grepai-init

yoanbernabeu/grepai-skills · updated Apr 8, 2026

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$npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-init
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summary

This skill covers the grepai init command and project initialization.

skill.md

GrepAI Init

This skill covers the grepai init command and project initialization.

When to Use This Skill

  • Setting up GrepAI in a new project
  • Understanding what grepai init creates
  • Customizing initial configuration
  • Troubleshooting initialization issues

Basic Usage

cd /path/to/your/project
grepai init

What Init Creates

Running grepai init creates the .grepai/ directory with:

.grepai/
├── config.yaml    # Configuration file
├── index.gob      # Vector index (created by watch)
└── symbols.gob    # Symbol index for trace (created by watch)

Default Configuration

The generated config.yaml:

version: 1

embedder:
  provider: ollama
  model: nomic-embed-text
  endpoint: http://localhost:11434

store:
  backend: gob

chunking:
  size: 512
  overlap: 50

watch:
  debounce_ms: 500

trace:
  mode: fast
  enabled_languages:
    - .go
    - .js
    - .ts
    - .jsx
    - .tsx
    - .py
    - .php
    - .c
    - .h
    - .cpp
    - .hpp
    - .cc
    - .cxx
    - .rs
    - .zig
    - .cs
    - .pas
    - .dpr

ignore:
  - .git
  - .grepai
  - node_modules
  - vendor
  - target
  - __pycache__
  - dist
  - build

Understanding Default Settings

Embedder Settings

Setting Default Purpose
provider ollama Local embedding generation
model nomic-embed-text 768-dimension model
endpoint http://localhost:11434 Ollama API URL

Store Settings

Setting Default Purpose
backend gob Local file storage

Chunking Settings

Setting Default Purpose
size 512 Tokens per chunk
overlap 50 Overlap for context

Watch Settings

Setting Default Purpose
debounce_ms 500 Wait time before re-indexing

Ignore Patterns

Default patterns exclude:

  • Version control: .git
  • GrepAI data: .grepai
  • Dependencies: node_modules, vendor
  • Build outputs: target, dist, build
  • Cache: __pycache__

Customizing After Init

Edit .grepai/config.yaml to customize:

Change Embedding Provider

embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}

Change Storage Backend

store:
  backend: postgres
  postgres:
    dsn: postgres://user:pass@localhost:5432/grepai

Add Custom Ignore Patterns

ignore:
  - .git
  - .grepai
  - node_modules
  - "*.min.js"
  - "*.bundle.js"
  - coverage/
  - .nyc_output/

Init in Monorepos

For monorepos, init at the root:

cd /path/to/monorepo
grepai init

Or use workspaces for separate indices:

grepai workspace create my-workspace
grepai workspace add my-workspace /path/to/project1
grepai workspace add my-workspace /path/to/project2

Re-Initialization

If you need to reset:

# Remove existing config
rm -rf .grepai

# Re-initialize
grepai init

Warning: This deletes your index. You'll need to re-run grepai watch.

Verifying Initialization

After init, verify with:

# Check config exists
cat .grepai/config.yaml

# Check status (will show no index yet)
grepai status

Common Issues

Problem: .grepai already exists ✅ Solution: Delete it first or edit existing config:

rm -rf .grepai && grepai init

Problem: Config created but Ollama not running ✅ Solution: Start Ollama before running grepai watch:

ollama serve

Problem: Wrong directory initialized ✅ Solution: Remove .grepai and init in correct directory

Best Practices

  1. Init at project root: Where your main code lives
  2. Add .grepai/ to .gitignore: Index is machine-specific
  3. Customize ignore patterns: Exclude generated/vendored code
  4. Review config after init: Adjust for your stack

Example .gitignore Addition

# GrepAI
.grepai/

Output Format

After successful initialization:

✅ GrepAI Initialized

   Config: .grepai/config.yaml

   Default settings:
   - Embedder: Ollama (nomic-embed-text)
   - Storage: GOB (local file)
   - Chunking: 512 tokens, 50 overlap

   Next steps:
   1. Ensure Ollama is running: ollama serve
   2. Start indexing: grepai watch
how to use grepai-init

How to use grepai-init on Cursor

AI-first code editor with Composer

1

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-init
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-init

The skills CLI fetches grepai-init from GitHub repository yoanbernabeu/grepai-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/grepai-init

Reload or restart Cursor to activate grepai-init. Access the skill through slash commands (e.g., /grepai-init) 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

GET_STARTED →

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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.639 reviews
  • Aisha Rao· Dec 16, 2024

    I recommend grepai-init for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Camila Khan· Dec 8, 2024

    Solid pick for teams standardizing on skills: grepai-init is focused, and the summary matches what you get after install.

  • Dhruvi Jain· Dec 4, 2024

    Solid pick for teams standardizing on skills: grepai-init is focused, and the summary matches what you get after install.

  • Camila Martinez· Nov 27, 2024

    We added grepai-init from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Oshnikdeep· Nov 23, 2024

    We added grepai-init from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Min Khan· Nov 19, 2024

    Keeps context tight: grepai-init is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Yuki Gill· Nov 7, 2024

    grepai-init reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yuki Rao· Oct 26, 2024

    Registry listing for grepai-init matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Luis Huang· Oct 18, 2024

    grepai-init fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ganesh Mohane· Oct 14, 2024

    grepai-init fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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