grepai-quickstart
82
总安装量
82
周安装量
#2767
全站排名
安装命令
npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-quickstart
Agent 安装分布
claude-code
56
opencode
52
codex
47
github-copilot
39
cursor
34
Skill 文档
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-textmodel- GOB file storage
- Standard ignore patterns
Step 4: Start Indexing
Start the watch daemon to index your code:
grepai watch
What happens:
- Scans all source files (respects
.gitignore) - Chunks code into ~512 token segments
- Generates embeddings via Ollama
- Stores vectors in
.grepai/index.gob
First indexing output:
ð GrepAI Watch
Scanning files...
Found 245 files
Processing chunks...
ââââââââââââââââââââââââââââââââ 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
ââââââââââââââââââââââââââââââââââââââââââ
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
ââââââââââââââââââââââââââââââââââââââââââ
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:
- â “user authentication and session management”
- â “auth”
Describe intent:
- â “where errors are logged to the console”
- â “console.error”
Use English:
- Models are trained primarily on English text
- Works best with English queries
Next Steps
After mastering the basics:
- Configure embeddings: See
grepai-embeddings-*skills - Setup storage: See
grepai-storage-*skills - Advanced search: See
grepai-search-*skills - MCP integration: See
grepai-mcp-*skills
Output Format
Successful quickstart:
â
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"