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Time Series Analysis

Checked
aj-geddes·vlatest·data·MIT

Analyze historical data trends to forecast future values and identify anomalies in time series datasets.

Repository

Install on your platform

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

Installs under a different name. Its SKILL.md names it Time Series Analysis, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Time Series Analysis" -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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/time-series-analysis/SKILL.md
Scanned
3 files, 310 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 AI agents to perform statistical analysis on data points collected over time intervals. It helps identify trends, seasonal patterns, and anomalies within datasets that evolve chronologically.

When to use it

  • Analyzing daily server traffic logs to predict capacity needs for upcoming weeks.
  • Evaluating monthly sales figures to forecast revenue for the next quarter.
  • Detecting irregular spikes in sensor readings from industrial machinery.
  • Reviewing historical stock prices to identify long-term market trends.

Key capabilities

  • Parsing and organizing sequential data points into structured time-series formats.
  • Calculating moving averages to smooth out short-term fluctuations.
  • Identifying seasonal patterns and cyclical behaviors within the data.
  • Generating statistical summaries such as mean, variance, and standard deviation over time windows.

Example prompts

  • "Analyze this CSV of hourly temperature readings and highlight any days where the average exceeded 30°C."
  • "Calculate a 7-day moving average for the provided sales dataset to visualize recent growth trends."
  • "Identify potential anomalies in this electricity usage log that deviate significantly from the historical mean."

Tips & gotchas

Ensure your input data is strictly ordered chronologically, as misaligned timestamps can invalidate trend analysis. This skill works best with clean, continuous data; missing intervals may require preprocessing before accurate pattern recognition occurs.