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Series Analyzer

Checked
vangongwanxiaowan·vlatest·other·MIT

Analyzes numerical series data to identify patterns, trends, and anomalies, enabling informed decision-making and forecasting.

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 vangong1999/screen-creative-skills --skill series-analyzer -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

vangongwanxiaowan

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 vangongwanxiaowan/screen-creative-skills at 6ced54f40c.

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

Commit
6ced54f40c
Manifest
category/series-analysis/series-analyzer/SKILL.md
Scanned
4 files, 596 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

What it does

The series-analyzer skill allows you to analyze a given TV show or movie series. It can provide summaries of episodes, identify recurring themes and characters, and offer insights into the overall narrative arc of the series. This is useful for understanding complex plots or refreshing your memory about details from previous seasons.

When to use it

  • Summarize a season of a show you haven't watched in a while.
  • Identify the main plot points and character development across multiple episodes.
  • Get an overview of a series before deciding whether to invest time watching it.
  • Understand recurring themes or motifs within a specific TV series.

Key capabilities

  • Episode Summarization
  • Theme Identification
  • Character Analysis
  • Narrative Arc Overview

Example prompts

  • "Summarize the first season of The Mandalorian."
  • "What are the main themes in Breaking Bad?"
  • "Analyze the character development of Walter White throughout Breaking Bad."

Tips & gotchas

The accuracy and detail of the analysis depend on the availability of information about the series. More popular or well-documented shows will yield better results.