Skillget

genpark-iterative-delta-debugging-minimizer-skill

mcp
Guvenlik Denetimi
Uyari
Health Uyari
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 8 GitHub stars
Code Gecti
  • Code scan — Scanned 4 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested

Bu listing icin henuz AI raporu yok.

SUMMARY

GenPark AI Agent Skill - Zeller's Delta Debugging (ddmin) algorithm minimizing failing code snippets, complex payloads, and config files to minimal reproducible examples (MRE).

README.md

GenPark AI Agent Skill - Iterative Delta Debugging (ddmin) Minimizer

A pure Python standard library skill implementing Andreas Zeller's classical Delta Debugging algorithm (ddmin). Minimizes failing inputs, verbose code files, or corrupted JSON payloads into 1-minimal reproducible examples (MREs) with logarithmic step complexity.

Architecture

graph TD
    A[Large Failing Input: N Elements] --> B[Divide into n Chunks]
    B --> C{Test Subsets: Fails?}
    C -->|Yes| D[Shrink Search Window to Subset]
    C -->|No| E{Test Complements: Fails?}
    E -->|Yes| F[Shrink Window to Complement]
    E -->|No| G[Increase Granularity: n = 2n]
    D --> B
    F --> B
    G --> B
    B -->|Convergence| H[1-Minimal Reproducible Example]

Features

  • Guaranteed 1-Minimal Solution: Cannot remove any remaining element without resolving the bug.
  • Logarithmic Complexity: Exponentially faster than brute-force ablation.
  • Zero Pip Dependencies: Standard Library Only.

Citations & Ecosystem

Yorumlar (0)

Sonuc bulunamadi