shinka-run

Launch ShinkaEvolve batch runs via shinka_run CLI with validated task directories.

Updated May 20, 2026
One-click install
npx skills add https://github.com/nhatnguyen1122/shinka --skill shinka-run-nhatnguyen1122
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shinka-run
Source: https://github.com/nhatnguyen1122/shinka/tree/main/skills/shinka-run
Command: npx skills add https://github.com/nhatnguyen1122/shinka --skill shinka-run-nhatnguyen1122

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Run existing ShinkaEvolve tasks with the shinka_run CLI from a task directory (evaluate.py + initial.<ext>). Use when an agent needs to launch async evolution runs quickly with required --results_dir, generation count, and strict namespaced keyword overrides.

Core Features & Use Cases

  • Batch-run ShinkaEvolve tasks from a directory containing evaluate.py and initial.* to start controlled evolution experiments.
  • Validate task readiness: ensures evaluate.py and initial.* exist before launching.
  • CLI-driven orchestration: configure generations, results path, and overrides with explicit --set properties for reproducible runs.
  • Suitable for agents or developers needing fast, asynchronous evolution runs with clear output directories.

Quick Start

Launch a batch evolution by pointing the CLI to your task directory and desired results directory, e.g., shinka_run --task-dir <task_dir> --results_dir <results_dir> --num_generations 40.

Frequently Asked Questions about shinka-run

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

FAQPage Schema
How do I run ShinkaEvolve tasks from a directory using the command line?▼

To run ShinkaEvolve tasks, point the shinka_run CLI to your prepared task directory containing evaluate.py and initial.* files, then specify the results directory and generation count to launch asynchronous evolution experiments.

What files are required to launch an evolutionary algorithm batch run?▼

Launching an evolutionary algorithm batch run requires a task directory containing both an evaluate.py script and an initial.* file, which the system validates before orchestrating the evolution experiment.

Can I configure generation count and output directory for automated evolution runs?▼

Yes, you can configure generation count and output directory for automated evolution runs by passing explicit parameters like --num_generations and --results_dir to the task runner CLI.

How do I ensure reproducible results when running evolutionary algorithms asynchronously?▼

You ensure reproducible results by using explicit --set properties for namespaced keyword overrides, defining strict results directories, and specifying generation counts when launching batch evolution runs.

Why does my batch evolution run fail before starting?▼

Your batch evolution run fails before starting if the task directory lacks the required evaluate.py script or initial.* file, as the system validates task readiness before launching any orchestration.

What is the best way to automate multiple evolutionary algorithm experiments quickly?▼

The best way to automate multiple evolutionary algorithm experiments quickly is using a CLI task runner that validates directory readiness and orchestrates asynchronous batch runs with explicit configuration overrides.