This skill covers installing and configuring Ollama as the local embedding provider for GrepAI. Ollama enables 100% private code search where your code never leaves your machine.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongrepai-ollama-setupExecute the skills CLI command in your project's root directory to begin installation:
Fetches grepai-ollama-setup from yoanbernabeu/grepai-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate grepai-ollama-setup. Access via /grepai-ollama-setup in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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This skill covers installing and configuring Ollama as the local embedding provider for GrepAI. Ollama enables 100% private code search where your code never leaves your machine.
| Benefit | Description |
|---|---|
| 🔒 Privacy | Code never leaves your machine |
| 💰 Free | No API costs |
| ⚡ Fast | Local processing, no network latency |
| 🔌 Offline | Works without internet |
# Install Ollama
brew install ollama
# Start the Ollama service
ollama serve
.dmg and drag to Applications# One-line installer
curl -fsSL https://ollama.com/install.sh | sh
# Start the service
ollama serve
GrepAI requires an embedding model to convert code into vectors.
# Download the recommended model (768 dimensions)
ollama pull nomic-embed-text
Specifications:
# Multilingual support (better for non-English code/comments)
ollama pull nomic-embed-text-v2-moe
# Larger, more accurate
ollama pull bge-m3
# Maximum quality
ollama pull mxbai-embed-large
| Model | Dimensions | Size | Best For |
|---|---|---|---|
nomic-embed-text |
768 | 274 MB | General code search |
nomic-embed-text-v2-moe |
768 | 500 MB | Multilingual codebases |
bge-m3 |
1024 | 1.2 GB | Large codebases |
mxbai-embed-large |
1024 | 670 MB | Maximum accuracy |
# Check if Ollama server is responding
curl http://localhost:11434/api/tags
# Expected output: JSON with available models
ollama list
# Output:
# NAME ID SIZE MODIFIED
# nomic-embed-text:latest abc123... 274 MB 2 hours ago
# Quick test (should return embedding vector)
curl http://localhost:11434/api/embeddings -d '{
"model": "nomic-embed-text",
"prompt": "function hello() { return world; }"
}'
After installing Ollama, configure GrepAI to use it:
# .grepai/config.yaml
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://localhost:11434
This is the default configuration when you run grepai init, so no changes are needed if using nomic-embed-text.
# Run in current terminal (see logs)
ollama serve
# Using nohup
nohup ollama serve &
# Or as a systemd service (Linux)
sudo systemctl enable ollama
sudo systemctl start ollama
# Check if running
pgrep -f ollama
# Or test the API
curl -s http://localhost:11434/api/tags | head -1
Embedding models load into RAM:
nomic-embed-text: ~500 MB RAMbge-m3: ~1.5 GB RAMmxbai-embed-large: ~1 GB RAMOllama uses CPU by default. For faster embeddings:
❌ Problem: connection refused to localhost:11434
✅ Solution: Start Ollama:
ollama serve
❌ Problem: Model not found ✅ Solution: Pull the model first:
ollama pull nomic-embed-text
❌ Problem: Slow embedding generation ✅ Solution:
ollama ps)❌ Problem: Out of memory ✅ Solution: Use a smaller model or increase system RAM
ollama serve is runningnomic-embed-text offers best balanceollama pull nomic-embed-text for updatesAfter successful setup:
✅ Ollama Setup Complete
Ollama Version: 0.1.x
Endpoint: http://localhost:11434
Model: nomic-embed-text (768 dimensions)
Status: Running
GrepAI is ready to use with local embeddings.
Your code will never leave your machine.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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Registry listing for grepai-ollama-setup matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: grepai-ollama-setup is focused, and the summary matches what you get after install.
grepai-ollama-setup is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in grepai-ollama-setup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added grepai-ollama-setup from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
grepai-ollama-setup fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: grepai-ollama-setup is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend grepai-ollama-setup for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in grepai-ollama-setup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for grepai-ollama-setup matched our evaluation — installs cleanly and behaves as described in the markdown.
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