Grepai Quickstart

GrepAI Quickstart

This skill provides a complete walkthrough to get GrepAI running and searching your code in 5 minutes.

When to Use This Skill

• First time using GrepAI
• Need a quick refresher on basic workflow
• Setting up GrepAI on a new project
• Demonstrating GrepAI to someone

Prerequisites

• Terminal access
• A code project to index

Step 1: Install GrepAI

macOS

brew install yoanbernabeu/tap/grepai

Linux/macOS (Alternative)

curl -sSL https://raw.githubusercontent.com/yoanbernabeu/grepai/main/install.sh | sh

Windows

irm https://raw.githubusercontent.com/yoanbernabeu/grepai/main/install.ps1 | iex

Verify: grepai version

Step 2: Install Ollama (Local Embeddings)

macOS

brew install ollama
ollama serve &
ollama pull nomic-embed-text

Linux

curl -fsSL https://ollama.com/install.sh | sh
ollama serve &
ollama pull nomic-embed-text

Verify: curl http://localhost:11434/api/tags

Step 3: Initialize Your Project

Navigate to your project and initialize GrepAI:

cd /path/to/your/project
grepai init

This creates .grepai/config.yaml with default settings:

• Ollama as embedding provider
• nomic-embed-text model
• GOB file storage
• Standard ignore patterns

Step 4: Start Indexing

Start the watch daemon to index your code:

grepai watch

What happens:

1. Scans all source files (respects .gitignore)
2. Chunks code into ~512 token segments
3. Generates embeddings via Ollama
4. Stores vectors in .grepai/index.gob

First indexing output:

🔍 GrepAI Watch
   Scanning files...
   Found 245 files
   Processing chunks...
   ████████████████████████████████ 100%
   Indexed 1,234 chunks
   Watching for changes...

Background Mode

For long-running projects:

# Start in background
grepai watch --background

# Check status
grepai watch --status

# Stop when done
grepai watch --stop

Step 5: Search Your Code

Now search semantically:

# Basic search
grepai search "authentication flow"

# Limit results
grepai search "error handling" --limit 5

# JSON output for scripts
grepai search "database queries" --json

Example Output

Score: 0.89 | src/auth/middleware.go:15-45
──────────────────────────────────────────
func AuthMiddleware() gin.HandlerFunc {
    return func(c *gin.Context) {
        token := c.GetHeader("Authorization")
        if token == "" {
            c.AbortWithStatus(401)
            return
        }
        // Validate JWT token...
    }
}

Score: 0.82 | src/auth/jwt.go:23-55
──────────────────────────────────────────
func ValidateToken(tokenString string) (*Claims, error) {
    token, err := jwt.Parse(tokenString, func(t *jwt.Token) (interface{}, error) {
        return []byte(secretKey), nil
    })
    // ...
}

Step 6: Analyze Call Graphs (Optional)

Trace function relationships:

# Who calls this function?
grepai trace callers "Login"

# What does this function call?
grepai trace callees "ProcessPayment"

# Full dependency graph
grepai trace graph "ValidateToken" --depth 3

Complete Workflow Summary

# 1. Install (once)
brew install yoanbernabeu/tap/grepai
brew install ollama && ollama serve & && ollama pull nomic-embed-text

# 2. Setup project (once per project)
cd /your/project
grepai init

# 3. Index (run in background)
grepai watch --background

# 4. Search (as needed)
grepai search "your query here"

# 5. Trace (as needed)
grepai trace callers "FunctionName"

Quick Command Reference

| Command | Purpose | |---------|---------| | grepai init | Initialize project config | | grepai watch | Start indexing daemon | | grepai watch --background | Run daemon in background | | grepai watch --status | Check daemon status | | grepai watch --stop | Stop daemon | | grepai search "query" | Semantic search | | grepai search --json | JSON output | | grepai trace callers "fn" | Find callers | | grepai trace callees "fn" | Find callees | | grepai status | Index statistics | | grepai version | Show version |

Search Tips

Be descriptive, not literal:

• ✅ "user authentication and session management"
• ❌ "auth"

Describe intent:

• ✅ "where errors are logged to the console"
• ❌ "console.error"

Use English:

• Models are trained primarily on English text
• Works best with English queries

Next Steps

After mastering the basics:

1. Configure embeddings: See grepai-embeddings-* skills
2. Setup storage: See grepai-storage-* skills
3. Advanced search: See grepai-search-* skills
4. MCP integration: See grepai-mcp-* skills

Output Format

Successful quickstart:

✅ GrepAI Quickstart Complete

   Project: /path/to/your/project
   Files indexed: 245
   Chunks created: 1,234
   Embedder: Ollama (nomic-embed-text)
   Storage: GOB (local file)

   Try these searches:
   - grepai search "main entry point"
   - grepai search "database connection"
   - grepai search "error handling"

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: yoanbernabeu
• Source: yoanbernabeu/grepai-skills
• License: MIT
• Homepage: https://github.com/yoanbernabeu/grepai

Install and usage instructions live in the source repository linked above.