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auto-tune-review

Analyze session transcripts and state files to prepare structured review context for rejected agents.

Updated Jun 21, 2026
Search Tags:#code review#agent review#context extraction#AI prompt engineering#prompt preparation
One-click install
npx skills add https://github.com/infantesromeroadrian/arca-agent --skill auto-tune-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-tune-review
Source: https://github.com/infantesromeroadrian/arca-agent/tree/main/template/skills/auto-tune-review
Command: npx skills add https://github.com/infantesromeroadrian/arca-agent --skill auto-tune-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, and includes scripts (resource) components.

What problem does it solve?

This Skill simplifies the process of reviewing and preparing agent prompts for editing, saving time and ensuring consistency.

Core Features & Use Cases

  • Automated Review: Quickly review agent prompts that have been rejected by the code-critic.
  • Context Extraction: Extracts recent rejection patterns and relevant information for the prompt editor.
  • Use Case: For an agent that is in the auto_tune_pending state and has received several rejections, use this Skill to automatically gather the necessary information and prepare a structured context for the prompt engineer.

Quick Start

Run the command /auto-tune-review <agent-name> to get the review context for an agent named 'code-critic'.

Frequently Asked Questions about auto-tune-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract context for reviewing agent prompts rejected by a code-critic?▼

To extract context for reviewing agent prompts rejected by a code-critic, you can use an automated review skill that analyzes session transcripts and state files to provide a structured handoff context for the prompt editor.

What is the best way to prepare AI prompt editing context for agents in an auto_tune_pending state?▼

The best way to prepare AI prompt editing context for auto_tune_pending agents is to automatically gather rejection patterns and relevant information from session transcripts, creating a structured handoff for the prompt engineer.

Do I need jq installed to analyze session transcripts and state files for agent reviews?▼

Yes, you need jq installed to analyze session transcripts and state files for agent reviews, because the Skill requires jq to process the underlying JSON data extracted during context preparation.

How do I run an automated agent review without accidentally mutating my session state files?▼

You can run an automated agent review without mutating state files by using a Skill specifically designed for prompt preparation that analyzes session transcripts and explicitly does not mutate the underlying state.

Does automated context extraction work for agents that have received multiple code-critic rejections?▼

Yes, automated context extraction works effectively for agents with multiple code-critic rejections by analyzing the session history to gather necessary information and extract recent rejection patterns for the prompt editor.

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