shinka-inspect

Load top Shinka programs and write a Markdown bundle with metadata and code snippets.

1.3k|268|Updated Sep 17, 2025
One-click install
npx skills add https://github.com/SakanaAI/ShinkaEvolve --skill shinka-inspect
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: shinka-inspect
Source: https://github.com/SakanaAI/ShinkaEvolve/tree/main/skills/shinka-inspect
Command: npx skills add https://github.com/SakanaAI/ShinkaEvolve --skill shinka-inspect

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Efficiently bring top-performing Shinka programs from a completed run into an agent's working context to accelerate task planning.

Core Features & Use Cases

  • Extracts top-performing Shinka programs and packages them into a compact Markdown context bundle for downstream agent tasks.
  • Ranks programs by combined_score and supports optional min_generation filtering, with a fallback to top-k by score if no correct programs exist.
  • Produces a ready-to-load artifact that agents can consume to guide subsequent mutation planning and evaluation.

Quick Start

Invoke the shinka-inspect skill on a completed run directory to generate a Markdown context bundle for the next mutation planning cycle.

Frequently Asked Questions about shinka-inspect

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

FAQPage Schema
How do I load top-performing Shinka programs from a completed run into agent context?

To load top-performing Shinka programs into agent context, read program records from a completed run directory, rank them by combined_score, and select the top-k rows where correct is true to generate a Markdown context bundle.

What is the best way to package ranked program records and code snippets for downstream mutation planning?

The best way to package ranked program records for mutation planning is to extract top-k entries by combined_score and produce a Markdown bundle containing metadata, ranking tables, feedback, and code snippets for agents to consume.

How does the fallback ranking work when no correct Shinka programs exist in a completed run?

When no correct Shinka programs exist in a completed run, the fallback ranking mechanism selects the top-k programs by combined_score across all available rows, ensuring a Markdown context bundle is still produced for agent planning.

Can I filter Shinka program records by min_generation before ranking them by combined_score?

Yes, you can apply optional min_generation filtering to Shinka program records before ranking them by combined_score. This filters the pool of programs prior to selecting the top-k correct entries for the Markdown bundle.

Do I need pandas to extract top-k Shinka programs and write the resulting Markdown bundle?

Yes, you need pandas installed to read program records, apply ranking logic by combined_score, and write the resulting Markdown bundle with metadata and code snippets to the specified output path.

What metadata is included in the Markdown bundle generated from top-k Shinka programs?

The Markdown bundle generated from top-k Shinka programs includes run metadata, a ranking table of the selected programs, associated feedback, and code snippets, providing comprehensive context for downstream agent tasks.