Can this skill help prevent regressions in my codebase?

Yes, by performing mandatory measurable checks and flagging regressions immediately, it ensures that new 'learnings' do not negatively impact existing performance.

How does the skill decide whether to adopt a new pattern?

Decisions are based on a comparison of baseline versus trial metrics; patterns are only adopted if they show a positive delta in metrics like pass rates or cycle time.

What happens if a specific tool is unavailable in my environment?

The skill is designed with OpenClaw constraints in mind, utilizing documented fallback scripts or marking checks as 'SKIP' to prevent hard failures.

What is the primary purpose of the learn-eval skill?

The learn-eval skill automates the process of testing newly identified patterns against existing baselines to ensure they provide measurable improvements before being adopted.

Learning Evaluation & Benchmarking

Name: Learning Evaluation & Benchmarking
Author: oabdelmaksoud

byoabdelmaksoud

0•

Analytics & Monitoring

Evaluates and benchmarks newly extracted patterns and learnings against established baselines to ensure measurable quality improvements.

The Learning Evaluation & Benchmarking skill provides a rigorous framework for validating AI-generated patterns and process optimizations derived from completed tasks. By running controlled trials and comparing results against baseline metrics—such as pass rates, defect counts, and cycle times—it ensures that only verified, evidence-based improvements are integrated into your workflow. This systematic approach prevents regressions and provides a clear 'adopt, revise, or reject' decision path for every proposed workflow change.

Key Features

01Automated pattern validation through controlled trial execution

02Evidence-based decision framework (Adopt/Revise/Reject)

03Delta analysis for quality scoring and regression detection

04OpenClaw-native execution with automated fallback script support

050 GitHub stars

06Measurable benchmarking against pass rates and cycle time metrics

Use Cases

01Validating new coding patterns extracted from complex refactoring tasks

02Performing regression checks on process adjustments to ensure output stability

03Benchmarking the efficiency of suggested automation scripts against manual baselines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add oabdelmaksoud/superclaw ecc-cmd-learn-eval

For use in Claude.ai and ChatGPT

Key Features

01Automated pattern validation through controlled trial execution

02Evidence-based decision framework (Adopt/Revise/Reject)

03Delta analysis for quality scoring and regression detection

04OpenClaw-native execution with automated fallback script support

050 GitHub stars

06Measurable benchmarking against pass rates and cycle time metrics

Use Cases

01Validating new coding patterns extracted from complex refactoring tasks

02Performing regression checks on process adjustments to ensure output stability

03Benchmarking the efficiency of suggested automation scripts against manual baselines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add oabdelmaksoud/superclaw ecc-cmd-learn-eval

For use in Claude.ai and ChatGPT