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Grepai Config Reference

  • 584 installs
  • 18 repo stars
  • Updated February 1, 2026
  • yoanbernabeu/grepai-skills

grepai-config-reference is a Claude Code skill that documents every GrepAI configuration option in .grepai/config.yaml for developers tuning agent-oriented codebase search.

About

grepai-config-reference is a yoanbernabeu/grepai-skills reference that explains all GrepAI settings stored at `/your/project/.grepai/config.yaml`, including the version field and advanced tuning blocks in the published schema excerpt. Developers reach for grepai-config-reference when optimizing GrepAI for a repo, troubleshooting misconfiguration, or setting up advanced search behavior before agents query the codebase. Use it whenever Claude needs authoritative option names and structure for GrepAI rather than guessing YAML keys during agent tooling setup or performance troubleshooting.

  • Complete reference for all configuration options in .grepai/config.yaml
  • Covers embedder, provider, model, endpoint, and dimensions settings
  • Details supported providers: ollama, openai, lmstudio
  • Lists recommended models per provider with default dimension sizes
  • Used for optimization, troubleshooting, and advanced setup

Grepai Config Reference by the numbers

  • 584 all-time installs (skills.sh)
  • +5 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #1,607 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-config-reference

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Listed on Skillselion
Installs584
repo stars18
Security audit3 / 3 scanners passed
Last updatedFebruary 1, 2026
Repositoryyoanbernabeu/grepai-skills

How do you configure GrepAI config.yaml options?

Quickly understand and correctly configure every option available in GrepAI's.grepai/config.yaml file.

Who is it for?

Developers configuring or debugging GrepAI semantic code search before agents rely on repository-aware queries.

Skip if: Teams not using GrepAI who only need generic ripgrep or IDE search without a .grepai/config.yaml file.

When should I use this skill?

The user needs to understand, optimize, or troubleshoot all available options in `.grepai/config.yaml` for GrepAI.

What you get

Validated `.grepai/config.yaml` with correct version and advanced GrepAI settings aligned to the project use case.

  • Configured .grepai/config.yaml
  • Documented option mappings

Files

SKILL.mdMarkdownGitHub ↗

GrepAI Configuration Reference

This skill provides a complete reference for all GrepAI configuration options in .grepai/config.yaml.

When to Use This Skill

  • Understanding all available configuration options
  • Optimizing GrepAI for your specific use case
  • Troubleshooting configuration issues
  • Setting up advanced configurations

Configuration File Location

/your/project/.grepai/config.yaml

Complete Configuration Schema

version: 1

# ═══════════════════════════════════════════════════════════════
# EMBEDDER CONFIGURATION
# Converts code text into vector embeddings
# ═══════════════════════════════════════════════════════════════
embedder:
  # Provider: ollama | openai | lmstudio
  provider: ollama

  # Model name (depends on provider)
  # Ollama: nomic-embed-text, bge-m3, mxbai-embed-large
  # OpenAI: text-embedding-3-small, text-embedding-3-large
  # LM Studio: nomic-embed-text-v1.5, bge-small-en-v1.5
  model: nomic-embed-text

  # API endpoint URL
  # Ollama default: http://localhost:11434
  # LM Studio default: http://localhost:1234
  # OpenAI: uses official API
  endpoint: http://localhost:11434

  # Vector dimensions (auto-detected if omitted)
  # nomic-embed-text: 768
  # text-embedding-3-small: 1536
  # text-embedding-3-large: 3072
  dimensions: 768

  # API key (for OpenAI, supports env vars)
  api_key: ${OPENAI_API_KEY}

  # Parallel requests (OpenAI only, for speed)
  parallelism: 4

# ═══════════════════════════════════════════════════════════════
# STORE CONFIGURATION
# Where vector embeddings are stored
# ═══════════════════════════════════════════════════════════════
store:
  # Backend: gob | postgres | qdrant
  backend: gob

  # PostgreSQL configuration (when backend: postgres)
  postgres:
    dsn: postgres://user:password@localhost:5432/grepai

  # Qdrant configuration (when backend: qdrant)
  qdrant:
    endpoint: localhost
    port: 6334
    use_tls: false
    api_key: your-qdrant-api-key  # Optional

# ═══════════════════════════════════════════════════════════════
# CHUNKING CONFIGURATION
# How code files are split for embedding
# ═══════════════════════════════════════════════════════════════
chunking:
  # Tokens per chunk (smaller = more precise, larger = more context)
  # Recommended: 256-1024
  size: 512

  # Overlap between chunks (preserves context at boundaries)
  # Recommended: 10-20% of size
  overlap: 50

# ═══════════════════════════════════════════════════════════════
# WATCH CONFIGURATION
# File watching daemon settings
# ═══════════════════════════════════════════════════════════════
watch:
  # Debounce delay in milliseconds
  # Groups rapid file changes together
  debounce_ms: 500

# ═══════════════════════════════════════════════════════════════
# TRACE CONFIGURATION
# Call graph analysis settings
# ═══════════════════════════════════════════════════════════════
trace:
  # Extraction mode: fast | precise
  # fast: Uses regex, no dependencies, faster
  # precise: Uses tree-sitter AST parsing, more accurate
  mode: fast

  # Languages to analyze for call graphs
  enabled_languages:
    - .go
    - .js
    - .ts
    - .jsx
    - .tsx
    - .py
    - .php
    - .c
    - .h
    - .cpp
    - .hpp
    - .cc
    - .cxx
    - .rs
    - .zig
    - .cs
    - .pas
    - .dpr

  # Patterns to exclude from trace analysis
  exclude_patterns:
    - "*_test.go"
    - "*.spec.ts"
    - "*.test.js"

# ═══════════════════════════════════════════════════════════════
# SEARCH CONFIGURATION
# Search result scoring and ranking
# ═══════════════════════════════════════════════════════════════
search:
  # Score boosting configuration
  boost:
    enabled: true

    # Reduce scores for certain paths
    penalties:
      - pattern: /tests/
        factor: 0.5
      - pattern: _test.
        factor: 0.5
      - pattern: .spec.
        factor: 0.5
      - pattern: /docs/
        factor: 0.6
      - pattern: /vendor/
        factor: 0.3
      - pattern: /node_modules/
        factor: 0.3

    # Increase scores for certain paths
    bonuses:
      - pattern: /src/
        factor: 1.1
      - pattern: /lib/
        factor: 1.1
      - pattern: /core/
        factor: 1.2
      - pattern: /app/
        factor: 1.1

  # Hybrid search (vector + keyword)
  hybrid:
    enabled: false
    k: 60  # BM25 parameter

# ═══════════════════════════════════════════════════════════════
# IGNORE CONFIGURATION
# Files and directories to exclude from indexing
# ═══════════════════════════════════════════════════════════════
ignore:
  # Directories
  - .git
  - .grepai
  - .svn
  - .hg
  - node_modules
  - vendor
  - target
  - __pycache__
  - .pytest_cache
  - dist
  - build
  - out
  - .next
  - .nuxt

  # Files
  - "*.min.js"
  - "*.min.css"
  - "*.bundle.js"
  - "*.map"
  - "*.lock"
  - package-lock.json
  - yarn.lock
  - pnpm-lock.yaml
  - go.sum

  # Generated
  - "*.generated.*"
  - "*.pb.go"
  - "*.d.ts"

Configuration by Use Case

Small Personal Project

version: 1
embedder:
  provider: ollama
  model: nomic-embed-text
store:
  backend: gob
chunking:
  size: 512
  overlap: 50

Large Codebase

version: 1
embedder:
  provider: ollama
  model: bge-m3  # Larger model
  parallelism: 4
store:
  backend: postgres  # Scalable storage
  postgres:
    dsn: postgres://user:pass@localhost:5432/grepai
chunking:
  size: 768  # Larger chunks
  overlap: 100

Team Environment

version: 1
embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}
  parallelism: 8
store:
  backend: qdrant
  qdrant:
    endpoint: qdrant.internal.company.com
    port: 6334
    use_tls: true

Maximum Privacy

version: 1
embedder:
  provider: ollama
  model: nomic-embed-text
  endpoint: http://localhost:11434
store:
  backend: gob  # Local file only

Environment Variables

GrepAI supports environment variable substitution:

embedder:
  api_key: ${OPENAI_API_KEY}

store:
  postgres:
    dsn: ${DATABASE_URL}

Set in your shell:

export OPENAI_API_KEY="sk-..."
export DATABASE_URL="postgres://..."

Validating Configuration

Check your config is valid:

grepai status

If there are config errors, they'll be displayed.

Configuration Precedence

1. .grepai/config.yaml in current directory 2. Workspace configuration (if using workspaces) 3. Default values

Best Practices

1. Start simple: Use defaults, optimize later 2. Match chunking to code style: Larger chunks for verbose code 3. Use boosting: Penalize test/vendor, boost src/lib 4. Secure API keys: Use environment variables, never commit 5. Exclude noise: Ignore generated files, dependencies

Output Format

Valid configuration status:

✅ GrepAI Configuration Valid

   Embedder: ollama (nomic-embed-text)
   Storage: gob (.grepai/index.gob)
   Chunking: 512 tokens, 50 overlap
   Trace mode: fast
   Languages: 18 enabled
   Ignore patterns: 12 configured
   Boosting: enabled

Related skills

How it compares

Use grepai-config-reference for GrepAI YAML tuning; use general ripgrep skills when no GrepAI agent search layer is installed.

FAQ

Where is the GrepAI configuration file located?

grepai-config-reference states GrepAI reads settings from `/your/project/.grepai/config.yaml`, including a version field and advanced option blocks.

When should you use grepai-config-reference?

Use grepai-config-reference when you need every GrepAI configuration option explained for optimization, troubleshooting, or advanced setup before agents search the repo.

Is Grepai Config Reference safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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