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Evaluate Exponential Trend Deviation Regimes

Listed
fatfingererr·vlatest·data·MIT

This skill analyzes data for shifts in exponential trends and deviations, helping identify regime changes crucial for forecasting accuracy.

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 fatfingererr/macro-skills --skill evaluate-exponential-trend-deviation-regimes -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

fatfingererr

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 1 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.

  • No undeclared network calls: failed

    Contacts 3 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: finance.yahoo.com, fred.stlouisfed.org, stooq.com.

    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

    finance.yahoo.comundeclaredfred.stlouisfed.orgundeclaredstooq.comundeclaredgithub.comsource host

    What it found (3 of 4)

    • skills/evaluate-exponential-trend-deviation-regimes/manifest.json:72contacts finance.yahoo.com
      "url": "https://finance.yahoo.com",
    • skills/evaluate-exponential-trend-deviation-regimes/manifest.json:78contacts fred.stlouisfed.org
      "url": "https://fred.stlouisfed.org",
    • skills/evaluate-exponential-trend-deviation-regimes/manifest.json:84contacts stooq.com
      "url": "https://stooq.com",

  • 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 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 fatfingererr/macro-skills at 13e5388028.

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

Commit
13e5388028
Manifest
skills/evaluate-exponential-trend-deviation-regimes/SKILL.md
Scanned
17 files, 3,595 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 time series data to identify deviations from expected exponential trends. It can detect periods where the growth rate significantly differs from a predicted exponential curve, highlighting potential anomalies or regime shifts in the underlying process. The tool provides insights into when and how much these deviations occur, enabling users to understand complex patterns within data. It's particularly useful for identifying unexpected changes in growth rates over time.

When to use it

  • Financial Analysis: Identify periods where stock prices deviate from predicted exponential growth models.
  • Sales Forecasting: Detect when sales growth significantly deviates from expected trends, signaling potential market shifts or campaign impacts.
  • Population Growth Studies: Analyze population data and identify periods of accelerated or decelerated growth compared to projected exponential increases.
  • Resource Consumption Monitoring: Track resource usage (e.g., energy consumption) and flag deviations from anticipated exponential patterns.

Key capabilities

  • Exponential trend analysis
  • Deviation detection
  • Regime shift identification
  • Time series data processing

Example prompts

  • "Analyze this sales data for deviations from an exponential growth model."
  • "Identify any regime shifts in the provided population time series."
  • “Show me periods where energy consumption significantly deviates from expected trends.”

Tips & gotchas

This skill requires a properly formatted time series dataset as input. Ensure your data includes timestamps and corresponding values for accurate analysis.