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Slb

Listed
dicklesworthstone·vlatest·cloud

Slb generates creative slug (short, memorable) names for products, brands, or projects, boosting recall and marketing impact.

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 dicklesworthstone/agent_flywheel_clawdbot_skills_and_integrations --skill slb -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

dicklesworthstone

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 pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • skills/slb/SKILL.md:27remote script piped into a shell
      curl -fsSL https://raw.githubusercontent.com/Dicklesworthstone/slb/main/scripts/install.sh | bash

  • 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

    Only contacts known or declared hosts: raw.githubusercontent.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

    raw.githubusercontent.comsource host

  • 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.

  • Pinned to a commit: passed

    Scanned dicklesworthstone/agent_flywheel_clawdbot_skills_and_integrations at 4d332440c3.

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

Commit
4d332440c3
Manifest
skills/slb/SKILL.md
Scanned
5 files, 630 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 Slb skill implements a two-person rule for AI coding agents to prevent potentially destructive commands from being executed without peer review and approval. It acts as a “Simultaneous Launch Button” (SLB), requiring a second reviewer to explicitly approve actions like deleting files, pushing code changes, or managing Kubernetes resources. This system is designed for multi-agent workflows where automated processes could cause significant damage if not carefully controlled. Importantly, commands execute within the user's existing shell environment, inheriting configurations like AWS credentials and virtual environments.

When to use it

  • Running potentially destructive commands: Before executing rm -rf, git push --force, or similar high-risk operations with an AI coding agent.
  • Multi-agent workflows: In scenarios where multiple agents are running in parallel, minimizing the risk of a single faulty command impacting the entire system.
  • Protecting critical infrastructure: When automated actions could affect data, code repositories, or cloud resources.
  • Ensuring accountability: To create an audit trail and ensure that someone reviews potentially harmful commands before they are executed.

Key capabilities

  • Tiered Risk Assessment: Commands are categorized by risk level (Critical, Dangerous, Caution, Safe) determining the number of approvals required.
  • Peer Review Workflow: Facilitates a clear process for agents to request approval and reviewers to approve or deny commands.
  • Client-Side Execution: Commands run within the user's existing shell environment, inheriting configured credentials and settings.
  • Session Management: Allows tracking and managing active agent sessions with features like resuming after crashes and cleaning up stale sessions.

Example prompts

These are examples of how a user might interact with an AI agent equipped with Slb:

  1. "Agent, please clean the build artifacts using rm -rf ./build." (The agent would then use slb run "rm -rf ./build" --reason "Clean build artifacts").
  2. "Show me any pending commands requiring approval." (The reviewer would use slb pending).
  3. “Approve request ID <request-id> with the comment 'Looks safe'." (The reviewer would use slb approve <request-id> --session-id <reviewer-id> --comment "Looks safe").

Tips & gotchas

  • Initialization Required: The skill requires project initialization (slb init) to create necessary configuration files.
  • Client-Side Dependency: Slb runs within your shell environment; it does not execute commands on a remote server. Ensure you have the correct environment configured.
  • Reviewer Role: A second agent or human reviewer is essential for the approval process.