shinka-run

Automate batch program mutation runs with the ShinkaEvolve CLI.

1|1|Updated Mar 5, 2026
One-click install
npx skills add https://github.com/tan-yong-sheng/GrowChat --skill shinka-run-tan-yong-sheng
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: shinka-run
Source: https://github.com/tan-yong-sheng/GrowChat/tree/main/.claude/skills/shinka-run
Command: npx skills add https://github.com/tan-yong-sheng/GrowChat --skill shinka-run-tan-yong-sheng

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Shinka Run CLI Skill automates batch evolution runs using the ShinkaEvolve CLI, enabling rapid exploration of program mutations from a task directory.

Core Features & Use Cases

  • Orchestrates validation of a task directory, ensuring evaluate.py and initial.* exist and the required run configuration is present.
  • Guides users through a confirm-before-run workflow, including budget scope, generation count, and knob selections.
  • Launches the main run with shinka_run using explicit --task-dir, --results_dir, --num_generations, and mutation/embedding model overrides, then verifies outputs.

Quick Start

Run a batch evolution by specifying --task-dir, --results-dir, and --num-generations with any needed overrides to execute the first run.

Frequently Asked Questions about shinka-run

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

FAQPage Schema
How do I automate batch runs of program mutations using the ShinkaEvolve CLI?

Automate batch runs of program mutations using the ShinkaEvolve CLI by specifying the task directory, results directory, and generation count. The process validates your run configuration, launches the main run, and verifies outputs with built-in guardrails.

What files do I need in my task directory to launch a ShinkaEvolve batch run?

To launch a ShinkaEvolve batch run, your task directory must contain an evaluate.py file, an initial.* file, and the required run configuration. The process validates these inputs before proceeding with the mutation experiments.

Can I override mutation and embedding models during an automated evolution run?

Yes, you can override mutation and embedding models during an automated evolution run by passing explicit flags. The launch process accepts model overrides alongside the required task directory, results directory, and generation count arguments.

What is the best way to ensure reproducible program mutation experiments?

The best way to ensure reproducible program mutation experiments is to use explicit task directories and results directories. The automation validates the run configuration and applies built-in guardrails to check outputs for consistent evolution exploration.

Why does my batch evolution run stop before processing any generations?

Your batch evolution run stops before processing generations because the validation phase failed. The process checks for the presence of evaluate.py, initial.* files, and the run configuration, halting the launch if any required input is missing.