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Got Controller

Checked
liangdabiao·vlatest·backend

Got Controller allows you to control external applications like games or media players directly from this chat, streamlining workflows and enhancing interaction.

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 liangdabiao/claude-code-stock-deep-research-agent --skill got-controller -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

liangdabiao

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 liangdabiao/claude-code-stock-deep-research-agent at 3299e26cff.

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

Commit
3299e26cff
Manifest
1.claude/skills/got-controller/SKILL.md
Scanned
3 files, 1,165 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

The Got Controller skill enables AI agents to manage and orchestrate complex research using a Graph of Thoughts (GoT) framework. It allows for strategic generation, aggregation, refinement, and scoring of research findings modeled as nodes in a graph, optimizing information quality through parallel exploration and iterative improvement. This facilitates managing multi-agent research workflows by controlling operations on the GoT structure.

When to use it

  • Initial exploration of a topic, creating multiple research paths simultaneously.
  • Combining related findings from different agents into a cohesive synthesis.
  • Improving existing research findings through clarification and better organization.
  • Managing graph complexity and focusing resources on high-quality research paths.

Key capabilities

  • Generate(k): Creates k new research paths from a parent node, spawning parallel research agents.
  • Aggregate(k): Combines k nodes into one stronger synthesis, resolving conflicts and extracting key insights.
  • Refine(1): Improves an existing finding by enhancing clarity, completeness, citations, and structure.
  • Score: Evaluates the quality of a research finding on a scale of 0-10 based on criteria like source quality and comprehensiveness.
  • KeepBestN(n): Prunes low-quality nodes, retaining only the top n at each level to manage graph complexity.

Example prompts

  • "Generate 4 research paths from this initial finding."
  • "Aggregate these three nodes into a single synthesis."
  • "Refine this node to improve its clarity and citations."
  • "Score this research finding based on the provided sources."

Tips & gotchas

  • The skill operates within the Graph of Thoughts (GoT) framework, which models reasoning as a graph. Understanding GoT concepts like nodes, edges, scores, and frontier is helpful for effective use.
  • Scoring criteria are important; findings with multiple high-quality sources and no contradictions receive higher scores.