What is the minimum amount of data required for this skill?

For statistically significant results, it is recommended to have at least 100 historical instances, such as error logs, test failures, or performance issues.

When should I choose retrospective over prospective validation?

Use retrospective validation when you have rich historical data and need fast validation (hours vs weeks). Use prospective for UX changes or emergent human behaviors.

How is the confidence score calculated?

The skill uses a model multiplying Data Quality, Accuracy (True Positive Rate), and Logical Correctness to provide a rigor score between 0 and 1.

Does this skill require write access to my repository?

No, it primarily uses read-only tools like Read, Grep, and Glob to analyze existing data patterns without modifying your source code.

Retrospective Validation

Name: Retrospective Validation
Author: yaleh

byyaleh

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分析与监控

Validates methodology effectiveness using historical session data to predict performance without the risks of live deployment.

关于

Retrospective Validation empowers developers to prove the value of new coding strategies, error-prevention tools, or performance optimizations by analyzing historical logs and session data. Instead of waiting weeks for prospective results from a live rollout, this skill applies pattern matching and statistical rigor to existing data to quantify how many past issues would have been prevented. It provides high-confidence assessments in hours rather than weeks, making it ideal for teams needing to justify workflow changes or tool integrations with hard data.

主要功能

Automated pattern matching for error prevention analysis
Quantitative impact reporting including prevention rates and speedup
15 GitHub stars
Statistical confidence modeling using the D×A×L formula
Historical data analysis for methodology proof-of-concept
Comparison metrics between retrospective and prospective validation

使用场景

Proving the ROI of a new validation tool before committing to CI/CD integration
Analyzing past CI/CD failures to optimize test suites and reduce flake
Pre-validating error-handling logic against thousands of historical logs to minimize deployment risk

关于

主要功能

Automated pattern matching for error prevention analysis
Quantitative impact reporting including prevention rates and speedup
15 GitHub stars
Statistical confidence modeling using the D×A×L formula
Historical data analysis for methodology proof-of-concept
Comparison metrics between retrospective and prospective validation

使用场景

Proving the ROI of a new validation tool before committing to CI/CD integration
Analyzing past CI/CD failures to optimize test suites and reduce flake
Pre-validating error-handling logic against thousands of historical logs to minimize deployment risk