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Timescaledb

Listed
2025emma·vlatest·database·MIT

Timescaledb optimizes time-series data storage and querying for high-volume analytics, improving performance and scalability.

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 2025emma/vibe-coding-cn --skill timescaledb -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

2025emma

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 failed 3 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    SKILL.md has no YAML frontmatter, so it declares nothing machine-readable.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: failed

    Contacts 3 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: api.aiven.io, console.cloud.timescale.com, packagecloud.io.

    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.

    Hosts contacted

    console.cloud.timescale.comundeclaredapi.aiven.ioundeclaredraw.githubusercontent.comsource hostpackagecloud.ioundeclared

    What it found (3 of 6)

    • i18n/en/skills/timescaledb/references/api.md:531contacts console.cloud.timescale.com
      curl -X GET "https://console.cloud.timescale.com/public/api/v1/projects/{project_id}/services" \
    • i18n/zh/skills/timescaledb/references/api.md:530contacts console.cloud.timescale.com
      curl -X GET "https://console.cloud.timescale.com/public/api/v1/projects/{project_id}/services" \
    • i18n/en/skills/timescaledb/references/api.md:2124contacts api.aiven.io
      curl -s -H "Authorization: aivenv1 $MST_API_TOKEN" https://api.aiven.io/v1/me|json_pp

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 2 places.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • i18n/en/skills/timescaledb/references/installation.md:1202remote script run via command substitution
      /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
    • i18n/zh/skills/timescaledb/references/installation.md:1201remote script run via command substitution
      /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

  • 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.

  • Pinned to a commit: passed

    Scanned 2025emma/vibe-coding-cn at 9b42dd10dd.

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

Commit
9b42dd10dd
Manifest
i18n/en/skills/timescaledb/SKILL.md
Scanned
24 files, 35,742 lines
Copies in repo
2 — all of them scanned
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

The TimescaleDB skill enables AI agents to interact with a specialized time-series database built on PostgreSQL. It allows for efficient storage, querying, and compression of data points that change over time, such as sensor readings or application metrics.

When to use it

  • Storing high-volume IoT telemetry data from thousands of devices simultaneously.
  • Analyzing historical trends in server performance logs to identify anomalies.
  • Managing financial tick data where sub-second precision and rapid aggregation are critical.
  • Archiving clickstream analytics for long-term user behavior analysis without sacrificing query speed.

Key capabilities

  • Inherits full compatibility with standard PostgreSQL syntax and tools.
  • Automatically partitions data into hypertables based on time boundaries.
  • Provides continuous aggregate functions to pre-calculate common metrics.
  • Offers compression algorithms that significantly reduce storage footprint for historical data.

Example prompts

  • "Create a hypertable in TimescaleDB to store temperature readings from 500 sensors every minute."
  • "Write a query using continuous aggregates to calculate the average CPU usage per hour over the last week."
  • "Compress data older than 30 days in the system_metrics table to optimize storage costs."

Tips & gotchas

Ensure your underlying PostgreSQL instance is properly configured for time-series workloads before initializing hypertables. While TimescaleDB handles compression well, be aware that decompressing large historical datasets may temporarily increase CPU usage during complex queries.