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Postgres Hybrid Text Search

Checked
timescale·vlatest·database·Apache-2.0

Helps with PostgreSQL, search as part of working with databases and data persistence workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add timescale/pg-aiguide --skill postgres-hybrid-text-search -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

timescale

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.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

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 timescale/pg-aiguide at b236d3583f.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
b236d3583f
Manifest
skills/postgres-hybrid-text-search/SKILL.md
Scanned
1 file, 272 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables hybrid text search in PostgreSQL, combining the strengths of both full-text search and vector embeddings. It allows for more relevant results than traditional full-text search alone, especially when dealing with nuanced or semantic queries. The skill leverages existing PostgreSQL infrastructure to provide this enhanced search capability without significant architectural changes.

When to use it

  • Semantic Search: When you need to find documents based on meaning rather than just keyword matches (e.g., "find articles about climate change solutions").
  • Improved Relevance Ranking: To refine the order of results beyond simple keyword frequency, incorporating semantic similarity.
  • Combining Keyword and Semantic Search: When both exact keywords and conceptual understanding are important for search accuracy.
  • Existing PostgreSQL Infrastructure: When you want to add advanced text search capabilities without migrating to a new database system.

Key capabilities

  • Hybrid full-text search and vector embedding integration
  • Leverages existing PostgreSQL infrastructure
  • Enhanced relevance ranking based on semantic similarity
  • Supports nuanced and conceptual queries

Example prompts

  • "Find documents similar in meaning to 'sustainable energy initiatives'."
  • "Search for articles containing the keywords 'renewable' AND conceptually related to 'environmental impact'."
  • "Rank search results by both keyword match score and semantic similarity to the query 'reducing carbon footprint'."

Tips & gotchas

  • Requires a PostgreSQL database instance.
  • The quality of vector embeddings significantly impacts search relevance; ensure appropriate embedding models are used.