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Feedback Mastery

Checked
softaworks·vlatest·ai-ml·MIT

Feedback Mastery helps refine your prompts for optimal AI responses by analyzing and suggesting improvements – boosting output quality & efficiency.

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 softaworks/agent-toolkit --skill feedback-mastery -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

softaworks

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 softaworks/agent-toolkit at 3027f20f31.

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

Commit
3027f20f31
Manifest
skills/feedback-mastery/SKILL.md
Scanned
5 files, 1,238 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 feedback-mastery skill enables AI agents to collect, analyze, and act on user feedback effectively. It helps in identifying patterns, prioritizing improvements, and generating actionable insights from qualitative or quantitative input.

When to use it

  • After launching a new feature to gather user reactions and refine the product.
  • During iterative development cycles to ensure continuous improvement based on real-world usage.
  • When managing customer support interactions to identify common pain points.
  • To evaluate the effectiveness of marketing campaigns through user sentiment analysis.

Key capabilities

  • Automated feedback classification (e.g., positive, negative, neutral).
  • Sentiment analysis for qualitative input.
  • Prioritization of feedback based on frequency and impact.
  • Integration with analytics tools to track trends over time.

Example prompts

  • "Analyze the latest user reviews and categorize them by sentiment."
  • "Summarize customer support tickets to identify common issues."
  • "Generate a report highlighting the top three areas for improvement based on feedback data."

Tips & gotchas

  • Ensure that the feedback data is clean and relevant before processing.
  • Use this skill in conjunction with other tools for deeper analysis, such as A/B testing or user behavior tracking.