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Historical Pattern Analysis

Checked
lgbarn·vlatest·data·MIT

Identifies recurring trends and anomalies in historical data to predict future outcomes and inform strategic decisions.

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 lgbarn/devops-skills --skill historical-pattern-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

lgbarn

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 lgbarn/devops-skills at 8a37cc3fe5.

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

Commit
8a37cc3fe5
Manifest
skills/historical-pattern-analysis/SKILL.md
Scanned
1 file, 215 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 analyzes historical data to identify recurring patterns and trends. It can process time series data, textual records, or other structured information to reveal hidden relationships and predict future outcomes based on past behavior. The analysis focuses on identifying cyclical events, anomalies, and correlations within the provided dataset.

When to use it

  • Predicting Sales Trends: Analyze historical sales figures to forecast demand for a product in upcoming seasons.
  • Identifying Infrastructure Bottlenecks: Examine server logs over time to pinpoint recurring performance issues or resource constraints.
  • Detecting Fraudulent Activity: Identify unusual patterns in financial transactions that may indicate fraudulent behavior.
  • Analyzing Customer Behavior: Review customer purchase history and website activity to understand evolving preferences and predict future actions.

Key capabilities

  • Time series analysis
  • Pattern recognition
  • Anomaly detection
  • Correlation identification
  • Trend forecasting

Example prompts

  • "Analyze the last five years of website traffic data and identify any seasonal trends."
  • "Find recurring patterns in server error logs from the past month."
  • "Can you detect anomalies in this dataset of daily stock prices?"

Tips & gotchas

The quality of the analysis depends heavily on the completeness and accuracy of the historical data provided. Ensure the input data is properly formatted and covers a sufficient time range for meaningful pattern identification.