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Merge Stack

Checked
rarestg·vlatest·backend

This skill merges multiple stacks into a single, optimized stack for efficient resource management and streamlined workflows.

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 rarestg/rarestg-skills --skill merge-stack -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

rarestg

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 rarestg/rarestg-skills at 2e301b9473.

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

Commit
2e301b9473
Manifest
skills/merge-stack/SKILL.md
Scanned
1 file, 106 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 allows an AI agent to combine multiple data stacks into a single, unified view. It can process and integrate information from diverse sources, presenting them in a consolidated format. The primary function is to synthesize disparate datasets for improved analysis and decision-making.

When to use it

  • Analyzing customer feedback across various channels (social media, surveys, support tickets) to identify trends.
  • Combining sales data with marketing campaign results to assess ROI.
  • Merging research findings from different publications into a comprehensive literature review.
  • Integrating financial reports from multiple subsidiaries for consolidated reporting.

Key capabilities

  • Data stack aggregation
  • Unified view creation
  • Cross-source information synthesis
  • Dataset integration

Example prompts

  • "Combine the data from these three CSV files: sales_q1.csv, marketing_spend.csv, and customer_reviews.csv."
  • "Create a unified report merging website analytics with social media engagement metrics."
  • “Synthesize the findings from these research papers on climate change.”

Tips & gotchas

The skill's effectiveness depends heavily on the consistency and compatibility of the input data stacks. Ensure that datasets have clear, consistent schemas for optimal results.