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Skill Tuning

Checked
catlog22·vlatest·agents·MIT

Skill Tuning optimizes AI responses by adjusting parameters for better accuracy, relevance, and style – improving output quality quickly.

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 catlog22/claude-code-workflow --skill skill-tuning -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

catlog22

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 catlog22/claude-code-workflow at 07491b04ac.

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

Commit
07491b04ac
Manifest
.claude/skills/skill-tuning/SKILL.md
Scanned
26 files, 6,278 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

Skill Tuning is designed to automatically diagnose and optimize AI agent execution, addressing common problems that impact performance and output quality. It identifies issues like excessive token usage, fragmented state management, or coordination failures between different parts of an agent's workflow. The skill then applies targeted fixes based on pre-defined strategies, aiming for improved accuracy, relevance, and overall efficiency – all without direct user intervention beyond the initial request.

When to use it

  • When you notice your AI agents are exhibiting inconsistent behavior or producing unexpected results.
  • If an agent's performance is slow due to excessive token usage or inefficient data handling.
  • To resolve issues related to fragmented state, unreliable chains of actions, or problems with error handling within the agent’s workflow.
  • When you suspect underlying architectural or configuration problems are hindering your AI agent's effectiveness.

Key capabilities

  • Autonomous Diagnosis: Identifies and categorizes execution issues based on pre-defined patterns and severity levels (P0 - P5).
  • Problem Taxonomy & Tuning Strategies: Leverages detailed specifications (problem-taxonomy.md and tuning-strategies.md) to guide diagnosis and remediation.
  • Gemini CLI Integration: Utilizes the Gemini CLI for deep analysis of complex issues.
  • State-Driven Orchestration: Manages a workflow with distinct phases: initialization, requirement analysis, diagnosis (multiple types), report generation, fix proposal, application, and completion.
  • Fix & Verification: Applies proposed fixes and automatically retests to ensure effectiveness.

Example prompts

  • "Skill Tuning: Analyze my agent's performance."
  • "Skill Tuning: Diagnose issues with token consumption in my skill."
  • "Skill Tuning: I’m seeing inconsistent results; can you identify the problem?"

Tips & gotchas

  • The skill requires access to and understanding of problem-taxonomy.md and tuning-strategies.md for effective operation.
  • While automated, Skill Tuning focuses on specific categories of problems outlined in its specifications. It may not address all possible issues.
  • The "Fix & Verify" process automatically applies changes; review the final report (state.final_report) to understand what adjustments were made.