| name | cocoindex |
| description | CocoIndex content indexing and search — use when you need to search indexed project content or trigger re-indexing |
CocoIndex Skill
When to Use CocoIndex
Use CocoIndex tools and commands when you need to:
- Search indexed project content — use
cocoindex_search instead of content_search or ctx_search
- Trigger re-indexing — use
/unipi:cocoindex-update after significant code changes
- Initialize indexing — use
/unipi:cocoindex-init for new projects
Key Differences from Old FTS5 Content Store
| Aspect | Old (FTS5) | New (CocoIndex) |
|---|
| Chunking | Heading/paragraph split | AST-aware code, recursive text |
| Search | BM25 + trigram fuzzy | Semantic vector + full-text |
| Incremental | Full re-index every time | Delta-only (changed files) |
| Scale | Small corpus | Multi-GiB, parallel |
| Store | SQLite FTS5 | LanceDB |
Tools
cocoindex_search
Search indexed content. It uses semantic vector search when the LanceDB table has vectors, LanceDB full-text search when an inverted index exists, and a lexical fallback for older text-only indexes.
cocoindex_search({ query: "how authentication works", limit: 10 })
Returns results with title, content, source, rank, contentType, and matchLayer.
cocoindex_status
Check indexing status — pipeline configured, last run, doc count, CLI availability.
cocoindex_status({})
Commands
| Command | Description |
|---|
/unipi:cocoindex-update | Run cocoindex update on current project |
/unipi:cocoindex-status | Show indexing status |
/unipi:cocoindex-init | Scaffold a default pipeline |
/unipi:cocoindex-search <query> | Search indexed codebase semantically |
/unipi:cocoindex-settings | Show configuration |
Prerequisites
-
Python 3.10+ with cocoindex installed:
pip install cocoindex
pip install 'cocoindex[lancedb]'
-
Pipeline initialized — run /unipi:cocoindex-init once per project
-
Embedding config — CocoIndex reuses the memory package's embedding settings:
- API key from
~/.unipi/memory/config.json
- Same model and dimensions for cross-system search
Architecture
Project files → LocalFile source → SplitRecursively → EmbedText → LanceDB
↓
Agent query ──────────────────────────────→ vector search → ranked results
- Pipeline defined in
.unipi/cocoindex/main.py (auto-generated, customizable)
- Data stored in
.unipi/cocoindex/.lancedb/
- Search queries LanceDB directly via Node.js SDK (no CLI round-trip)
Troubleshooting
| Issue | Fix |
|---|
| "CLI not found" | pip install cocoindex |
| "Pipeline not initialized" | /unipi:cocoindex-init |
| "Search unavailable" | npm install @lancedb/lancedb |
| "No results" | Run /unipi:cocoindex-update first |
| Embedding errors | Check ~/.unipi/memory/config.json API key |
