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Self Repair

Checked
simhacker·vlatest·other·MIT

This skill automatically fixes minor system errors and glitches, ensuring smoother operation and preventing unexpected downtime for your character.

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 simhacker/moollm --skill self-repair -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

simhacker

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 simhacker/moollm at 8f6d840b2f.

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

Commit
8f6d840b2f
Manifest
skills/self-repair/SKILL.md
Scanned
5 files, 371 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 identify and correct errors in its own reasoning or output. It can detect inconsistencies, logical fallacies, and factual inaccuracies within a generated response. The agent then attempts to revise the original text to improve accuracy and coherence based on internal analysis.

When to use it

  • When generating complex reports where accuracy is paramount.
  • During brainstorming sessions to refine ideas and eliminate flawed logic.
  • To debug code or other technical documents produced by the AI.
  • In creative writing scenarios to identify plot holes or inconsistencies in narratives.

Key capabilities

  • Error detection within generated text
  • Logical fallacy identification
  • Factual inaccuracy correction
  • Text revision and refinement

Example prompts

  • "Review this paragraph for logical errors and correct them: [paragraph]"
  • "Can you find any inconsistencies in my reasoning here? [reasoning]"
  • "Improve the accuracy of this report: [report]"

Tips & gotchas

The skill's effectiveness depends on the clarity and structure of the initial text. It’s most useful for correcting errors within a single, cohesive response rather than disparate pieces of information.