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hint-mode

Extract parameter range constraints from task descriptions into SPACE_CONFIG for tuning.

6|1|Updated Apr 19, 2026
Search Tags:#optimization#task-description#hyperparameter#hint#parameter-space#space_config
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill hint-mode
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hint-mode
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/designer/hint-mode
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill hint-mode

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Hint mode automatically derives parameter range constraints from natural task descriptions and converts them into a reusable parameter space configuration (SPACE_CONFIG) for automated tuning.

Core Features & Use Cases

  • Extracts hints from standard and legacy formats to build SPACE_CONFIG entries.
  • Generates META_INFO and input constructors to integrate with optimization loops.
  • Suitable for hyperparameter tuning, model selection, and adaptive experimentation.

Quick Start

Provide a task description and let the Hint mode infer a parameter space configuration for optimization.

Frequently Asked Questions about hint-mode

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

FAQPage Schema
How do I generate a parameter space configuration from a task description?▼

To generate a parameter space configuration from a task description, this Skill extracts parameter range constraints from your text and converts them into a reusable SPACE_CONFIG dictionary for automated tuning.

What is a SPACE_CONFIG dictionary used for in hyperparameter tuning?▼

A SPACE_CONFIG dictionary defines the inferred parameter ranges for hyperparameter tuning, allowing automated optimization loops to systematically explore the parameter space derived from natural language task descriptions.

Can I extract parameter constraints from legacy or non-standard hint formats?▼

Yes, you can extract parameter constraints from legacy hint formats, as the Skill supports multiple hint formats including standard and compatibility forms to build SPACE_CONFIG entries.

Does this approach provide integration templates for automated optimization loops?▼

Yes, the approach provides META_INFO and input construction templates alongside the SPACE_CONFIG dictionary to directly integrate with automated optimization loops for model selection and adaptive experimentation.

What's the best way to infer hyperparameter ranges for AI tasks without manual setup?▼

The best way to infer hyperparameter ranges without manual setup is to provide a natural task description, letting the system automatically derive the parameter space constraints and output the configuration.

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