Faiss
CheckedFaiss enables fast similarity search across large datasets by building indexes for efficient nearest neighbor retrieval – crucial for applications like recommendation and image search.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add davila7/claude-code-templates --skill faiss -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
davila7
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned davila7/claude-code-templates at 79182c51fb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 79182c51fb
- Scanned
- 2 files, 463 lines
About this skill
What it does
This skill enables AI agents to interact with FAISS (Facebook AI Similarity Search), a library designed for efficient similarity search and clustering of dense vectors. It allows agents to build indexes from vector data, perform fast nearest neighbor searches, and manage large-scale vector datasets directly within the agent's workflow.
When to use it
- RAG Optimization: Accelerate retrieval-augmented generation by indexing document embeddings for instant semantic lookup.
- Recommendation Systems: Identify similar items or users based on vector representations in real-time.
- Anomaly Detection: Quickly find vectors that deviate significantly from a learned cluster pattern.
- Vector Database Management: Handle high-dimensional data storage and querying without relying on external database connections.
Key capabilities
- Building FAISS indexes (Flat, IVF, HNSW) for varying dataset sizes.
- Executing efficient nearest neighbor searches with configurable distance metrics.
- Managing vector collections through add, remove, and update operations.
- Configuring index parameters like dimensionality and metric types (L2, Inner Product).
Example prompts
- "Build a FAISS IVF index from this list of 10,000 product embeddings using cosine distance."
- "Search for the top 5 most similar vectors to this query embedding in the current index."
- "Update the existing FAISS collection by removing vectors that match these specific IDs."
Tips & gotchas
Ensure your vector dimensionality is consistent before building an index, as mismatched dimensions will cause runtime errors. For datasets exceeding a few thousand vectors, prefer IVF or HNSW indexes over Flat to maintain search speed and memory efficiency.