What happens if a failure is caused by a language limitation?

If testing reveals a missing feature in AILANG itself, the skill helps you create a structured design document to plan the necessary language or stdlib updates.

Which models should I use for gap analysis?

The skill is optimized for use with fast dev models like gemini-3-flash and claude-haiku-4-5 to allow for rapid, cost-effective iteration before testing on larger models.

How does this skill improve AI model prompts?

It identifies specific error patterns, such as the model accidentally using Python syntax, and helps developers add targeted examples to the prompt to prevent these errors.

What is an eval gap in the context of AILANG?

An eval gap is the difference in success rates between a baseline language like Python and AILANG when an AI model attempts to solve the same benchmark problems.

Eval Gap Finder

Name: Eval Gap Finder
Author: sunholo-data

bysunholo-data

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数据科学与机器学习

Automates the identification and resolution of performance discrepancies between Python and AILANG benchmark success rates.

The Eval Gap Finder is a specialized workflow tool designed for developers working with the AILANG substrate to bridge the performance gap between standard Python execution and AI-native reasoning. It automates the process of running comparative evaluations, categorizing failure modes—such as syntax errors, type unification failures, or logic gaps—and iteratively improving model prompts or language specifications. By providing structured analysis and automated testing of examples, it ensures that AI models achieve high success rates in domain-specific languages by identifying exactly where documentation or language features are lacking.

主要功能

01Prompt coverage analysis to identify missing documentation patterns

02Automated testing suite for verifying AILANG code examples

03Automated Python vs. AILANG benchmark success rate comparison

0417 GitHub stars

05Detailed failure categorization including syntax, type, and logic errors

06Design document generation for tracking and fixing language limitations

使用场景

01Analyzing why AI models fail on specific AILANG benchmarks compared to Python

02Systematically improving LLM prompt performance for domain-specific languages

03Identifying missing standard library functions or language features in AILANG

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sunholo-data/ailang eval-gap-finder

For use in Claude.ai and ChatGPT

Download Skill