cursor-codebase-indexing
Configures and manages Cursor's semantic indexing for enhanced codebase-wide AI context.
Install
mkdir -p .claude/skills/cursor-codebase-indexing && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1795" && unzip -o skill.zip -d .claude/skills/cursor-codebase-indexing && rm skill.zipInstalls to .claude/skills/cursor-codebase-indexing
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Set up and optimize Cursor codebase indexing for semantic code searchKey capabilities
- →Set up Cursor codebase indexing for semantic search
- →Exclude files from indexing using `.cursorignore` and `.cursorindexingignore`
- →Verify indexing status and progress
- →Perform semantic queries using `@Codebase`
- →Resync the index or clear local cache for troubleshooting
- →Optimize indexing for large projects and monorepos
How it works
The system splits code files into chunks, generates vector embeddings, and stores them for nearest-neighbor search. A Merkle tree detects changes to re-index only modified files.
Inputs & outputs
When to use cursor-codebase-indexing
- →Initialize Cursor codebase indexing
- →Check indexing status
- →Troubleshoot @Codebase search relevance
- →Optimize indexing for large projects
About this skill
Cursor Codebase Indexing
Set up and optimize Cursor's codebase indexing system. Indexing creates embeddings of your code, enabling @Codebase semantic search and improving AI context awareness across Chat, Composer, and Agent mode.
How Indexing Works
Your Code Files
│
▼
Syntax Chunking ─── splits files into meaningful code blocks
│
▼
Embedding Generation ─── converts chunks to vector representations
│
▼
Vector Storage (Turbopuffer) ─── cloud-hosted nearest-neighbor search
│
▼
@Codebase Query ─── your question → embedding → similarity search → relevant chunks
Key Architecture Details
- Merkle tree for change detection: only modified files are re-indexed (every 10 minutes)
- No plaintext storage: code is not stored server-side; only embeddings and obfuscated metadata
- Privacy Mode compatible: with Privacy Mode on, embeddings are computed without retaining source code
- Indexing runs in the background; small projects complete in seconds, large projects (50K+ files) may take hours initially
Initial Setup
- Open your project in Cursor
- Indexing starts automatically on first open
- Check status: look at the bottom status bar for "Indexing..." indicator
- View indexed files:
Cursor Settings>Features>Codebase Indexing>View included files
Verify Indexing Status
The status bar shows:
- "Indexing..." with progress indicator -- initial indexing in progress
- "Indexed" -- indexing complete,
@Codebasequeries are available - No indicator -- indexing may be disabled or not started
Configuration
.cursorignore
Exclude files from indexing and AI features. Place in project root. Uses .gitignore syntax:
# .cursorignore
# Build artifacts (large, not useful for AI context)
dist/
build/
out/
.next/
target/
# Dependencies
node_modules/
vendor/
venv/
.venv/
# Generated files
*.min.js
*.min.css
*.bundle.js
*.map
*.lock
# Large data files
*.csv
*.sql
*.sqlite
*.parquet
fixtures/
seed-data/
# Secrets (defense in depth -- also use .gitignore)
.env*
**/secrets/
**/credentials/
.cursorindexingignore
Exclude files from indexing only but keep them accessible to AI features when explicitly referenced:
# .cursorindexingignore
# Large test fixtures -- don't index, but allow @Files reference
tests/fixtures/
e2e/recordings/
# Documentation build output
docs/.vitepress/dist/
Difference: .cursorignore hides files from both indexing and AI features. .cursorindexingignore only excludes from the index; files can still be referenced via @Files.
Default Exclusions
Cursor automatically excludes everything in .gitignore. You only need .cursorignore for files tracked by git that you want to exclude from AI.
Using the Index
@Codebase Queries
Ask semantic questions about your entire codebase:
@Codebase where is user authentication handled?
@Codebase show me all API endpoints that accept file uploads
@Codebase how does the payment processing flow work?
@Codebase find all places where we connect to Redis
@Codebase performs a nearest-neighbor search using your question's embedding. It returns the most semantically similar code chunks, even if they do not contain the exact keywords you used.
@Codebase vs @Files vs Text Search
| Method | When to Use | Context Cost |
|---|---|---|
@Codebase | Discovery -- you don't know which files | High (many chunks) |
@Files | You know exactly which file | Low (one file) |
@Folders | You know the directory | Medium-High |
Ctrl+Shift+F | Exact text/regex match | N/A (editor search) |
Use @Codebase for discovery, then switch to @Files once you know where the code lives.
Optimization for Large Projects
Monorepo Strategy
For monorepos with many packages, open the specific package directory instead of the root:
# Instead of opening the entire monorepo:
cursor /path/to/monorepo # Indexes everything -- slow
# Open the specific package:
cursor /path/to/monorepo/packages/api # Indexes only this package -- fast
Or use .cursorignore at the root to exclude packages you are not actively working on:
# .cursorignore -- monorepo, focus on api and shared
packages/web/
packages/mobile/
packages/admin/
# packages/api/ ← not listed, so it IS indexed
# packages/shared/ ← not listed, so it IS indexed
Re-Indexing
If search results are stale or indexing appears stuck:
Cmd+Shift+P>Cursor: Resync Index- Wait for status bar to show indexing progress
- If that fails, delete the local cache:
- macOS:
~/Library/Application Support/Cursor/Cache/ - Linux:
~/.config/Cursor/Cache/ - Windows:
%APPDATA%\Cursor\Cache\
- macOS:
- Restart Cursor and allow full re-index
File Watcher Limits (Linux)
On Linux, large projects may hit the file watcher limit:
# Check current limit
cat /proc/sys/fs/inotify/max_user_watches
# Increase (temporary)
sudo sysctl fs.inotify.max_user_watches=524288
# Increase (permanent)
echo "fs.inotify.max_user_watches=524288" | sudo tee -a /etc/sysctl.conf
sudo sysctl -p
Enterprise Considerations
- Data residency: Embeddings are stored in Turbopuffer (cloud). Obfuscated filenames and no plaintext code, but metadata exists
- Privacy Mode: With Privacy Mode on, embeddings are computed with zero data retention at the provider
- Air-gapped environments: Indexing requires network access to Cursor's embedding API. Not available offline
- Indexing scope: Only files in the currently open workspace are indexed. Closing a project removes its index from active queries
Troubleshooting
| Symptom | Cause | Fix |
|---|---|---|
| @Codebase returns no results | Index not built | Wait for "Indexed" in status bar |
| Search misses known files | File in .gitignore or .cursorignore | Check ignore files |
| Indexing stuck at N% | Large project or network issue | Resync index via Command Palette |
| Stale results after refactor | Index not yet updated | Wait 10 min or manual resync |
| High CPU during indexing | Initial embedding computation | Normal for first run; subsides |
Resources
When not to use it
- →When network access to Cursor's embedding API is unavailable
- →When working in air-gapped environments
- →When only exact text or regex matching is needed
Limitations
- →Indexing requires network access to Cursor's embedding API
- →Only files in the currently open workspace are indexed
- →Large projects on Linux may hit file watcher limits
How it compares
This skill enables semantic search across an entire codebase using embeddings, allowing queries based on meaning rather than exact keywords, unlike traditional text search.
Compared to similar skills
cursor-codebase-indexing side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| cursor-codebase-indexing (this skill) | 8 | 27d | Review | Beginner |
| godot | 1,044 | 5mo | Review | Intermediate |
| software-architecture | 333 | 6mo | No flags | Intermediate |
| drizzle | 238 | 2mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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