Skip to content
TrustedSkillsbeta
Back to all skills

Verification Loop

Checked
peopleforrester·vlatest·other·MIT

This skill automates data validation by repeatedly checking outputs against source information, ensuring accuracy and reliability of results.

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 peopleforrester/claude-dotfiles --skill verification-loop -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

peopleforrester

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 peopleforrester/claude-dotfiles at db7caa0374.

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

Commit
db7caa0374
Manifest
skills/development/verification-loop/SKILL.md
Scanned
1 file, 173 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 Verification Loop skill facilitates a structured feedback and refinement process for AI agent outputs. It allows users to provide specific, actionable feedback on generated responses, which are then incorporated into subsequent iterations of the response. This loop aims to improve accuracy, relevance, and overall quality over time by continuously learning from user input.

When to use it

  • Complex Tasks: When an AI agent is generating detailed reports or analyses where nuanced understanding and adjustments are crucial.
  • Creative Writing: To refine drafts of stories, articles, or other creative content through iterative feedback.
  • Code Generation: To improve the accuracy and functionality of generated code snippets by correcting errors and suggesting improvements.
  • Data Extraction: When extracting data from unstructured sources requires multiple passes to ensure completeness and correctness.

Key capabilities

  • Iterative response refinement based on user feedback
  • Actionable feedback integration
  • Continuous learning loop for improved accuracy
  • Structured feedback process

Example prompts

  • "Run the verification loop on this draft email, focusing on tone and clarity."
  • "Please generate a Python function to sort a list. Then, run the verification loop so I can refine it."
  • “Analyze this customer review and provide a summary. Initiate the verification loop for accuracy.”

Tips & gotchas

The effectiveness of this skill depends on providing clear and specific feedback during each iteration of the verification loop. Vague or ambiguous feedback may not lead to meaningful improvements in subsequent responses.