yoanbernabeu/grepai-skills1 file

Grepai Quickstart

Get started with GrepAI in 5 minutes. Use this skill for a complete walkthrough from installation to first search.

Specification
Skill ID
yoanbernabeu/grepai-skills/grepai-quickstart
Publisher
yoanbernabeu
Repository
grepai-skills
Installs
0
Files
1
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

yoanbernabeu/grepai-skills/grepai-quickstartInstalls these files
  • SKILL.md

What this skill tells the agent

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

CommandPurpose
grepai initInitialize project config
grepai watchStart indexing daemon
grepai watch --backgroundRun daemon in background
grepai watch --statusCheck daemon status
grepai watch --stopStop daemon
grepai search "query"Semantic search
grepai search --jsonJSON output
grepai trace callers "fn"Find callers
grepai trace callees "fn"Find callees
grepai statusIndex statistics
grepai versionShow 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"

Use this skill in a project

Open any RiverX project, search the Skills panel for yoanbernabeu/grepai-skills/grepai-quickstart, and install it in one click.

Use this skill