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Nested Deep Skill

Listed
maxvaega·vlatest·agents·MIT

This skill generates deeply nested, complex text structures for creative writing or data modeling, boosting output variety and detail.

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 maxvaega/skillkit --skill nested-deep-skill -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

maxvaega

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.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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 maxvaega/skillkit at 2a30ae54cf.

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

Commit
2a30ae54cf
Manifest
examples/skills/nested-example/category-a/subcategory-1/SKILL.md
Scanned
0 files
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 AI agents to process and respond to complex, multi-layered instructions. It can handle requests that involve multiple steps or conditions, breaking them down into manageable actions. The agent can also maintain context across these nested operations, ensuring accurate and consistent results.

When to use it

  • Complex Task Automation: Automating a workflow with several dependencies, such as preparing a report that requires data extraction, cleaning, and formatting.
  • Conditional Logic Execution: Implementing "if-then-else" scenarios within an agent's response, adapting its actions based on specific conditions.
  • Hierarchical Data Processing: Handling datasets organized in nested structures, like extracting information from a JSON file with multiple levels of embedded data.
  • Multi-Step Reasoning: Guiding the AI through a chain of reasoning steps to arrive at a conclusion or solution.

Key capabilities

  • Nested instruction processing
  • Context maintenance across operations
  • Conditional execution
  • Hierarchical data handling

Example prompts

  • "First, summarize this article. Then, extract all named entities and categorize them by type."
  • "If the current temperature is above 80 degrees, send a reminder to water the plants; otherwise, check the soil moisture level."
  • "Parse this JSON file and return the value of 'item_3' under the key 'details'."

Tips & gotchas

The skill’s effectiveness depends on clear and well-structured prompts. Ambiguous or overly complex requests may lead to unexpected results; break down tasks into smaller, more manageable steps for optimal performance.