Vector Search
CheckedQuickly retrieve relevant documents from large datasets using semantic similarity search powered by vector embeddings.
Install on your platform
vector-search. cleanexpo/nodejs-starter-v1 is deleted or private, so it cannot be installed from.We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.
What we know about this skill
Publisher
cleanexpo
Taken from the source listing. We haven't verified who controls this account.
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 cleanexpo/nodejs-starter-v1 at 7b45925bd4.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 7b45925bd4
- Scanned
- 1 file, 432 lines
About this skill
What it does
The vector-search skill enables AI agents to perform similarity searches within a dataset represented as vectors. This allows retrieval of items most closely related to a given query, based on semantic meaning rather than keyword matching. It's useful when you need to find similar documents, products, or other data points.
When to use it
- Product Recommendations: Find products similar to one a user is viewing.
- Document Retrieval: Retrieve relevant documents from a large knowledge base based on the meaning of a query.
- Semantic Search: Allow users to search for information using natural language, rather than specific keywords.
- Content Discovery: Suggest related articles or content pieces based on what a user is currently consuming.
Key capabilities
- Vector similarity search
- Dataset indexing
- Query processing
Example prompts
- "Find documents similar to 'AI ethics and bias'."
- "What products are most like this one?" (referring to an item)
- “Search for articles related to sustainable energy.”
Tips & gotchas
The skill requires a pre-existing dataset that has been converted into vector embeddings. Ensure your data is properly vectorized before using the vector-search skill for optimal results.