string>/usr/local/bin/grepai</string>
<string>watch</string>
</array>
<key>WorkingDirectory</key>
<string>/path/to/your/project</string>
<key>RunAtLoad</key>
<true/>
<key>KeepAlive</key>
<true/>
</dict>
</plist>
Load:
launchctl load ~/Library/LaunchAgents/com.grepai.watch.plist
Linux (systemd)
Create ~/.config/systemd/user/grepai-watch.service:
[Unit]
Description=GrepAI Watch Daemon
After=network.target
[Service]
Type=simple
WorkingDirectory=/path/to/your/project
ExecStart=/usr/local/bin/grepai watch
Restart=always
[Install]
WantedBy=default.target
Enable:
systemctl --user enable grepai-watch
systemctl --user start grepai-watch
Checking Logs
# Background daemon logs
tail -f ~/.grepai/daemon.log
# Or with systemd
journalctl --user -u grepai-watch -f
Multiple Projects
One Daemon Per Project
Run separate daemons for each project:
# Terminal 1: Project A
cd /path/to/project-a
grepai watch --background
# Terminal 2: Project B
cd /path/to/project-b
grepai watch --background
Using Workspaces
For multi-project setups:
grepai workspace create my-workspace
grepai workspace add my-workspace /path/to/project-a
grepai workspace add my-workspace /path/to/project-b
grepai watch --workspace my-workspace
Troubleshooting
Daemon Won't Start
❌ Problem: "Another daemon is already running" ✅ Solution:
grepai watch --stop
grepai watch --background
❌ Problem: "Config not found" ✅ Solution: Initialize first:
grepai init
grepai watch
❌ Problem: "Embedder connection failed" ✅ Solution: Start your embedding provider:
ollama serve # For Ollama
Indexing Issues
❌ Problem: Files not being indexed ✅ Solution: Check ignore patterns in config, ensure file extension is supported
❌ Problem: Indexing very slow ✅ Solutions:
- Use OpenAI for faster cloud embeddings
- Add more ignore patterns
- Increase chunking size
❌ Problem: Index seems outdated ✅ Solution: Clear and reindex:
rm .grepai/index.gob
grepai watch
File Watch Issues
❌ Problem: Changes not detected ✅ Solutions:
- Reduce debounce_ms
- Check inotify limits (Linux):
echo 65536 | sudo tee /proc/sys/fs/inotify/max_user_watches
Best Practices
- Run in background: For continuous monitoring
- Use workspace for monorepos: Better organization
- Set up auto-start: launchd or systemd
- Check logs periodically: Monitor for errors
- Reindex after config changes: Especially after changing embedding model
Status Check
Regular health check:
grepai status
Output:
✅ GrepAI Status
Project: /path/to/project
Config: .grepai/config.yaml
Embedder: Ollama (nomic-embed-text)
Storage: GOB (.grepai/index.gob)
Index:
- Files: 245
- Chunks: 1,234
- Size: 12.5 MB
- Last updated: 2025-01-28 10:30:00
Daemon: Running (PID 12345)
Output Format
Watch daemon status:
✅ Watch Daemon Active
Mode: Background
PID: 12345
Project: /path/to/project
Initial Index:
- Files scanned: 245
- Chunks created: 1,234
- Duration: 45s
Real-time Monitor:
- Debounce: 500ms
- Events processed: 23
- Last event: 5 minutes ago
Next steps:
- Run 'grepai search "query"' to search
- Run 'grepai watch --status' to check status
- Run 'grepai watch --stop' to stop daemon
How to use grepai-watch-daemon on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add grepai-watch-daemon
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches grepai-watch-daemon from GitHub repository yoanbernabeu/grepai-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate grepai-watch-daemon. Access the skill through slash commands (e.g., /grepai-watch-daemon) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★45 reviews- ★★★★★Maya Haddad· Dec 20, 2024
grepai-watch-daemon fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Kwame Ghosh· Dec 20, 2024
grepai-watch-daemon is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Nia Choi· Dec 12, 2024
We added grepai-watch-daemon from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aisha Reddy· Dec 12, 2024
I recommend grepai-watch-daemon for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Dec 4, 2024
grepai-watch-daemon is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zaid Martinez· Dec 4, 2024
Solid pick for teams standardizing on skills: grepai-watch-daemon is focused, and the summary matches what you get after install.
- ★★★★★Nia Perez· Nov 27, 2024
grepai-watch-daemon fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Rahul Santra· Nov 23, 2024
Useful defaults in grepai-watch-daemon — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Alexander Malhotra· Nov 15, 2024
I recommend grepai-watch-daemon for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aanya Gill· Nov 11, 2024
We added grepai-watch-daemon from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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